a cost-utility analysis of diabetic foot ulcer treatment
TRANSCRIPT
A Cost-utility analysis of Diabetic Foot Ulcer
treatment in Norway: A Markov model
Thesis submitted as a part of the Master of Philosophy Degree in
Health Economics, Policy and Management
University Of Oslo Faculty of Medicine
Department of Health Management and Health Economics
May 2018
Author: Supervisor: Charlotte Indre Lund Hans Olav Melberg
II
© Charlotte Indre Lund
2018
Cost-utility analysis of Diabetic Foot Ulcer treatment in Norway: Markov model
Charlotte Indre Lund
http://www.duo.uio.no/
Trykk: Reprosentralen, Universitetet i Oslo
III
Acknowledgement
I would like to express my gratitude to my supervisor Hans Olav Melberg for the valuable
remarks and suggestions through the learning process of this master thesis.
Special thanks to people who indirectly have influenced the creative process of model
conceptualization and writing including the staff at the Health Economics Department of
University of Oslo, my fellow master students for exchanging ideas and providing continuous
encouragement. Specifically, I am grateful for insightful comments to Tonje Marie Lukkassen
and Tove Olaussen Freeman. Thank you for cheering for me.
Finally, I express my gratitude to Iacob Mathiesen and Andreas Mollatt for inspiring me to
purse research in the field of chronic wounds.
IV
List of figures
Figure 1. Markov state transition model of Diabetic Foot Ulcers comparing standard care
versus the IDRT technology along the standard care treatments. Squares represent the
two tunnel states, they reflect the need to account for one-off procedural costs. ............ 28
Figure 2. The Tornado plot represents the results of one-way sensitivity analyses for different
parameters. ....................................................................................................................... 42
Figure 3. Cost-effectiveness plane. .......................................................................................... 43
Figure 4. Cost-effectiveness acceptability curves. ................................................................... 44
Figure 5. The most cost-effective option was also represented on the cost-acceptability
frontier. ............................................................................................................................. 44
Figure 6. The expected value of perfect information for IDRT intervention in patients with
neuropathic diabetic foot ulcer for age group 50-65 years old. ....................................... 45
Figure 7. The expected value of perfect information for population. ...................................... 46
Figure 8. Population expected value of perfect information for groups of parameters. The
population EVPPI is expressed in monetary terms, millions of NOK for WTP threshold
of 550,000 NOK. .............................................................................................................. 47
Figure 9. The molecular biology of chronic wounds and delayed healing in diabetes. ........... 69
V
List of tables
Table 1 Classification system of DFU based on IBPG Wound Management in Diabetic Foot
Ulcers, 2013. ...................................................................................................................... 4
Table 2 Categories of diabetic Foot Ulcers based on IBPG Wound Management in Diabetic
Foot Ulcers, 2013 ............................................................................................................... 9
Table 3 Transition probabilities representing movement between DFU health states in the
Markov state model .......................................................................................................... 33
Table 4 Utility weights representing DFU health states .......................................................... 35
Table 5 Cost estimates representing DFU health states ........................................................... 36
Table 6 Individual cost for treating “DFU” health state with the IDRT intervention and one–
off costs presented in the table. ........................................................................................ 37
Table 7. Total direct hospital cost of Standard Care and IDRT + Standard Care per person are
presented at 12 months and 3 years. All costs expressed in NOK. .................................. 38
Table 8. Cost-effectiveness results for a cohort of patients with chronic DFUs from health
care provider’s perspective which includes only the direct medical costs. Discounted at
4% per annum for a time horizon of three years. ............................................................. 39
Table 9. Number of months spent in a healed state (“No DFU”) during 1st, 2nd, 3rd year and
a total number of months for 3 years presented for Standard Care only and IDRT +
Standard care treatments. ................................................................................................. 40
Table 10. Expected outcomes at 1 year and 3 years after the start of standard care alone and
IDRT combined with standard care. ................................................................................ 41
Table 11. A summary of RCTs and CEAs examining skin substitutes, becaplermin, HBOT,
VAC and optimal care treatments for management of DFUs. ......................................... 69
VI
List of abbreviations
CEA Cost-effectiveness analysis
CEAC Cost-effectiveness acceptability curve
CEAF Cost-effectiveness acceptability frontier
CUA Cost-utility analysis
EQ-5D European Quality of Life 5 dimensions
EVPI Expected value of perfect information
EVPPI Expected value of partial perfect information
HRQoL Health-related quality of life
ICER Incremental cost-effectiveness ratio
IDRT Integra Dermal Regeneration Template
NICE National Institute for Health and Care Excellence
NMB Net monetary benefit
NoMA Norwegian Medicine Agency
PSA Probabilistic sensitivity analysis
RCT Randomized controlled trial
QALY Quality-adjusted life-year
QoL Quality of life
WTP Willingness-to-pay
VII
Table of contents List of figures ........................................................................................................................................ IV
List of tables ........................................................................................................................................... V
List of abbreviations .............................................................................................................................. VI
Abstract ................................................................................................................................................. IX
1 Introduction .................................................................................................................................... 1
Introduction ............................................................................................................................. 1 1.1
Research question .................................................................................................................... 2 1.2
Impact of DFU......................................................................................................................... 3 1.3
Challenge in management of DFUs ........................................................................................ 3 1.4
Review of effectiveness of current treatments in RCT studies ............................................... 5 1.5
Review of cost-effectiveness studies ....................................................................................... 6 1.6
Review of existing methodologies .......................................................................................... 7 1.7
Structure of the thesis .............................................................................................................. 8 1.8
2 BACKGROUND ............................................................................................................................ 9
Diabetic foot ulcer ................................................................................................................... 9 2.1
Risk factors ............................................................................................................................ 10 2.2
Epidemiology ........................................................................................................................ 12 2.3
Disease Management ............................................................................................................. 13 2.4
National Health Care system and National Reimbursement Scheme .................................... 14 2.5
3 Theoretical framework.................................................................................................................. 16
Overview of theory ................................................................................................................ 16 3.1
3.1.1 Economic evaluation ..................................................................................................... 16
3.1.2 Health outcomes ............................................................................................................ 16
3.1.3 Cost-utility analysis ....................................................................................................... 18
3.1.4 Sensitivity analysis ........................................................................................................ 19
3.1.5 Expected value of perfect information .......................................................................... 19
3.1.6 Expected value of perfect information for population and parameter ........................... 20
4 Methods ........................................................................................................................................ 22
Overview ............................................................................................................................... 22 4.1
Perspective .................................................................................................................................... 22
Target population .......................................................................................................................... 22
Health outcomes ........................................................................................................................... 22
Comparator ................................................................................................................................... 23
Intervention ................................................................................................................................... 23
VIII
Half-cycle correction .................................................................................................................... 23
Time horizon ................................................................................................................................. 24
Discount rate ................................................................................................................................. 24
Uncertainty ................................................................................................................................... 24
EVPI and EVPPI .......................................................................................................................... 25
Model structure...................................................................................................................... 26 4.2
Markov state model ............................................................................................................... 26 4.3
Key assumptions.................................................................................................................... 29 4.4
5 Input and material ......................................................................................................................... 31
Parameter list ......................................................................................................................... 31 5.1
Transition probabilities .......................................................................................................... 31 5.2
Utilities .................................................................................................................................. 34 5.3
Costs ...................................................................................................................................... 35 5.4
6 Results .......................................................................................................................................... 38
Cost of treatment ................................................................................................................... 38 6.1
Cost – effectiveness threshold ............................................................................................... 38 6.2
Cost effectiveness analysis .................................................................................................... 39 6.3
Secondary outcomes .............................................................................................................. 40 6.4
Deterministic sensitivity analysis .......................................................................................... 41 6.5
Probabilistic sensitivity analysis............................................................................................ 42 6.6
Cost acceptability curve ........................................................................................................ 43 6.7
The expected value of perfect information ............................................................................ 44 6.8
Expected value of perfect information for population........................................................... 45 6.9
Expected value of perfect information for parameters ...................................................... 46 6.10
7 Discussion ..................................................................................................................................... 48
Main findings ........................................................................................................................ 48 7.1
Comparison to previous research .......................................................................................... 50 7.2
Strengths ................................................................................................................................ 53 7.3
Limitations ............................................................................................................................ 54 7.4
Implications ........................................................................................................................... 58 7.5
Recommendations for future research ................................................................................... 59 7.6
8 Conclusion .................................................................................................................................... 60
References ............................................................................................................................................. 61
IX
Abstract
Background
Diabetic foot ulcer (DFU) is the number one complication from diabetes mellitus type I and II
(DM), a costly disease that also significantly affects the quality of life of the patients. DFU
clinical trial of the bio-engineered skin substitute Integra Dermal Regeneration Template®
(Integra Life Sciences, Plainsboro, New Jersey, US) demonstrated enhanced clinical effect
compared to the standard care alone.
Research objective
To determine the cost-effectiveness of Integra Dermal Regeneration Template® in conjunction
with the standard care compared to the standard care alone in management of non-healing
neuropathic DFUs in Norwegian setting.
Methods
A Markov state model was designed to compare the costs and the health effects from health
care provider’s perspective of IDRT® adjunct to standard care to standard care alone. A 3 year
time horizon was chosen. Transition probabilities were collected from secondary literature
that was based on synthesized clinical trial results, while costs were based on average
estimates of resource utilization in Norway.
Results
Results demonstrated cost savings per patient of 20,235 NOK for the 3 year period and
improved health effect of 0.737 measured as quality-adjusted life years (QALYs). At month
12 patients treated with IDRT® intervention showed improved healing by 30.38%, reduced
infection by 3.8% and reduced probability of amputation by 4.4%. Probabilistic sensitivity
simulation indicated that IDRT® always had a higher probability of being cost-effective
compared to the standard care alone.
Conclusion
Findings of the Markov model indicated that IDRT® is a cost-effective treatment compared to
the standard care alone for non-healing neuropathic DFUs in Norway. Sensitivity analyses
showed that the results are robust to the changes in key parameters. However, the CEAC
stressed that there might be a probability of making an incorrect decision; hence the EVPI
suggested that there is value of investing in further information on utilities to reduce decision
uncertainty.
1
1 Introduction
Introduction 1.1
Diabetes mellitus (DM) is a metabolic disorder which poses an economic burden on
healthcare systems worldwide due to the cost of treating microvascular, macrovascular and
neuropathic complications (Botros et al, 2018). It has increased prevalence that developed
into the global public health peril in the last few decades. Epidemiological literature indicated
an amplifying incidence of 30 million patients with DM in 1985, 177 million in 2000 further
increasing to 285 million in 2010 with predictions of more than 360 million DM patients by
2030 (Yazdanpanah et al, 2015). In the US alone, the cost of managing DM has been
estimated to be over 1.3 trillion in 2015. Remarkably, one third of this cost stems from lower
limb issues (Jeffcoate et al, 2018).
Many other developed countries also struggle with controlling the cost of DM. One of the
largest cost drivers of DM are diabetic foot ulcers (DFUs). For example, due to the common
complications from DFU condition, the healthcare costs in the UK have exceeded 1 billion
pounds, costing the National Health Service 1% of its total budget. Similarly, management of
DFUs in the US has been estimated to cost between 9 and 13 billion US dollars (Jeffcoate et
al, 2018). Similar trends are reflected in other European countries. Specifically, costs of DM
undertook 2.4% of the total national healthcare budget in Norway in 2005, accumulating to
4.2 billion Norwegian krones (NOK) (Solli, 2013). Although the cost of managing DFUs is
unknown, it could be inferred to approximate almost half of DM expenditure given that the
major contributors of cost were hospital admissions, medical devices and drugs (Solli, 2013).
In response to an economic burden posed by diseases such as DM and its complications, the
need for an economic evaluation has become a part of improved decision-making in health
care. An economic evaluation is a tool that facilitates an effective management of resource
allocation and aid decision-makers to decide whether medical technology qualifies for
reimbursement given the cost and the health effect it yields. As a result of poor healing of
DFUs in clinical practice, patients experience low quality of life (Qol) and hospitals face
escalating health care costs, hence it is important to identify a successful treatment solution.
This challenge has been discussed by the Norwegian parliament as stated in
Representantforslag 2016-2017 Dokument 8:91 S, suggesting to provide better prevention and
2
treatment to patients with chronic ulcers (Micaelsen et al, 2017). Few researchers have
addressed this question of cost-effectiveness in treatment of DFUs. Despite the supporting
RCT evidence, the superiority of cost-effectiveness of one treatment or an intervention over
the others has not yet been established. Previous work investigating cost-effectiveness of skin
substitutes as adjunct therapy for non-healing DFUs has been limited to the UK, Netherlands,
France, Germany, Switzerland and US. Thus, it is an underexplored area in terms of
populations that these new technologies could be applied to. It is not clear from the existing
literature whether Scandinavian countries or specifically Norway could possibly benefit from
adopting a skin substitute technology adjunct to standard care in terms of costs and health
effects compared to the current conventional therapy. Finally, the majority of articles on skin
substitutes estimated the cost-effectiveness of treatment of DFUs over 12 month period. This
might not be sufficient to consider the long term outcomes especially in relation to resource
utilization for patients in post amputation state.
The purpose of this analysis is to aid decision making under uncertainty and to contribute to
the existing body of literature on cost-effectiveness of treatments in management of non-
healing neuropathic DFUs in Scandinavia.
Research question 1.2
Following the objective of this cost-effectiveness analysis, the research question aims to
answer whether Integra Dermal Regeneration Template® technology is a cost-effective
treatment option for patients with non-healing neuropathic diabetic foot ulcers in Norway.
In support of the main research question, the following sub-questions will be addressed:
What is the probability of having a healed ulcer?
What is the probability of avoiding an amputation?
What is the probability of avoiding an infection?
Do patients with DFUs benefit from healing faster? What is the length of time spent in
an ulcer free state?
What is the cost per 1 year? Per 3 years?
3
Impact of DFU 1.3
DFU is one of the most common and major complications that follow from both Type 1 and
Type 2 DM. It is a result of deficient circulation in the vascular system that unveils higher risk
in the lower limbs amid other DM complications (Noor et al, 2015). Significantly, individuals
with DM have a lifetime risk of DFU between 15 to 25%. According to the literature on DM
and DFU, patients with DM are prone to develop other chronic complications entailing
retinopathy, peripheral neuropathy, atherosclerosis, and nephropathy. In general, DFUs of
either etiology peripheral neuropathy or ischemic and also infected DFUs precede lower limb
amputations. In particular, the International Diabetes Federation indicated that 85% of all
lower limb amputations precede neuropathic DFUs (Botros et al, 2018). Moreover,
management of DFUs poses this patient group at a high risk of morbidity and a speculated 5-
year mortality rate of 50% post amputation (Boulton et al, 2005; Botros et al, 2018). Patients
with diabetes and a foot ulcer have low quality of life; hence the risk of co-morbidities
increases as time elapses, thereby impeding the healing of a DFU or prevention of its
complications. Disease burden from DFU patients’ perspective is significant due to frequent
outpatient visits and hospitalization, invasive and painful procedures as well as loss or limited
mobility, and fear of amputation (Lazzarini et al, 2016). Common symptoms for patients with
DFUs cause physical, psychological and emotional distress and include pain associated with
the wound and dressing changes, itching, bleeding, excess exudate that leads to unpleasant
odors (Evans et al, 2017 Helsebiblioteket).
Challenge in management of DFUs 1.4
A DFU is regarded as a unique chronic wound with impaired physiological wound healing
cycle. In other words, it can be defined as a “full thickness lesion of the skin of the foot … in
people with diabetes” (Lazzarini et al, 2016). Wound assessment is a crucial aspect in
prevention of the first or recurrent DFU; thereby clinicians can utilize a few available
validated tools (Table 1.) An important facet to consider in evaluation of DFUs is the etiology
whereby can be categorized as neuropathic, ischemic or neuro-ischemic DFUs (Botros et al,
2018; Table 2). It is critical for clinicians to properly evaluate the wound in order to forecast
the likely clinical outcomes given the ability of the DFU to heal. Iversen et al (2017) found
that a delay in DFU assessment leads to more severe ulcers with poor healing prognosis and
consequently enhanced resource use. Provided that, the management of DFUs can be planned
4
more efficiently, thus awareness of an integrated context of various DFU characteristics,
etiology and risk factors is imperative in making clinical judgments.
A systematic review by Netten et al (2016) pointed out that research on health interventions
targeting prevention of a first DFU is nonexistent, whilst the focus is on the prevention of
recurrence. Game et al (2016) systematic review addressed the concern that many of the
routine treatments used in practice lack supporting evidence. Therefore, the RCTs indicate
poor study design, lack of transparency that pertains to the challenge of management of
DFUs. There is a growing body of evidence advocating for multidisciplinary approach in
DFU (Acker et al, 2014). However, the most prevailing treatment of DFUs is a health
intervention - the standard care addressing different needs of a patient depending on the
outcomes from clinical assessment. According to the DFU clinical guidelines, the standard
care consists of wound cleansing, different types of debridement, infection management,
moist wound environment, pressure offloading, negative pressure wound therapy (NPWT).
Up to date, the healing rate of DFUs is known to be poor in clinical practice (Netten et al,
2016), thus standard care is not sufficient on its own and requires a supplementary therapy to
enhance the likelihood of DFU healing. Some existing literature in the DFU field examines
bio-engineered dressings or skin substitutes as a viable supplementary option. Then again a
significant concern is the inferior quality of clinical studies pertaining to DM patients with
foot ulcers whereby suggesting weak evidence in the use of biologically active dressings and
skin grafts (Botros et al, 2018). Overall, it can be implied that further research is required
because no consensus has been reached in terms of a singular treatment strategy. The main
Table 1. Classification system of DFU based on IBPG Wound Management in Diabetic Foot Ulcers, 2013.
Classification system Characteristics
Wagner uses six grades (0-5) to assess the depth of ulcer, presence of gangrene or
loss of perfusion
Meggitt–Wagner ulcers categorized into three groups including infective, non-infective and
mixed
University of Texas uses a matrix of four grades supplemented with four stages to evaluate
ulcer depth, presence of infection, or signs of ischemia
PEDIS
evaluates ulcers using four grades (1-4) in terms of perfusion, size, depth
infection and neuropathy
SINBAD
evaluates ulcer on site, ischemia, neuropathy, bacterial infection, depth;
due to the scoring system predictions of outcomes can be made whereby
enabling comparisons among distinct countries
5
body of literature of DFUs focuses on prevention of incidence of DFUs in DM patients,
whereas for those with a current ulcer – the prevention of complications. Thus, early detection
of a DFU or infection in the existing DFU may offset the economic and clinical consequences
in this sub-group of population.
Review of effectiveness of current 1.5
treatments in RCT studies
Although the burden of managing DFUs is well-known for both the patient and the healthcare
system, there is limited number of clinical studies and cost-effectiveness analyses evaluating
the costs and quality related life for this population. Majority of better quality randomized
clinical trials (RCT) focused on patients who have had full thickness neuropathic non-healing
DFUs for longer than 4 or 6 weeks and with HbA1c from 6% to 12% in order to ensure the
effectiveness of the adjunct intervention of interest. There is a relatively extensive amount of
RCTs indicating the effectiveness of biologically active matrices for the treatment of diabetic
foot ulcers. In a recent systematic review of RCTs of biologic wound treatments (Jordan et al,
2018), some RCTs were identified to show first class level of evidence (Table 11, Appendix
A). These include skin substitutes such as Dermagraft (Marston et al, 2003), Oasis SIS
(Cazzell et al, 2015), Integra Dermal Regeneration Template (IDRT; Integra LifeSciences,
Plainsboro, New Jersey, US; Driver et al, 2015), Apligraf (Veves et al, 2001) and Grafix
(Lavery et al, 2014). Jordan et al (2018) categorized matrices and dermal substitutes in four
groups: acellular dermal matrices, dermal regenerative scaffolds and semisynthetic matrices,
cellular substrates, and placental derived cellular substrates. According to the present health
policy in the US on using bio-engineered skin substitutes for treatment of non-healing DFU,
the Food and Drug Administration (FDA) guidelines approved the following technologies for
reimbursement Apligraf, Dermagraft, and IDRT (Priority Health US, 2017; HMSA, 2017).
Due to a great number of prospective and retrospective studies, the review by Jordan et al
(2018) constrained its analysis to a limited number of clinical trials. In general, the outcomes
of dermal substitutes demonstrated enhanced rates of wound closure and shorter time to
healing. Most of these RCTs were sponsored by the manufacturer hence they might had a
stake in proving the efficiency of their device. The risk of bias should be taken into
consideration. Individuals qualify for a treatment with skin substitutes only if the
conventional therapy fails and is only performed together with the standard care. Moreover,
6
the majority of the RCTs in Jordan’s et al (2018) systemic review recruited patients who had
non-healing DFUs of at least 30 days length. Although the RCTs were designed to compare
patients with hard to heal (chronic ulcers) it can be inferred that different cohorts were distinct
from each other. Specifically, patients with DFUs are prone to various comorbidities or sets of
comorbidities, as well as the location and size of the ulcers vary greatly. Hence, this disease
area is challenging because it is burdensome to design adequate RCTs, thus, only a few
studies produce high quality outcomes on effectiveness of therapies in the DFU area.
Review of cost-effectiveness studies 1.6
Regardless of the number of available RCTs for skin substitutes, Netten et al (2015) prompted
to perform more research in terms of cost-effectiveness studies in the field of DFU
management. Up to date, there are a few existing cost-effectiveness studies assessing optimal
care of DFUs, platelet-rich plasma gel, Becaplermin gel, Hyperbaric Oxygen therapy (HBO)
or collagen based dressings such as Apligraf, Dermagraft, Promogran and porcine small
intestine submucosa (SIS; Oasis Ultra). A systematic search on RCTs and cost-effectiveness
studies examining available treatments of DFUs was conducted and is described in Section
5.1.
In general, the evidence from the current cost-effectiveness studies on skin substitutes is
encouraging. The outcomes indicate that the use of advanced wound dressings or skin
substitutes generates savings while also bettering the quality of life. A summary of CEAs of
different DFU treatment is represented in Table 11 in the Appendix A. Guest et al (2017)
reported that SIS spurred up higher number of ulcer-free months by 42% compared to the
standard care. Equivalently, Redekop et al (2003) found that the probability of amputation
and infection occurrence was reduced by using Apligraf skin graft indicating it to be a cost-
effective option to standard care. Another collagen-based dressing Promogran was assessed in
one cost-effectiveness study and demonstrated cost savings in France, Germany, UK and
Switzerland (Ghatnekar et al, 2002). The authors stressed that more ulcers healed with
Promogran in the first three months compared to the standard care (26% vs 20.7%). Other
cost-effectiveness studies explored the benefits of optimal care which included a
multidisciplinary approach to management of DFUs versus the standard care. Ragnarson
Tennvall et al (2001) showcased that optimal care was cost-effective for different age groups
across distinct levels of DFU risk and severity for Swedish population. More recently, Cheng
7
et al (2017) confirmed optimal care cost-effectiveness in an Australian setting for different
age groups with neuropathic DFUs. A cost-effectiveness analysis by Dougherty (2008) found
that a platelet rich plasma gel (PRP) improved the quality of life as well as the economic
burden when compared to other alternative treatments in patients with non-healing DFUs.
PRP was thought to be cost-saving at a 5 year horizon against saline gel, standard care,
ultrasound therapy, NPWT and three different types of skin substitutes. However, Kantor and
Margolis (2001) findings showed that Becaplermin gel was even less costly and more
effective than PRP 20 weeks post treatment. Finally, although the benefit of Hyperbaric
Oxygen therapy remains unestablished (Hinchliffe et al, 2008), Chuck et al 2008 concluded
HBO to be cost-effective versus standard care in Canada at 12 year horizon. HBO therapy
yielded more quality adjusted life years (QALYs) than the conventional therapy (3.64 vs
3.01).
In sum, the review of cost-effectiveness studies regardless of medical therapy, demonstrate
more benefits in both economic and QALYs with advanced technologies or dressings.
Notwithstanding the positive findings, the limitations of clinical trial methodology cast a
shadow of uncertainty whether any of the aforementioned therapies would be effective in the
clinical practice.
Review of existing methodologies 1.7
Given that a diabetic foot ulcer is a complex chronic condition, researchers in this field have
adopted decision-analytic techniques in order to extrapolate the findings from short-term
RCTs. Economic evaluation quantifies health outcomes and the costs of interventions in order
to determine whether an intervention of interest improves health of a particular patient
population. Moreover, cost-effectiveness analyses are built on study designs that address
causal questions. Additionally, it requires information that pertain computation of effects and
costs. The review of research methodologies of DFUs indicated that almost all of the
published studies employed a cost-effectiveness design by using a Markov model.
By far, the most cited Markov model of DFUs has been produced by Persson et al (2000). It
has been utilized and adapted by other researchers in the field of DFUs and health economics.
For illustration, a series of systematic reviews by Nelson et al (2006) validated this model as a
comprehensive model and a great reflection of natural history of DM patients with foot ulcers.
8
Persson et al (2000) Markov model enables simulation of foot ulcer related complications and
recurrences of DFU over the lifetime. The model comprises of six discrete health states
including healed, uninfected ulcer, infected ulcer, gangrene, amputation, healed with history
of amputation and deceased. Person et al simplified their model by making a couple of
assumptions. For instance, the infected ulcers were thought to cause 80-85% of amputations,
whereas gangrene preceded 15-20% of amputations.
Cheng et al (2017) in its recent cost-effectiveness analysis of optimal care in Australia
adapted Persson et al (2000) model with a few adjustments. In total, their Markov model
entailed seven health states as follows no DFU, uncomplicated DFU, complicated DFU with
infection, post minor amputation, post major amputation, infected post minor amputation and
dead. Thus, Cheng et al (2017) model reflects the clinical outcomes of DM patients with foot
ulcers more precisely compared to the Persson et al. The main reason for that is the inclusion
of minor and major amputations that were proven to demonstrate differences in QALYs and
costs. Significantly, gangrene state has been removed and only one state representing a
complicated DFU remained, namely DFU with infection. Such decision has been supported
by the lack of existing data pertaining to different severity levels of infections. Other Markov
state models used in cost-effectiveness studies were either the same as Persson et al (2000) or
with very minor adjustments, hence not discussed in this review.
Structure of the thesis 1.8
The remainder of the paper is organized into Background, Theoretical framework, Methods,
Input & Material, Results, Discussion and Conclusion sections. Section 2 presents
background information on DFU disease, its epidemiology, available treatments. Moreover,
risk factors, disease management and the effect of the national reimbursement scheme will be
briefly explained to put this CUA in the context. Section 3 reviews theory in economic
evaluation and health economics and provides definitions of terminology used in this field.
Section 4 outlines the methodology utilized for cost-effectiveness analysis and also methods
that address the uncertainty in parameters such as probabilistic sensitivity analysis (PSA),
expected value of information (EVPI). Section 5 describes the sources of model input in more
detail. Section 6 outlines the findings from analysis on the ICER, costs of both treatments
over one and three years. Section 7 is devoted to discussion, limitations of this analysis,
implications and direction for further research. Section 8 concludes this CUA.
9
2 BACKGROUND
Diabetic foot ulcer 2.1
A diabetic ulcer or sometimes referred as a diabetic wound, is an external, open sore below
the ankle caused by the break in the skin. Wound healing is a complex process that is
dependent on an integrated sequence of events consisting of collaborative interaction among
diverse cell types, growth agents and enzymes (Blakytny et al, 2006; Fig. 9 in Appendix A).
Normal ulcer healing mechanism progresses through the following stages: clot formation,
inflammation, re-epithelialization, angiogenesis, granulation tissue formation, wound
contraction, scar formation and tissue re-modelling (Blakytny et al, 2006). Nevertheless,
when an ulcer fails to follow normal skin regeneration chain of events, it becomes a chronic
ulcer indicating a deteriorated ability to heal (Dickinson et al, 2016). The DFUs can be
categorized into neuropathic, ischemic or neuro-ischemic ulcers (Table 1).
Table 2. Categories of diabetic Foot Ulcers based on IBPG Wound Management in Diabetic Foot Ulcers,
2013
Feature of DFU Neuropathic Ischemic Neuro-ischemic
Sensation loss of sensation pain loss of sensation
Callus/necrosis presence of callus; thick
callus
common to have
necrosis
low callus; probe to
necrosis
Wound bed pink and granulating,
surrounded by callus
pale and sloughy
with poor
granulation
poor granulation
Foot temperature
and pulses
warm with bounding
pulses
cool with absent
pulses
high risk of infection
Other dry skin and fissuring delayed healing high risk of infection
Typical location weight bearing areas of
the foot including
metatarsal heads, the
heel, over the dorsum of
clawed toes
tips of toes, nail
edges, between
the toes, lateral
borders of the
foot
margins of the foot and
toes
Prevalence 35% 15% 50%
10
Development of the diabetic foot ulcers is triggered by complications of diabetes such as
neuropathy, vascular foot changes and deformities on the feet, nephropathy, retinopathy,
cardiovascular disease (CVD). The pathophysiology of a DFU indicates that such processes
are expedited by cellular and biochemical irregularities in patients with DM (Alavi et al,
2014). Different types of neuropathy can be present in patients with diabetes contributing to
the development of a foot ulcer. Damaged nerves on the muscles of the foot result in
deformed shape and can be defined as motor neuropathy. As a consequence, it triggers the
development of an ulcer due to excessive pressure points on the affected foot. Second,
sensory nerve damage impairs individual’s ability to feel pain or pressure. It is common for
sensory neuropathy to occur on the foot. Third, Charcot neuropathy deforms the bones in the
foot due to the high blood flow and is termed as a Charcot foot. Finally, other body systems
may be influenced if an individual is affected by an autonomic neuropathy that damages the
sympathetic and parasympathetic nerves (Alavi et al, 2014).
The DFU condition is a serious debilitating disease that affects both the physical and
psychological health aspects. Patients with DFUs not only have high morbidity and mortality
but also suffer from decreased quality of life (QoL) (Boulton et al, 2005).
Risk factors 2.2
Vascular disease, foot deformity, previous DFU or amputation, and peripheral neuropathy are
known as the main contributors of DFU etiology. That being said, ulceration in the foot is a
result of an interaction of multiple risk factors. Research on risk of developing DFU
demonstrates that a history of a previous foot ulceration or amputation increases such risk
(Katsilambros, 2010). There is also an existing evidence of long duration of diabetes and poor
diabetes control impact on foot ulceration. In particular, a study by Al-Rubeaan et al (2015)
found that diabetes duration of more than 10 years increased an occurrence of DFU and a
need for amputation rate by 3 to 4 times. Whereas, due to poor glycemic control diabetic
patients are exposed to two-times elevated risk of DFU. Diabetic complications precede
hyperglycemia, where elevated blood sugar increases the number of inflammatory cells and
low response to infection (Alavi et al, 2014). Thus, hyperglycemia hinders the normal
function of the different cells participating in the healing process. Due to aforementioned
cellular changes, the diabetic patients are predisposed to an elevated risk of ulcer infection or
osteomyelitis. If a deep wound infection such as osteomyelitis is suspected, it requires
11
additional diagnostics because it is a challenging diagnosis that can manifest in 60% of
hospitalized individuals and in 20% of outpatients. An adequate diagnosis or the golden
standard of osteomyelitis diagnosis is performed with magnetic resonance imaging (MRI).
Such complications lead to the death of tissues known as gangrene followed by lower
extremity amputations (Blakytny et al, 2006). Therefore, patients with diabetes may be
exposed to an altered ulcer healing process compared to non-diabetic patients (Blakytny et al,
2006). Moreover, the estimation of DFU risk in patients with diabetes is more complicated
than in non-diabetics because of the asymptomatic nature of diabetes. The diagnosis of DFU
is somewhat complex and thus requires a multidisciplinary team of specialists to clinically
evaluate the health state of the patient.
Furthermore, DM patients that experience callus formation, neuro-osteoarthropathy and
exhibit limited join mobility are at the higher risk of DFU. Several studies indicated an
elevated DFU predisposition in males (Al-Rubeaan et al 2015). Prevalence among patients
with diabetes of age >= 45 is considerably more prominent in development of DFU (Al-
Rubeaan et al 2015). Age and diabetes duration risk factors are equally present in both types
of diabetes. It was observed that a lower prevalence of DFU was among younger patients
between 1.7 – 3.3% and 5-10% in older patients. Additionally, older age is positively
associated with amputation rate, 1.6% among 18-44 years, 3.4% among 45-64 years and 3.6%
in over 65 years olds (Katsilambros et al. in Al-Rubeaan et al 2015). Age specific prevalence
is lower in females compared to males. Al Rubeaan et al (2015) found that diabetic patients
with hypertension condition had more than 50% of occurrences of ulcers, gangrene and
amputations. Other risk factors among diabetic patients entail social factors such as low
socioeconomic status and education level, restrained access to health care as well as
withdrawn lifestyle are all related to the DFU. Additionally, patient’s ability to comply with
prescribed medical procedures. A recent study by Pereira et al (2017) demonstrated that foot
ulcerations can also be caused by the skin microbiota among diabetic patients.
In sum, risk factors that can affect skin integrity and ulcer healing are as follows: high
glycosylated hemoglobin (HbA1c) levels, ill-fitting footwear, neuropathy, bony deformity or
restricted joint mobility, peripheral arterial disease (PAD), history of a wound or amputation
and age. However, there is no consensus as to which of aforementioned risk factors are the
most important and threatening (Netten et al, 2016).
12
Epidemiology 2.3
In a recent systematic review, Zhang et al (2016) estimated the global prevalence of DFU to
be 6.3%. North America owing to the highest prevalence of 13%, Europe 5.1% and Oceania
region indicated the lowest 3% prevalence. Other study by Amin et al (2016) reported
prevalence between 4% and 10% and an annual incidence rate range of 1%-4%. There is a
vast amount of literature that demonstrated the lifetime risk of development of a DFU in
diabetic patient population between 15% and 25% (Bartus et al, 2005; Walters et al 2016).
Very little is known about the lower extremity amputations related to diabetes, the incidence
rate seems to be much more heterogeneous among different countries. Previous research has
identified that DFU prevalence is much higher in individuals with type 2 diabetes (6.4%) than
type 1 diabetes patients (5.5%) (Zhang et al, 2016). Moreover, higher prevalence of DFU is
observed in individuals older than 60 years old.
Estimation of incidence and prevalence presents researchers with challenges due to the
diagnostic methods used and the population selection (Amin et al 2016). DFU recurrence
rates are as high as 50% and it increases further after 3 years (Boulton, 2005). Out of all
patients that are newly diagnosed with diabetes approximately 8% develop neuropathy.
Whereas the prevalence of neuropathy increases in those with chronic diabetes and can affect
more than 50% of the population (Walters et al, 2016).
According to the evidence from several Norwegian studies, DFU prevalence is between 7-
10% (Robberstad et al 2017). In comparison with other countries an occurrence of DFU was
higher than in the following international studies 2.2%, 4.1%, 2.1% and 7.4% respectively
(Abbott et al., 2002; Abbott et al., 2005; Tapp et al, 2003; Walters, Gatling, Mullee, & Hill,
1992). This prevalence rate is in line with estimations made in Zhang et al (2016) research
though. The epidemiological estimate of 7.4% in Molvær et al (2014) study was based merely
on population in the Nord-Trøndelag county in Norway. Therefore, this might not be
representative to the country in general (Krokstad & Knudtsen, 2011).
These changes are observed due to increasing prevalence in DM, for instance in Norway,
prevalence rose from 2.5% in 2005 to 3.2% in 2011. (Robberstad et al, 2017). As a result, it is
speculated that approximately 400-500 lower extremity amputations take place annually due
to DFU in Norway.
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Disease Management 2.4
As mentioned earlier, diverse etiologies of DFU and complex physiological mechanisms
involved in normal wound healing holds a strain on finding effective clinical treatments. A
considerable amount of literature has been published on available treatments for DFU patients
(Game et al, 2016). However, a major challenge is that the results from randomized control
trials remain unclear and fail to provide consistent evidence.
All patients with DM should receive annual checks for the risk of a DFU. Aforementioned
risk factors of DFU including neuropathy, limb ischemia, ulceration, callus, infection,
deformity, gangrene and Charcot arthropathy should be evaluated by health professionals.
Additionally, the latter should be followed by the risk assessment of an amputation which
then can be categorized as low, moderate and high-risk groups. Patients with high risk
characteristics include previous ulcer or amputation, on renal replacement therapy; have
combined neuropathy and non-critical limb ischemia, combined neuropathy with callus or
deformity, also non-critical limb ischemia combined with callus or deformity, and an active
DFU.
Disease management is heterogeneous and thus healing of ulcers heavily relies on the
complexity of the ulcer. Therefore, clinicians need to be aware of a need of different
combination of treatment strategies for easy, moderate and difficult ulcers. Assessment of
DFUs utilizes validated wound assessment tools including Wagner, Meggitt-Wagner,
University of Texas, PEDIS and SINBAD (Table 1.) Notably, the University of Texas system
offers the best accuracy in predicting the risk of an amputation and other complications
(Botros et al, 2018). Notwithstanding the use of Wagner classification tool in numerous
studies, NICE specifically notes against its application in assessing the severity of DFUs.
The National Institute for Health and Care Excellence (NICE) guidelines on treatment of
DFU provides a detailed description (Best practice UK guidelines). It has also been
referenced by a Norwegian organization responsible for wound care (Norsk
interessefaggruppe for Sårbehandling) for good practice in a Norwegian setting. Despite the
great number of developed treatments for DFUs, the standard care remains the most common
medical treatment. Standard care includes a combination of 1 or more procedures depending
on the etiology of the DFU. For example, it may combine wound dressings with offloading;
wound debridement, management of foot infection as well as ischemia (NICE guidelines,
14
2015). Standard care for uninfected ulcers require daily saline gauze dressing changes for the
first two weeks and every second or third day for the following weeks. An infected ulcer may
require having bandages changed twice per day in combination with a 14 day course of
antibiotics. Whereas care for patients in either minor or major post amputation states requires
home care, community care and outpatient visits to the specialists as well as prostheses.
The current market offers a broad range of topical therapies for DFUs, though the evidence
from clinical trials of their effectiveness is limited (Lavery et al, 2016). Moreover, an
important point to highlight is that all recommended medical procedures should be provided
by the specialized health care professionals, ideally from a multidisciplinary foot care team. A
multidisciplinary team (MDT) entails clinicians that contribute knowledge to the different
aspects of diabetic foot problem such as podiatrists, specialist diabetic nurses, orthopedic
surgeons, vascular specialists and endocrinologists (Amin et al 2016; Buggy et al, 2017). A
recent systematic review (Buggy et al, 2017) found some positive associations of MDT on
reduced rate of amputations, resource use, mortality and quality of life (QoL). However, their
findings are inconclusive due to the questionable methodological quality of some studies.
Furthermore, in combination with the standard wound care NICE advises to consider NPWT,
dermal or skin substitutes in treating a complex DFUs (NICE guidelines; Amin et al, 2016).
While the WHS DFU guidelines also mention surgery and prevention of recurrent ulcers
(Lavery et al, 2016). In case of infection complications, antibiotics are prescribed for 2 weeks
for a mild DFU soft tissue infection. More severe cases such as osteomyelitis may require
prolonged antibiotic treatment up to 6 weeks, usually treating with intravenous antibiotics.
National Health Care system and National 2.5
Reimbursement Scheme
In accordance with principals of equal access, quality of services, and free choice of provider,
the provision of health care services is a primary responsibility of the government, and is
known as the Norwegian National Health Care system. Thus, health care in Norway is owned
and funded by the state, more specifically 85% of health care expenditure is attributable to
public financing and the rest is private financing. Primary health care is organized and funded
at the municipal level, whilst specialist health care is coordinated by the four Norwegian
regional health authorities, hence health care provision is decentralized. Amongst the
15
countries in the Organisation for Economic Co-operation and Development (OECD),
Norway’s expenditure was estimated at 9.9% of the GDP in 2015, whereas the average is
8.9% (Lindahl, The Norwegian Health Care System). All the residents are automatically
covered by the universal National Insurance Scheme (Folketrygden, NIS) which is financed
through the national and local tax revenues. To put it differently, national taxes consists of
employer and income-related employee contributions in addition to the co-payments.
Furthermore, the general reimbursement of approved pharmaceuticals guarantees at least
partial refund to the patients and is managed by the Norwegian Medicines Agency (NoMA).
Pharmaceuticals without general reimbursement are monitored by the Health Economics
Administration (Helseøkonomiforvaltningen, HELFO). Nevertheless the state reimbursement
of health interventions is somewhat more complex and less transparent due to decentralization
of provision compared to reimbursement of pharmaceuticals. The intervention of interest in
this CUA is the IDRT technology adjunct to the standard care which entails a variety of
procedures depending on the need of the patient with a DFU. Although Norwegian guidelines
in management of DFUS include all the aspects of this treatment, it can be inferred that in
practice standard care is of lower standard. In addition, patients with chronic neuropathic
DFUs receive care not only in the hospitals but also require community care. As established
before, reimbursement of care at hospital level and municipal level are funded differently, and
thus it is challenging to define how this coverage decision could be achieved to ensure a
homogenous health intervention across Norway.
This issue has also been raised by the Norwegian parliament (Micaelsen et al, 2017).
Markedly, it has been identified that the government should implement new measures for
patients with chronic wounds who require better prevention and treatment (Micaelsen et al,
2017). The parliament suggested that the objective should aim to reduce the number of
patients with chronic wounds and amputations. What’s more, to address the poor current
practice, the need for enhanced clinical competence and interaction between the specialist
care and community care was stressed by the parliament. In other words, chronic wound care
should be coordinated to achieve the best outcomes for patients.
16
3 Theoretical framework
Theory overview 3.1
3.1.1 Economic evaluation
The role of economic analysis is profound in making judgments of social values, specifically
in a health context. In general, economic evaluation is used to advise on a range of pragmatic
or inevitable decisions that need to be made regardless of the fact that decision makers will be
evaluating the existing evidence or not (Drummond et al, 2005). Due to that, health care
service provision is highly depended on such decisions and the expected health effects. With
the aim to determine the optimal course of action given the best evidence available, the
outcomes of two alternatives are compared. This refers to the notion of scarce resources
which encompasses efficiency of resource allocation and the benefits of alternative treatment.
When a decision is made to finance a treatment for a population with lung cancer patients, for
instance, than these resources will be unavailable for other patient groups. In sum, economic
evaluation aids improved decision-making because it considers whether what is given up by
one patient group as a result of additional costs of intervention can be justified given its
benefits to the immediate recipients.
Economic evaluation can take form in any of the three analyses: cost-benefit (CBA), cost-
effectiveness (CEA) and cost-utility (CUA). The main difference between these techniques is
an expression of health effects. For illustration, costs and effects in CBA are expressed in
monetary terms, whereas in CEA the effects are expressed in natural units – life years gained,
and in CUA the effects are quantified in quality-adjusted life years (QALYs).
3.1.2 Health outcomes
As proposed by Drummond et al (2005), a quality-adjusted life year (QALY) is the preferred
measure of health gain when conducting economic evaluations. This has also been approved
by the Norwegian Medicines Agency (NoMA). QALY is a generic measure that reflects the
state of health comprising an element of the length of life as well as health-related quality of
life (HRQoL). It is an advantageous feature since QALYs of different treatment options can
be easily compared within and across medical interventions. In particular, this is of benefit to
17
the budget holders and decision-makers because it helps to determine the opportunity costs.
Opportunity cost concept explains the value forgone per investment and how it compares
within the health care and other sectors.
Subsequently, a quality of life assessment is related to the value of a specific health state. The
intention of the HRQoL measure is to showcase the overall well-being including the aspects
of physical, psychological and social health. Notably, HRQoL are expressed in values
between 1 as in perfect health and 0 – death, albeit some health states are worse than death
and hence, they obtain a value below 0.
There are numerous generic and specific utility instruments that evaluate the HRQoL values
with a corresponding weight. Markedly some of the generic instruments include EQ-5D-3L,
15D, or EuroQol. Although all of these instruments have the same goal of assessing health
states, they differ in their structure, for example number of dimensions and severity levels.
Thus, the weights are pre-determined because they are usually measured beforehand with one
of the following techniques the time-trade-off (TTO), visual analogue scale (VAS) and the
standard gamble method. After the HRQoL values were obtained, QALYs were calculated by
multiplying HRQoL for one state by the length of time remaining in that state. Please refer to
Section 5.3 for more details on utility values used for this CUA.
In economic evaluation, the mathematical models are utilized in a way that accounts of
resource use for a given healthcare issue. Decision makers are driven by the set budgets and
also by ensuring the return of investment. Hence, the perspective of analysis is indispensable
for when it comes to cost estimation. Determination of costs for a particular treatment relies
on identification of all relevant resources and the best representative units that help to
quantify the consumption of resources. Therefore, ISPOR guidelines recommend the societal
perspective which includes the key health outcomes and costs for the health care payer,
public, patient and their relatives or friends (Roberts et al., 2012).
Modelling of chronic diseases usually suggests adopting a lifetime horizon due to belief that
all future consequences are apprehended. Yet it is known that the longer the time horizon, the
more uncertainty it introduces in the economic models.
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3.1.3 Cost-utility analysis
Cost utility analysis (CUA) is one of the methods used in economic evaluation that guides
decision making in health care while also ensuring the return on investment. That is, the
decision makers effectively manage the consumption of resources by maximizing the benefits
of a medical intervention within the budgetary constraints. Additionally, CUA is a type of
cost-effectiveness, (Brazier et al, 2007) most commonly a preferred method of analysis by
Health Technology Assessment (HTA) agencies in Europe including The Netherlands,
Germany, Norway and the UK as specified by the Institute for Health and Care Excellence
(NICE). As mentioned before, health outcomes are estimated and converted into a generic
measure known as a QALY. QALY is a useful measure because it is comparable across health
conditions and between various medical interventions (Brazier et al, 2007). The primary
interest of the CUA is the incremental cost-effectiveness ratio (ICER). Specifically, the
calculation of ICER is expressed as the incremental cost to gain an additional unit of QALY.
Moreover, ICER representing the IDRT and standard care treatment of DFU over the standard
care only is given in the formula below:
ICER =
Cost IDRT + Standard care – Cost Standard Care only = Incremental cost
QALY IDRT + Standard care – QALY Standard Care only Incremental effect
In an event of a negative ICER, the IDRT along standard care intervention would be deemed
either dominant or dominated. When an intervention is considered dominant it means that it
yields more health gain for smaller costs. In contrary, a dominated ICER refers to an
intervention with less health benefits and higher costs. Cost-effectiveness of the intervention
depends on the decision rule set by the willingness to pay (WTP) per QALY obtained. The
WTP varies across different countries, and thus, what is deemed to be cost-effective in one
country may not be true for other countries. NICE estimated WTP is €37,500 per gained
QALY, whereas threshold in Norway highly depends on the level of severity. For instance, a
threshold of 588,000 NOK has been used as WTP among researchers in Norway in order to
evaluate cost-effectiveness. Meanwhile the WTP threshold for market access depends on the
impact of disease including the length of life loss and the quality of life with the disease.
19
3.1.4 Sensitivity analyses
Deterministic sensitivity analysis (DSA) is a tool to examine the impact of variation in certain
input parameters or a set of parameters on an outcome such as ICER. Particularly, the chosen
parameters are changed manually within a pre-set range and then the effect of the change is
analysed. If one parameter is simulated at a time then it is a univariate sensitivity analysis.
This requires a cautious interpretation of univariate sensitivity analysis results because most
of the time the input variables are highly correlated. Then again, it is beneficial for
development and review of a model because one can explore and check the structure of the
model (Drummond et al, 2005).
Multivariate sensitivity analysis can simultaneously simulate two parameters and thus it is
represented in a two-way threshold analysis (Drummond et al, 2005). The outcome of
deterministic sensitivity analysis is presented in a graphical way or bar charts.
In sum, the deterministic analysis emphasizes the sensitivity of the model output given the
changes in input albeit no conclusions can be made about the uncertainty of the decision.
Likewise, it does not specify which parameters add to decision uncertainty.
3.1.5 Expected value of perfect information
According to Briggs et al (2011), the CEAC along expected value of perfect information is
the best method to represent decision uncertainty from the PSA. The principles of value of
information can serve to evaluate the significance of uncertainty and then comprehend
whether alternative research topics or investigation of certain parameters need to be
prioritized. Patient outcomes can be improved with the supplementary evidence supporting
the cost-effectiveness of current intervention and diminishing the uncertainty around it. With
this in mind, reliance solely on existing evidence indicates a chance that other unexplored
interventions may be more beneficial. Moreover, the expected value of perfect information
(EVPI) analysis was performed post PSA to calibrate the costs related to uncertainty. By
deploying this technique, the parameters of interest that indicate high uncertainty are assessed
and a value for yielding perfect information can then be estimated. Indeed the concept of
perfect information refers to an assumption that an analyst obtains the true value of a
previously unknown (uncertain) parameter. Therefore, perfect information validates all model
input confirming the absence of the probabilistic uncertainty.
20
In the process of defining EVPI, a treatment option with the highest NMB with the existing
information is calculated. It is assumed that there are alternative interventions that contain net
benefits dependent on ambiguous θ parameters, and thus a decision is made choosing an
intervention with the highest expected NB, given the current evidence.
maxj Eθ NB (j, θ)
Provided that the perfect information removes the uncertainty, then the values of θ were
known prior to making a decision. In the subsequent formula, one can choose an intervention
that maximises NB for every value of θ:
maxj NB (j, θ)
Notwithstanding, θ values with the perfect information are obtained through an estimation of
expected NB by computing the average of the highest NB for all iterations in the simulation
that represent potential values of θ.
Eθ maxj NB (j, θ)
Therefore, the value of perfect information (EVPI) is the difference between decision with
perfect information in relation to uncertain θ parameters subtracted by decision made with the
current information:
EVPI = Eθ maxj NB (j, θ) - maxj Eθ NB (j, θ)
3.1.6 Expected value of perfect information for population and
parameter
It is common to continue the analysis with calculation of the population EVPI (pEVPI). When
the EVPI is set to account for the entire population of interest, it is to acknowledge that better
decisions can be made for an applicable patient group. The formula below entails components
about the effective lifetime of technology (t) as well as estimate incidence of patients during
that period (It). Future EVPI for patients is subject to being discounted at rate (r).
pEVPI = EVPI. ∑t=0,1, 2,…,T It /(1+r)t
Provided pEVPI demonstrates that an investment in further research surpasses the costs of the
uncertainty, hence pursuing to conduct more research can be cost-effective.
21
Essentially EVPI and pEVPI analyses disclose the value of rectifying uncertainty associated
with the choice between the available treatment options. Yet to obtain more details on which
particular parameters contribute to uncertainty and the type of supplementary evidence that
brings most value the expected value of perfect parameter information (EVPPI) follows the
other two analyses. EVPPI analysis identifies the different sources of uncertainty that
influence the NB of the treatment options. By focusing research on parameters with the
highest expected value of information savings can be made. For illustration, the latter feeds
into improved choice of RCT type or design of RCT, additionally, it could determine the
sequence of types of studies (Drummond et al, 2005).
The same principals apply in EVPPI method as in EVPI analysis. Thus, the expected value of
perfect parameter information (EVPPI) can be computed for both an individual patient and for
the entire population of interest. Then again EVPPI is the difference between a decision with
the perfect information for a group of parameters and a decision with current information.
Hence, the perfect information for a set of parameters or a single parameter becomes θ1
whilst the remaining uncertain parameters are defined as θ2.
EVPPIθ1 = Eθ1 max j Eθ2 Iθ1 NB (j, θ2, θ1) – max j Eθ NB (j, θ)
Here θ reflects the one used in EVPI formula to indicate uncertainty in all parameters, thus θ
= θ1 + θ2. The results from the EVPPI are set into inner and outer loop simulations for all
uncertain parameters θ2 while setting specific values for examining the group of Q1
parameters. The simulations are repeated multiple times, usually with 1,000 iterations for
inner loop and same for the outer loop to produce enough samples for further analysis (Briggs
et al, 2011).
22
4 Methods
Overview 4.1
Perspective
This cost-utility analysis of treating DFU patients in Norway adopted a narrower healthcare
provider perspective (NoMA). The healthcare provider perspective solely reflects the health
outcomes that are experienced by the patient and direct medical costs that entail health service
provision in relation to the treatment strategy. Cost inputs from the societal perspective
specific to Norway were challenging to collect, thus out of pocket co-payments,
transportation, productivity loss at work was not included in the analysis.
Target population
The cost-effectiveness analysis is based on one cohort of patients with specific baseline
characteristics. Target population consists of females and males aged between 50 - 65 years
old with a mean age of 57.5. This certain age group represents patients that are at high risk of
developing a DFU because it was assumed that all patients have been diabetic for at least 10
years. All selected patients had DM and had a full thickness neuropathic lower extremity foot
ulcer that lasted more than 6 weeks to qualify for a chronic DFU. The baseline characteristics
were based on cost-effectiveness and RCT studies in western countries; hence, this is
comparable to the Norwegian population. Particularly, the patients were evaluated on
characteristics such as age, gender, duration of diabetes, and type of diabetes (both types
included in majority of studies for treatment of DFU).
Health outcomes
The primary health outcome of this analysis is a quality-adjusted life years (QALYs). In
addition to the primary health outcome, costs related to the IDRT intervention and standard
care were reported at 1 year and 3 years. Moreover, this analysis provided information on
clinical outcomes such as ulcer-free months, probability of avoided infection, and probability
of avoided amputation.
23
Comparator
The comparator in this CUA is the standard care as described in Norwegian guidelines (Norsk
Interessefaggruppe for Sårheling) and also resembles the NICE guidelines (Best practice UK
guidelines). Depending on the needs of the patient, the diabetic foot can include one or a
combination of procedures as follows: offloading, managing a foot infection, and controlling
ischemia, wound debridement, keeping the wound area moist by changing wound dressings.
Intervention
In this economic evaluation the intervention includes the Integra Dermal Regeneration
Template®
(IDRT) along the standard diabetic foot ulcer care. The Food and Drug
Administration (FDA) cleared 510(k) and their products using the IDRM technology were pre
market approved. IDRT technology is a cellular, bilayer matrix devised to improve skin
regeneration processes. The first layer acts as a dermal replacement that eventually degrades
and it entails collagen, the glycosaminoglycan, and chondroitin-6-sulfate (Driver et al, 2015).
The epidermal layer contains silicone and takes a mechanical function to provide temporary
protection and aid as a shield from bacterial contamination. The IDRT technology has shown
a proven effect in third degree burns, scar reconstruction, acute and chronic wounds. A multi-
center RCT by Driver et al (2015) reported that the majority of DFUs completely healed with
merely one application of IDRT technology. In addition, when IDRT is used, the number of
dressing changes also decreases. For this cost-effectiveness analysis a conservative
assumption was made, a patient with DFU needs four applications to complete ulcer healing
and have their dressings changes every second day for the first week, and three times for the
subsequent weeks.
Half-cycle correction
Half-cycle correction method is widely applied in Markov models as a more precise reflection
of reality. This is mainly due to the modelling practice that accounts of transitions between
health states either at the start or the end of the chosen cycle. However, it is more likely that
on average patients will transit during the cycle. Depending on the timing of transition,
Markov models estimate the costs and health gains that may be either underestimated or
otherwise overestimated. As a result, a half-cycle correction tackles these calculation
24
discrepancies so that the evaluation of costs and health outcomes are representative of
changes.
Time horizon
Management of non-healing neuropathic DFUs is complex, therefore a three year time
horizon was considered appropriate to account for all related outcomes such as costs and
health effects (Roberts et al, 2012).
Discount rate
Future costs and health outcomes for the treatment of DFU patients were discounted at 4%
per year. This discount rate is recommended by the NoMA as well as Norwegian Ministry of
finance (Statens legemiddelverk, 2012).
Uncertainty
One-way sensitivity analyses were conducted to examine the impact of utility parameters,
costs of Markov states and the key transition probabilities on the ICER. A tornado plot was
used to represent the effects on the ICER. The y-axis on the tornado plot specifies the value of
the ICERs for a single parameter and identifies the minimum and maximum values. The x-
axis indicates the different ICER values.
Provided the limitations of deterministic sensitivity analysis, mainly using point estimates, the
probabilistic sensitivity analysis (PSA) and the calculation of the expected value of perfect
information (EVPI) was performed to estimate the effect of global uncertainty on the model
output.
PSA, therefore, is a feasible method to address the uncertainty of all model inputs
simultaneously. Thus, it provides a quantitative indication of decision uncertainty, a
significant feature that aids superior differentiation between poor and good decisions. Due to
the PSA one can express the extent of confidence in the output given the uncertainty of the
model inputs. Moreover, to reflect the uncertainty in each parameter, distributions were
specified based on mean values and standard errors per parameter. Beta distribution was
applied for binomial data, utility values and transition probabilities. Gamma distribution was
used for costs and log-normal distribution for relative risk parameters (Briggs et al, 2011).
25
Then the model was run by setting the probabilistic value per parameter and a vector to assess
the single estimate of output. The VBA macro was recorded to run the PSA simulation with
1,000 repetitions. As a result, 1,000 ICERs will be plotted in the cost-effectiveness plane.
Given the relevant WTP this will determine which ICERs are cost-effective.
Following the PSA simulation the cost-effectiveness acceptability curves (CEAC) were
plotted for the IDRT along the standard care intervention and standard care only treatment as
a function of WTP threshold. Thus, the total cost and effects were estimated per single
iteration out of 1,000 iterations. Provided a set cost-effectiveness threshold lambda of 550,000
NOK, a net monetary benefit (NMB) was calculated using the following formula:
NMB = lambda * Effect – Cost
The CEAC represents the probability of being cost-effective for the two treatment options.
Moreover, the probability is evaluated by quantifying the proportion of 1,000 iterations with
the highest NMB. Cost-effectiveness of the two treatments is demonstrated for different WTP
thresholds on the CEAC graph.
In addition to the CEAC, the uncertainty for choosing one treatment over the other was also
presented in the cost-effectiveness acceptability frontier graph (CEAF). The CEAF includes
only the cost-effective part of treatment options for a range of WTP thresholds.
EVPI and EVPPI
The calculation of expected value of perfect information (EVPI) was utilized to identify an
upper threshold of a monetary value related to the supplementary research in order to decrease
the overall parameter uncertainty. Its monetary value is associated with the probability of an
intervention being cost-effective meaning that there is a likelihood of making a wrong
decision. Thus, higher EVPI values yield higher opportunity costs. This EVPI analysis
generated 1,000 simulations across a range of threshold values. Moreover, the computation of
effective population of Norwegian patients with non-haling neuropathic DFUs aged 50 - 65
was performed to evaluate the EVPI for population.
In addition, expected value of perfect information for parameter (EVPPI) analyses was
performed to gain further understanding which specific parameters show uncertainty. Thus,
EVPPI was conducted on a one single parameter - the effectiveness of intervention in healing
26
ulcers, group of utilities, group of costs for Markov states and group of transition
probabilities.
Model structure 4.2
In an attempt to answer the research question whether the IDRT intervention along the
standard care is a cost-effective option for the treatment of DFUs in Norway, a state transition
Markov model was adopted. The Markov model simulates the consequences of the IDRT
adjunct to standard care and standard care alone for DM patients with the foot ulcers. The
design of the Markov model is applicable to the standard care and the intervention treatment
alike also reflecting the natural history of the medical condition. The state transition Markov
model was based on previous economic evaluations by Cheng et al (2017) and an earlier
study by Persson et al (2000), US RCT (Driver et al, 2015) and Norwegian national
guidelines to inform the clinical practice. Further developments were made to the original
model by adding two tunnel states to ensure an accurate account of costs. Tunnel states
represent one-off procedural costs, the transition from having an infected ulcer to either post
minor surgery or major surgery state. Moreover, patients underwent either a minor or major
surgery prior to entering post amputation states that indicate ongoing outpatient costs of care.
Markov state transition model is presented in Figure 1.
Markov state model 4.3
DM patients with a chronic foot ulcer represent a complex decision problem and it entails an
ongoing risk of recurring important clinical events. Hence, the consequences and timing of
clinical events are critical albeit can be easily illustrated in a Markov state transition model as
opposed to a conventional decision tree. Thus, modelling the chronic DFU condition allowed
accounting for complexity of the existing clinical pathway and treatment options. A cohort of
patients is assumed to transit along the Markov states, known as mutually exclusive health
states at discrete time periods called ‘cycles’. In this case, the health states depict prominent
clinical and economic effects presented as a set of transitions between the states for patients
with DFUs. Moreover, the proportion of cohort at the start of the cycle is multiplied by an
appropriate transition probability in order to compute the proportion of patients starting in
other Markov states. Based on proportion of the cohort in each state and cycle, costs and
utilities are calculated, provided that each state has an assigned value of cost and utility.
27
Calculation of ulcer free months is a sum of proportion of ulcer free cohort across the cycles.
Notwithstanding, this approach lacks the memory regarding previous transitions in the model
due to the finite number of states. Therefore, additional health states were added to indicate a
history of complication or other important event – an extension of the “memory” (Briggs et al,
2011).
Furthermore, in accordance with the literature and a natural progression of disease, the length
of the cycle is one month. One month cycle provides a flexible representation of patient
transitions since patients can heal an ulcer in one month or may contract an infection when
moving from state of uncomplicated DFU. An even shorter cycle length, for instance a week,
would be more accurate; however it is not possible in this case due to the lack of information
(Roberts et al, 2012). Patients can only occupy one health state per cycle.
Prior to the simulation, a hypothetical cohort of 1,000 DM patients with non-healing
neuropathic DFUs aged 50-65 entered the Markov model in “DFU” health state with IDRT
adjunct to the standard care and same for the standard care only. “No DFU” state indicates
that the ulcer has completely healed and requires no further treatment. Transition to the “No
DFU” state can only be made in the absence of infection through the “Uninfected Ulcer” state
back to the “DFU” state. After the ulcer is healed it may remain in the same state or recur. A
recurred ulcer has been shown to have a higher probability of an amputation; however, for
simplification reasons this Markov model omitted a separate state representing a recurred
ulcer. The literature shows some consensus on patients with recurred ulcer to be more likely
to undergo an amputation. Furthermore, it was assumed that an “Infected Ulcer” state
represented a complicated DFU from where patients could transit into either “Minor or Major
Surgery” state. Previously, Persson et al (2000) included a “Gangrene” state in addition to the
infected ulcer to indicate that only patients in those health states can receive an amputation. In
general, gangrene could be thought as a form of infection depending on the type of gangrene
and it indicates the death of tissue due to poor blood supply. As for this model, the challenge
was to keep the model simple given the absence of data and this determined the decision to
include only one state of infection.
After the “Minor Surgery” the patients move to the “Post Minor Amputation” and either
remain there or contract an infection by moving to the “Infected Post Minor Amputation”
state. If healing of an ulcer is achieved after the amputation, the patients will return back to
the “Post Minor Amputation” or “Post Major Amputation” state if they underwent a major
28
surgery. Thus, the treatment is most successful for patients in “No DFU”; however, “Post
Minor Amputation” and “Post Major Amputation” states also represent healed health states. A
patient that entered the “Infected Post Minor Amputation” state may return to the “Post Minor
Amputation”, remain or transit from minor amputation to the “Major Surgery”. Patients can
go to “Death” state from any of the health states, besides “Minor Surgery” and “Major
Surgery”. “Death” health state is absorbing and, hence patients cannot return from this state.
The cost-effectiveness analysis of the DFU and sensitivity analyses were conducted with the
Microsoft Excel 2010 package. Moreover, PSA and EVP(P)I simulations were based on
macros written in the Visual Basic for Applications (VBA).
Figure 1. Markov state transition model of Diabetic Foot Ulcers comparing standard care versus the IDRT
technology along the standard care treatments. Squares represent the two tunnel states, they reflect the need to
account for one-off procedural costs.
Patients start in the “DFU” health state which stands for diabetic foot ulcer.
29
Key assumptions 4.4
This cost-utility study of the diabetic foot ulcer treatment with the IDRT along standard care
intervention draws on assumptions in relation to the structure of the model and model inputs
for the Markov state transition model.
Structural limitations of Markov model assumptions
Markov models are harnessed to enable better decision making and hence it represents reality
in a simplistic way. Due to that assumptions were made to the structure of the model in
association with complexity of disease and treatments.
A DFU patient will be in one health state per cycle;
A DFU patient will transit to another health state once per cycle;
The probability of progressing further or dying is irrespective from the time spent in a
cycle;
A patient with DFU can only transit from infected ulcer state to either minor or major
surgery state;
Infection complication can only occur once per cycle;
In the pathology of lower extremity (LE) ulcers gangrene state usually results in
amputation albeit it was omitted. Instead, an infected ulcer state reflected all of DFU
patients that had an increased risk of an amputation;
Minor surgery event was assumed to be non-recurrent, however, a DFU patient post
minor amputation can transit to infected state and from there one can receive a major
surgery;
A DFU patient that achieves healing via major amputation is assumed to be at no risk
of ulcer recurrence;
Post minor/major amputation states were included to explicitly account for the long-
term costs associated with the pathology of undergoing an amputation;
Minor surgery and major surgery are modelled as a treatment promoting healing and
not as health states;
Mortality probabilities were assumed the same for all ulcer states except “Post minor
amputation” and “Post major amputation”;
30
RCT study by Driver et al (2015) reported that 1 application of IDRM technology was
sufficient to complete ulcer healing. In this model, 4 applications of IDRM technology
were assumed to be necessary to achieve healing;
PSA distribution for transitional probabilities
Transition probabilities and utilities were gathered from secondary evidence sources,
particularly from Flack et al (2008) cost-effectiveness study on DFUs in the US. Besides
“Post major amputation” state the rest of health states are multinomial thereby indicating that
Dirichlet distribution is the most appropriate choice for the PSA. However, Flack et al (2008)
populated their model using transition probabilities from various published sources including
the two US RCTs Apligraf (Novartis) and Dermagraft (Smith & Nephew). Due to that, this
CUA applied beta distributions on all transition probabilities because the assumption that
patients came from the same population is relatively strong. In addition, alpha and beta values
were computed adopting a 20% standard deviation assumption on all baseline probabilities,
except for probability of remaining in the “No DFU” state – 15% standard deviation.
31
5 Input and material
Parameter list 5.1
The model was populated with input from diverse sources from different systematic searches.
The latter were conducted separately for clinical effectiveness parameters and for existing
economic evaluations in PubMed, Oria and Google Scholar databases with no filters on
publishing date, language or other. Moreover, the search strategy comprised of the following
keywords: “diabetic foot” “foot ulcer” and “randomized controlled trial”, “Markov model”,
“cost-effectiveness” and combined with “standard wound care”, “standard care”, “ulcer
treatment”, “collagen dressings”, “biological skin substitutes”. Relevant studies were selected
in a two-step procedure. First, the titles of the articles were scanned to evaluate their
relevance. Second, abstracts of the chosen studies were screened and downloaded if they
seemed to be feasible for analysis. Economic evaluation studies containing transition
probabilities, costs and utilities were appraised in terms of their suitability to the Norwegian
setting also addressing publication date and treatment strategies.
A review of CEA studies, meta-analyses and RCTs that studied the effects of standard wound
care in patients with a DFU has helped to determine the choice of a target patient population
for the model. Therefore, cost-effectiveness of two DFU treatments was evaluated on
individuals with a full thickness neuropathic, non-healing DFUs with no previous history of
amputations and comorbidity free. Previous studies examined adult patients between 18 and
85 years old. It is common to most of the studies to entail patients with the mean age of 55
years old or mean age of 60 years old. This CUA analysis was based on a cohort of 50-65
year old individuals with a mean age of 57.5.
Transition probabilities 5.2
Baseline transition probabilities were adopted from the published literature which included
cost-utility analyses on DFUs, namely Flack et al (2008) study. All transition probabilities
utilized in this cost-effectiveness model are represented in Table 3. Concretely, the latter
reflect the likely incidences of events for a patient cohort with non-healing neuropathic DFUs
between 50-65 years old. Due to limited quality studies in the area of DFUs and also with
constricted knowledge about incidence rates of infections and amputations in DM population
32
in Norway, the current data from Flack et al (2008) is the best resource available before more
specific data becomes accessible. Moreover, transition probabilities are specific to the bio-
engineered skin substitute technology, Apligraf and Dermagraft and also the standard wound
dressings. Since these skin substitutes are on the most advanced spectrum of dressings, it was
assumed that it is comparable to the IDRT technology. If data on particular transitions was
unavailable, a conservative approach was adopted. For illustration, data on minor and major
amputations were unavailable, thus the following transitions were synthesized based on
previously used methods. In particular, the overall transition probability for an amputation
was split into minor and major by subtracting the difference between these states provided in
the Cheng et al (2017) study. Furthermore, transitions from “DFU” to “No DFU” and from
“DFU” to “Infected DFU” were unique to the IDRT technology because the specific relative
risk was derived from a dedicated RCT by Driver et al (2015) and calculated for the healing
and infection rates with the intervention. Therefore, the current set of transition probabilities
in Flack et al (2008) were further adapted to this CUA.
The care pathway of treating DFUs in terms of health states was identical to both standard
care and the IDRT technology along standard care treatments. Since all transition
probabilities were presented as monthly probabilities these readily fit the Markov model
monthly cycle length, thus no conversion was necessary. The probability is defined as the
likelihood of occurrence of an event over a given time period and it is on the interval between
zero and one. Moreover, Drummond et al (2005) suggested that probabilities with different
time periods can be converted by re-computing the rate which is constant over time and then
using it to recalculate the time appropriate probability.
33
Table 3. Transition probabilities representing movement between DFU health states in the Markov state model
Parameters Transition probabilities
Deterministic value Standard deviation
Distribution
From DFU to DFU 0,84 0,168 Beta
From DFU to No DFU 0,103 0,0206 Beta
From DFU to Infected DFU 0,043 0,0086 Beta
From DFU to Death 0,009 0,0018 Beta
From No DFU to No DFU 0,960 0,144* Beta
From No DFU to DFU 0,031 0,0062 Beta
From No DFU to Death 0,009 0,0018 Beta
From Infected DFU to Infected DFU SC 0,8387 0,1677 Beta
From Infected DFU to DFU SC 0,082 0,0164 Beta
From Infected DFU to Minor surgery SC 0,038 0,0076 Beta
From Infected DFU to Major surgery SC 0,0323 0,0065 Beta
From Infected DFU to Death 0,009 0,0018 Beta
From Infected DFU to Infected DFU IDRT 0,8927 0,1785 Beta
From Infected DFU to DFU IDRT 0,082 0,0164 Beta
From Infected DFU to Minor surgery IDRT 0,011 0,0022 Beta
From Infected DFU to Major surgery IDRT 0,0053 0,0011 Beta
From Minor surgery to Post minor amputation 1,0 N/A N/A
From Major surgery to Post minor amputation 1,0 N/A N/A
From Post minor amputation to Post minor amputation
0,851 0,1702 Beta
From Post minor amputation to Infected post minor amputation
0,029 0,0058 Beta
From Post minor amputation to Death 0,12 0,024 Beta
From Post major amputation to Post major amputation
0,88 0,176
From Post major amputation to Death 0,12 0,024 Beta
From Infected post minor amputation to Infected post minor amputation
0,881 0,1762 Beta
From Infected post minor amputation to Major surgery
0,029 0,0058 Beta
From Infected post minor amputation to Post minor amputation
0,081 0,0162 Beta
From Infected post minor amputation to Death 0,009 0,0018 Beta
34
Utilities 5.3
In order to calculate the health outcomes, this CUA used utility estimates from a previous
published study. Namely the QALY estimates came from Redekop et al (2004) study, they
adopted health states that were used in DFU Markov models by Persson et al (2000) and
Ghatnekar et al (2002). Likewise, a recent CUA by Cheng et al (2017) used six of these health
states in their Markov model. Health outcomes were evaluated by EuroQol EQ-5D instrument
and utilities for health states were measured with the time-trade-off method. Redekop et al
(2004) estimated health utilities of diabetic foot ulcers and amputations from the
recommended societal perspective. Study participants were general public representative of
the Dutch population in terms of age and gender (17-70). It is likely that their perspective
might be different from diabetes patients. For instance, Ragnarson et al (2000) study found
lower health utility scores using EQ-5D tool in 5 sub-groups of patients with either a previous
DFU or present DFU in a Swedish population. Particularly, the scores were much lower for
amputations at foot and leg level compared to the Redekop et al (2004) study. This cost-
effectiveness analysis focuses on a patient population with neuropathic DFUs without other
co-morbidities, thus it was assumed that health utility estimates from Redekop et al (2004)
were more suitable for this problem. Health utilities are presented in Table 4. A 95%
confidence interval was provided for utility probabilities; however standard errors were not
reported and instead the mean value was calculated as 20% of the deterministic value.
Relative Risk Probabilities
Healing probability* 1,4730 0,129 Log normal
Infection probability 0,5290 0,419 Log normal
The main source of transition probabilities from Flack et al (2008), except for relative risk probabilities from Driver et al (2015). SC – standard deviation specific probability, IDRT – Integra Dermal Regeneration Template specific probability; Standard deviation estimated on 20% of the deterministic value; Standard deviation* estimated on 15% of the deterministic value; Transition probabilities equal to 1 or close to 1 were not assigned distributions;
35
Costs 5.4
Estimate costs for some of the Markov health states were calculated using the existing data
from Cheng et al (2017) economic evaluation study and are represented in Table 5.
Specifically, the estimates correspond to the monthly cycles in the model and it was assumed
that it included all the relevant resource use pertaining to the standard wound care. Cheng et
al (2017) presented their costs in Australian dollars in 2013, thus the first step was to convert
the costs to Norwegian kroner using the exchange rate for 2013 year. In the next step, the
2013 costs were inflated to the 2017 year costs. Inflation rates were taken from the EuroStat
website containing statistical information at European level.
Furthermore, cost estimates for the remaining health states were calculated based on
Norwegian DRG codes with values in 2017 year released by the Norwegian Directorate of
Health (Helsedirektoratet, Innsatsstyrt finansiering 2017). The fixed price for treating somatic
diseases was set at 42,753 NOK in 2017 year. In order to calculate the monthly cost of staying
in the “DFU” state, the DRG-809S for the basic ulcers was combined with 3% of the overall
DRG-271 for treating chronic ulcers.
Table 4. Utility weights representing DFU health states
Parameters Utilities
Deterministic value Standard deviation Distribution
Uncomplicated DFU 0.75 0.15 Beta
No DFU 0.84 0.168 Beta
Infected DFU 0.70 0.14 Beta
Post minor amputation 0.68 0.136 Beta
Post major amputation 0.62 0.124 Beta
Infected post minor amputation 0.59 0.118 Beta
Minor surgery* 0.68 0.136 Beta
Major surgery* 0.62 0.124 Beta
Standard deviation estimated on 20% of the deterministic value;
Main source of utilities based on Redekop et al (2004); Minor/major surgery utilities assumed;
36
Table 5. Cost estimates representing DFU health states
Cost parameters for health states
Cost per month NOK
Standard deviation
Distribution
DFU 3,519
704 Gamma
No DFU 0 - Gamma
Infected DFU 105,814
21,163 Gamma
Post minor amputation 11,042
2,208 Gamma
Post major amputation 30,312
6,062 Gamma
Infected post minor amputation 156,182
31,236 Gamma
Cost of DFU, No DFU, Infected DFU, Post minor/major amputation, infected post minor amputation health states were converted from Australian dollars to Norwegian kroner to 2013 prices and inflated to 2017. Cost estimated based on Cheng et al, 2017 source; Standard error was 20% of the mean cost;
Monthly costs of the “DFU” state with the IDRT intervention has been computed slightly
differently. First, the price of IDRT technology was identified in the official handbook of
prices for high cost skin substitutes (Acelity Company, 2017). It was assumed that all high
cost skin substitutes under the 2017 year CPT15271 code had the same price of 1427.77 US
dollars to cover 100cm2 would area. Thus, the estimate was made by converting the total
price of IDRT technology to Norwegian kroner and then dividing it by four to capture the
price per 25cm2. This assumption was based on the Driver et al (2015) RCT because the
study included patients that had ulcers not bigger than the 25cm2 area (1cm2 – 12cm2).
Although the effectiveness of IDRT intervention was achieved with using one application
(Driver et al, 2015) and the manufacturer’s guidelines claim that one application is enough for
3-4 weeks, a conservative assumption was made that at least four applications were required
to achieve healing. In addition to this, a one-off physician fee and a monthly cost for treating
basic ulcers were calculated towards the total estimate of “DFU” state with the intervention.
Likewise, the physician fee in relation to applying IDRT technology in the hospital was found
in the official handbook for physicians and converted to the Norwegian kroner for 2017 year.
The handbook provided separate physician fees for private physician offices and for facilities.
The latter was a lower cost and a preferred one because it reflected the practice in Norway
where the National Health Care System dominates.
37
Moreover, the costs of one-off minor and major amputations were based on the Norwegian
DRG codes for 2017 year (Helsedirektoratet, Innsatsstyrt finansiering 2017). Patients qualify
for a minor surgery if the amputation is performed below the ankle, usually toes. In this case,
to account for lower resource consumption the cost estimate was based on a DRG-1130 code
for outpatient surgery. Previous economic evaluations considered this health state to require
hospitalization meaning that the cost of this state was higher in other countries than in the
Norwegian setting. On the other hand, the major surgery indicated the need for amputation
above the ankle level. Thus, the cost estimation was based on the DRG-113 code for inpatient
surgery which also includes hospitalization. All cost estimates are presented in Table 6.
Table 6. Individual cost for treating “DFU” health state with the IDRT intervention and one–off costs presented
in the table.
Cost parameters Cost per unit Cost per month
SE Distribution Source
One-off minor amputation
77,468
N/A Gamma Innsatsstyrt finansering DRG 2017
One-off major amputation
154,894
N/A Gamma Innsatsstyrt finansering DRG 2017
1 IDRT application 3,082
N/A N/A Derma Sciences 2017 guide
One-off physician fee 855 N/A N/A 2017 physician coding guide
Basic ulcer treatment N/A 1,496
N/A Innsatsstyrt finansering DRG 2017
Chronic ulcer treatment
N/A 2,022
N/A Innsatsstyrt finansering DRG 2017
DFU state with IDRT N/A 14,681
Gamma N/A
One-off minor amputation was based on 2017 DRG1130; One-off major amputation was based on 2017 DRG113; 1 IDRT application covers up to 25cm
2 wound area which was assumed to be enough for modelled ulcers. High cost 2017
CPT15275 code refers to IDRT application that covers 100cm2, thus the total cost was divided by 4. USD price was
converted to Norwegian kroner in 2017; One-off physician fee was based on 2017 CPT15275 for physicians in facility; Cost of treating chronic ulcers was based on 3% of the total 2017 DRG271 of skin chronic ulcers; Cost of DFU state with IDRT intervention estimate included 2 IDRT applications, one-off physician fee and a monthly basic ulcer treatment;
38
6 Results
Cost of treatment 6.1
The total cost per patient with the new intervention, a combination of IDRT application along
the standard care is 20,234 NOK lower than the standard care alone at 3 years’ time horizon
(Table 7). Table 7 also indicated the total costs of two alternative treatments at one year time
point with results expressed in undiscounted and discounted costs. Discounted cost for the
standard care at 1 year point is 118,865 NOK and 3 years point is 206,652 NOK. Discounted
cost for IDRT intervention with standard care at 1 year point is 118,274 NOK and at 3 years
point is 186,418 NOK.
Table 7. Total direct hospital cost of Standard Care and IDRT along standard care per person are presented
at 12 months and 3 years. All costs expressed in NOK.
Duration of
treatment
Standard Care
IDRT + Standard Care
Undiscounted costs Discounted costs Undiscounted costs Discounted costs
1 year 152,210
118,865
145,644
118,274
3 years 355,919 206,652 305,942 186,418
Cost – effectiveness threshold 6.2
A WTP threshold can be set by evaluating the severity of disease. Thus, the usual practice
suggests that the severity, for example for a diabetic foot ulcer condition, is determined by
disutility per year for the remaining life expectancy of a patient cohort aged 50-65 with a
neuropathic DFU that did not heal within 6 weeks. Hence, using this information an absolute
shortfall estimate has been generated to address the future healthy life years to be lost due to
the low quality of life at the present health state. Thus, the severity of disease can be
determined by assessing the quantity of QALYs lost, the greater the loss the higher the
severity of disease. Therefore, a brief evaluation of an absolute shortfall has been made by
estimating, the level of severity given the utilities of DFU health state and life years lost due
to this condition.
An estimated average life expectancy for people without diabetes in Norway is 82.6 years
(Statistisk sentralbyrå). Female’s life expectancy is a little bit higher at 84.28 years compared
39
to male’s life expectancy at 80.91 years. According to the Livingstone et al (2015) men with
diabetes lose 11.1 years whereas women’s estimated loss in life expectancy is 12.9 years
when evaluated from the age of 20. As mentioned before in the methods section, Redekop et
al (2004) assessed the utility of being diabetic equals to 0.84 QALYs and for a patient with
diabetes to have a foot ulcer equals to 0.75 QALYs. Moreover, complications from having a
DFU reflect the decreasing utilities and can be found in the Table 2 in Section 4 Input and
Materials. Provided this, it may be assumed that patients with a DFU experience at least a
moderate level of severity and perhaps even a bit higher. Therefore, patients with DFU can be
assigned to the third or fourth severity group which reflects the WTP threshold between
495,000 NOK and 605,000 NOK (Magnussen group, 2015). Given this information, the
chosen WTP threshold is 550,000 NOK per QALY.
Cost effectiveness analysis 6.3
The results from deterministic cost-effectiveness analysis are summarized in Table 8. It
illustrates the overall costs and QALYs obtained for the new intervention IDRT along the
standard care from the Norwegian health care provider’s perspective. In comparison with the
standard care alone, the calculations indicated a negative incremental cost of -20,235 NOK
and a positive incremental QALY of 0.737. Total QALYs for standard care is equal to 12.86,
whereas with IDRT intervention is 13.60. Moreover, the ICER is -27,441 NOK per QALY
which is lower than the set WTP threshold of 550,000 NOK from the health care provider’s
perspective.
Table 8. Cost-effectiveness results for a cohort of patients with chronic DFUs from health care provider’s
perspective which includes only the direct medical costs. Discounted at 4% per annum for a time horizon of three
years.
Treatment Total costs Total
QALY
Incremental total
cost (ΔTotalCost)
Incremental
QALY (ΔQALY)
ICER
(ΔTotalCost/ΔQALY)
Standard care
only
206652 12.86 N/A N/A N/A
IDRT +
Standard care
186418 13.60 -20235 0.737 -27441
40
Secondary outcomes 6.4
A DFU free month is a clinically significant outcome; therefore, the time spent in a healed
state was quantified per year and documented in Table 9. At 1 year point, time spent in the
ulcer-free state with standard care is 4.07 months compared to the 5.53 months with IDRT
intervention. In total for a 3 year time horizon, individuals who received the IDRT
intervention spent more time in a healed state (IDRT = 20.16 months; Standard care = 15.45
months) than those who were treated with the standard care only. The biggest difference is
observed for the second year where patients in the intervention arm spent 7.59 months
compared to 5.92 months in the standard care arm. Interestingly, the number of months spent
in both standard care and IDRT intervention slightly decreases in the third year compared to
the second year (SC – 5.92 vs 5.46; IDRT – 7.59 vs 7.04).
The probability of having a healed ulcer at 1 year for standard care and IDRT intervention are
0.4852 and 0.6326 respectively. Similarly, at 3 years point the probability of healing is 0.4255
for the standard care and 0.5569 for the IDRT intervention. In addition to this, the probability
of avoiding an infection for standard care (0.9056) is lower than with IDRT intervention
(0.9401). Notably, more amputation avoided was with IDRT intervention given the
probability of avoiding an amputation of 0.9935 versus 0.9517 with the standard care alone
(Table 10). Hence, the percentage of people with amputations at 12 months point for IDRT
compared to standard care is 0.65% vs 4.83%; percentage with infections is 5.99% vs 9.44%.
Table 9. Number of months spent in a healed state (“No DFU”) during 1st, 2nd, 3rd year and a total number
of months for 3 years presented for Standard Care only and IDRT + Standard care treatments.
Duration of
treatment
Standard Care IDRT + Standard Care Increment
Time spent in the DFU free health state (months)
1st year 4.07 5.53 1.46
2nd year 5.92 7.59 1.67
3rd year 5.46 7.04 1,58
Total for 3 years 15.45 20.16 4.71
Months presented in the table were adjusted to continuity and undiscounted.
41
Deterministic sensitivity analysis 6.5
One-way sensitivity analyses revealed five key parameters that had a great impact on the
ICER. These are the following: the monthly cost of treating ulcers with the standard care
alone and IDRT intervention, the cost of treating the infected ulcer, utility of “No DFU” state
and the transition probability from “DFU” to “Infected DFU” state. In fact, the ICER value
ranges from -118,980 NOK to 76,942 NOK. The results of one-way sensitivity analyses are
presented in Figure 2.
Some parameters with low values reduce the value of the ICER, whereas other times, low
parameter values influence the ICER to increase. Lower monthly cost estimates of a state with
IDRT intervention yielded higher ICER values. Meanwhile, higher cost estimates of a
monthly state with standard care alone showed lower ICER values. Yet lower costs of treating
an “Infected DFU” state yielded lower ICER values albeit for higher parameter values ICER
values increased accordingly. Finally, higher transition probability values of transitioning
from “DFU” to “Infected DFU” state indicated lower values of the ICER. Relative risk of
infection and healing rate as well as transition probability from “DFU” to “No DFU” state had
a moderate effect on the ICER values ranging from -60,149 to 9,341 NOK.
Table 10. Expected outcomes at 1 year and 3 years after the start of standard care alone and IDRT combined
with standard care.
Treatment Probability of having
a healed ulcer at 1
year
Probability of having
a healed ulcer at 3
years
Probability of avoiding
an infection
Probability of avoiding
an amputation
Standard Care 0.4852 0.4255 0.9056 0.9517
IDRT +
Standard Care 0.6326 0.5569 0.9401 0.9935
42
Figure 2. The Tornado plot represents the results of one-way sensitivity analyses for different parameters.
Probabilistic sensitivity analysis 6.6
Findings of the probabilistic sensitivity analysis are presented using a graphical method, a
cost-effectiveness plane in Fig. 3. This is a standard way of illustrating information and the
surrounding uncertainty around the decision. As described in the Section 3 Methods, the
differences in effect and cost between the IDRT adjunct to standard care and standard care
alone, 1,000 ICERs were plotted from the simulation. After the simulation, the new
intervention appeared to be cost-effective 92.9 % of the time whilst the standard care was
cost-effective 7.1 % of the time.
The ICERs on the CE plane are distributed across all four quadrants, with majority of ICERs
observed in the south-east and north-east quadrants. There are more ICERs on the south-east
quadrant indicating that the IDRT intervention is a dominant strategy, less costly and provides
more health gain (Fig. 3). A small fraction of the ICERs have landed in the south-west
quadrant, illustrating that the IDRT intervention is less costly and contributes no health gain.
Likewise, some ICERs can be found in the dominated north-west quadrant, where the cost is
high and no health gain is obtained. Therefore, given the distribution of the ICERs from 1,000
Post Major amputation utility
Post Minor amputation utility
Post Infected amputation utility
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ICER values
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ICER high values
ICER low values
43
probabilistic sensitivity simulations, it can be concluded that the IDRT intervention is cost-
effective 92.9% of the time for WTP threshold of 550,000 NOK (Fig. 3).
Figure 3. Cost-effectiveness plane.
Cost acceptability curve 6.7
The results of the PSA were utilized in the NMB analysis and plotted on the cost-
effectiveness acceptability curve. Figure 4 illustrates the likelihood of the standard care and
IDRT along standard care treatments being cost-effective given the value of the willingness-
to-pay thresholds on the horizontal axis. The IDRT intervention is the dominant treatment
strategy for any given WTP threshold (0 NOK <= WTP>1,200,000 NOK). Specifically, for 0
<=WTP = 100,000 NOK, the probability of IDRT intervention gradually increases from its
lowest point 67.1% to 88.3% at the WTP of 50,000 NOK and finally rising to 92.6% at the
WTP of 100,000 NOK. For WTP threshold values higher than 550,000 NOK, the probability
of IDRT being a cost-effective option slightly decreases from 92.9% to 92.6% at WTP value
of 1,000,000 NOK. In contrary, the probability of the standard care alone being cost-effective
reaches the maximum of 7.4% at WTP 1,000,000 NOK. The CEAC does not cut the y axis
because some of the ICERs involve cost-savings (67.1%) and it does not asymptote to 1
because bot all ICERs include health effects (92.9%).
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ICERs
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44
Figure 4. Cost-effectiveness acceptability curves.
Figure 5. The most cost-effective option was also represented on the cost-acceptability frontier.
The expected value of perfect information 6.8
The EVPI curve is represented in Fig. 6 to demonstrate the level of decision uncertainty
between the standard care and the IDRT intervention options. The EVPI value at 0 WTP
threshold is 8,339 NOK per patient, then it decreases to a minimum of 2,642 NOK at WTP of
100,000 NOK. From this point onwards, the EVPI value steadily increases to infinity. At the
chosen WTP threshold of 550,000 NOK the expected value of perfect information is 11,488
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45
NOK per individual (Fig. 6). There are a couple of reasons to explain the increasing EVPI
curve, please see Section 7.1.
Figure 6. The expected value of perfect information for IDRT intervention in patients with neuropathic diabetic
foot ulcer for age group 50-65 years old.
Expected value of perfect information for 6.9
population
The expected value of perfect information for population was conducted to ascertain the
maximum value of further research for Norwegian society (Fig. 7). Moreover, the population
EVPI reflects the costs of the target population for which the new treatment is considered.
Therefore, in order to make an evaluation, an effective population was computed to represent
all the individuals that would gain an advantage from supplementary information given the
duration of technology. It should be noted that an estimate for Norwegian people of 50-65
years old with neuropathic foot ulcers in not available in the literature. Thus, the calculation
of an effective population was performed to the best of available knowledge from different
resources. Specifically, the Norwegian Diabetes registry for adults estimated that 248,894
Norwegians have diabetes (4.7% of the population) then this number was multiplied by
percentage of prevalence among 50-65 year olds is 21.59% (IDF diabetes atlas, 7th). Also it
was important to account how many of these people already have neuropathic diabetic ulcers
hence to account for patients with ulcers the prevalence of 4% was applied. Finally, the
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Expected Value of Perfect Information
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46
percentage of neuropathic ulcers is approximately 65-75%, thus the number of individuals
with ulcers in age group 50-65 was multiplied by 70% prevalence of neuropathic ulcers. The
effective population is 14,021 people over 10 lifetime of IDRT technology. This might be a
crude number, however, until a better estimate is provided this should suffice for this
population of EVPI analysis. As expected the population EVPI for WTP threshold of 550,000
NOK given the uncertainty takes a high value of 161,072,480 NOK per QALY.
Figure 7. The expected value of perfect information for population.
Expected value of perfect information for 6.10
parameters
It was imperative to investigate the impact of parameters on decision uncertainty. Thus,
parameters were grouped in categories in accordance with their special characteristics. In
particular, utility values were in one category, costs of Markov states in the second category
and transition probabilities in the third category. Moreover, it was of particular interest to
assess the effectiveness of the intervention parameter; hence, it was included as a single
parameter. Interestingly, only the group of utilities demonstrated value for further research.
An investment in getting supplementary information about utilities for different ulcer states
indicated a high value of 2.2 billion NOK per QALY for the effective population calculated
for the population EVPI (Fig. 8). In contrary, there was no indication of value for a single
effectiveness parameter and neither for other parameter groups.
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47
Figure 8. Population expected value of perfect information for groups of parameters. The population EVPPI is
expressed in monetary terms, millions of NOK for WTP threshold of 550,000 NOK.
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48
7 Discussion
This is the first cost-utility analysis contributing to the cost-effectiveness field of diabetic foot
ulcers (DFU) treatment with Integra Dermal Regeneration Template® adjunct to standard care
and compared to the conventional treatment in patients with non-healing neuropathic DFUs.
The objective of this CUA is to aid decision making under uncertainty by providing economic
and health outcomes evidence with regards to treatment of DFUs in Norway.
Main findings 7.1
Integra Dermal Regeneration Template® adjunct to standard care was concluded to be cost-
effective over standard care alone from the health care provider’s perspective. Simulated
ICERs fell below the set WTP threshold of 550,000 NOK per QALY. Results suggest that for
patients with non-healing neuropathic DFUs, implementing IDRT in combination with
standard care which adheres to the national guidelines yields improved health benefits and
cost savings for the health care system. IDRT intervention was less costly compared to the
standard care alone by 20,235 NOK over three years and yielded an additional QALY gain of
0.737. The health gain for IDRT intervention is 13.60 QALYs in contrast to 12.86 QALYs for
the standard care. The respective ICER is -27,441 NOK per QALY gained. Thus, this result is
cost-effective compared to the standard care treatment for a WTP threshold of 550,000 NOK
per QALY in the Norwegian context.
The difference in costs between the treatment options was insignificant at 1 year, with IDRT
intervention costing less by 591 NOK only. CUA findings on differences in costs at 1 and 3
years points at least hint that the break-even point occurred after 12 months, provided that the
intervention was assumed to be approximately four times more costly than the conventional
treatment.
Uncertainty surrounding the decision from this CUA has been addressed by performing one-
way analyses, probabilistic sensitivity analysis as well as the computation of the EVPI and
EVPPI. One-way sensitivity analyses showed that ICERs ranged between -118,980 and
76,942; however it did not affect the decision. PSA demonstrated that the probability of IDRT
being cost-effective is always higher than the conventional wound care regardless of the WTP
threshold. At the willingness-to-pay threshold of 550,000 NOK per QALY, the IDRT
49
intervention was 92.9% cost-effective. Despite some variance in the ICER values, findings
from one-way sensitivity analyses also confirmed that the decision was robust.
Furthermore, in an attempt to abolish doubts whether IDRT intervention can be reimbursed
with the existing evidence or additional research is needed to support this decision in the
future, EVPI and EVPPI were calculated. The EVPI established the value of supplementary
information at WTP threshold of 550,000 NOK at 11,488 NOK per individual. After the
effective population of patients with neuropathic DFUs for 50-65 year olds was evaluated in a
Norwegian context, the population EVPI resulted in a high value of 161 million NOK
(161,072,480 NOK) at WTP threshold of 550,000 NOK.. Significantly, the population EVPI
indicated a great value of further research for a relatively small population of 14,021 people.
The EVPPI addressed that further research would be most valuable for utility parameters at
2.2 billion NOK for the relative Norwegian population or 164,858 NOK per person.
To explain the results of EVPI, it is imperative to stress that the EVPI curve is based on the
balance between the probability of acquiring an opportunity loss and the magnitude of the
opportunity loss that is the result of making an incorrect decision (Fenwick et al, 2004;
Oostenbrink et al, 2008). Initially the EVPI curve fell as the value of WTP threshold
increased because the probability of gaining an opportunity loss was lower compared to the
magnitude of the opportunity loss per se. An increase in probability from 67% to 88%
demonstrated that the IDRT technology had the highest net monetary benefit as the WTP
threshold values increased. Therefore, the EVPI was lower due to the reduced value of
opportunity loss. However, at higher values of WTP threshold, the decline in opportunity loss
counterbalanced the relatively small increase in probability of acquiring an opportunity loss
(Oostenbrink et al, 2008).
In addition to the first argument, the CEAC is not rigidly an increasing function of WTP
threshold (Fenwick et al, 2004) because of the joint density of effects and costs in the NE and
the SW quadrants. In other words, the joint distribution of ICERs presupposes that the trade-
off between the effects and costs is greater in the SW than in the NE quadrant. Therefore, the
CEAC increases prior to falling due to the joint density in the NE being counted as cost-
effective before the effects and costs in SW quadrant are suspended as not cost-effective. Due
to the higher value of forgone effects this could have influenced the EVPI curve to rise
regardless of the WTP threshold.
50
Overall, a hypothetical cohort of 1,000 patients with neuropathic diabetic foot ulcers aged
between 50-65 years old healed faster with the IDRT intervention by 30.38% and therefore
spent more time in the healed ulcer state by 35.87%. In addition, patients treated with IDRT
intervention underwent fewer amputations by 4.4% and avoided 3.8% infections than patients
in the conventional wound care. Hence, this explains the higher costs with the standard care
treatment compared to the IDRT treatment. The more time is spent in the healed state, the less
costly the treatment strategy proves to be. This is an important finding for medical
professionals when choosing between alternative treatments for patients with DFUs. Due to
the chronic nature of this condition, the patients are exposed to a high likelihood of a
recurrent infection, if not properly healed it can lead to more serious complications such as
minor or major amputation. The percentage of a hypothetical cohort with recurred ulcers is
unclear because the recurred ulcer was not implemented in the model as a separate health
state. An extended DFU model is needed to enhance further understanding and should be
populated once the patient specific data in Norway becomes available.
Comparison to previous research 7.2
Previous research of IDRT intervention treatment in DM related foot management has not
been carried out, thus this is the first CUA evaluating the cost-effectiveness of this
technology. Much of the literature that focuses on addressing the economic and clinical
aspects of DFU condition primarily has been conducted using other types of bio-engineered
skin matrixes and substitutes. Due to the lack of cost-effectiveness studies with the same
intervention, merely an indirect comparison of studies is appropriate. Also it is important to
note that there exists a great variety of cost-effectiveness outcomes among CEAs in patients
with DFUs. Nevertheless, the foremost trend from the literature indicates that the use of skin
substitute adjunct to standard care proves to be cost-saving and even cost-effective compared
to the standard care alone. Even though the skin substitutes are expensive, the cost-
effectiveness models show that the costs can be offset by their potential to increase the
number of ulcer-free months, probability of healing or enhanced probabilities of avoiding an
infection and amputation. Therefore, the findings of the current CUA confirm previous
findings in the literature.
According to the RCTs comparing the different types of skin substitutes to standard care,
regardless of the type of substitute, the intervention was always found to be more effective. In
51
particular, effectiveness ranged from 30% - 62% for skin substitutes and 18.3% - 38% for
standard care, with Grafix dressing indicating the highest effectiveness (62%) and Dermagraft
demonstrating the lowest effectiveness (30%), whereas IDRT was found to be 51% effective.
A detailed review of the most relevant cost-effectiveness studies is outlined below to support
the results in the bigger context and to stress the heterogeneity of outcomes reported.
The values of cost-effectiveness from this CUA are barely distinguishable from Guest et al
(2017). Guest et al (2017) utilized patient level data based on Cazell et al (2015) RCT in the
US and examined the cost-effectiveness of the porcine small intestine submucosa combined
with the standard care (SIS; Oasis Ultra). SIS was found to improve the probability of healing
for new DFUs and reduce the cost per patient by 100 US dollars at 2016 values over 12
months period. Specifically, SIS intervention compared to the standard care led to higher
number of ulcer-free months by 42%; similarly IDRT intervention yielded a difference of
36%. It also demonstrated an increased probability of healing by 32% whereas probability of
healing with IDRT was slightly lower 30.4%. Moreover, the probability of transiting from an
ulcer state to infection was reduced by 2.5% and the probability of amputation was reduced
by 1%. The IDRT intervention showed slightly higher probability of avoiding an infection by
3.8% and probability of avoiding an amputation by 4.4% compared to the standard care only.
On the other hand, this study did not report the ICER nor the QALYs, thus, this could
possibly have underestimated the cost-effectiveness of SIS intervention.
Ghatnekar et al (2002) utilized the findings of a US based RCT to accustom to an existing
Markov model to assess the cost-effectiveness of Promogan adjunct to standard care treatment
in the UK, Switzerland, Germany and France for non-superficial DFUs. The Markov model
was populated for 1 year horizon and demonstrated cost-savings in all four countries. What’s
more, it was found that at three months point 26% of DFUs healed with Promogran compared
to 20.7% of ulcers with the standard care alone. The total cost of treatment with Promogan
and standard care per year ranged from 8,172 euros to 16,191 euros across the four countries
(1999 year values). On the other hand, the total costs with the standard care were between
8,455 euros to 17,270 euros. Unfortunately, Ghatnekar et al (2002) did not report the cost per
QALY. Number of months spent in the ulcer free state was higher with Promogran (3.75)
compared to the standard care (3.41) at 12 months. Conversely, patients with DFUs treated
with IDRT intervention spent 5.53 months in an ulcer free state whilst with the standard care
4.07 months over 1 year period. Therefore, the effectiveness of improvement in time spent in
52
ulcer-free state with IDRT intervention was raised by 35.87% compared to 9.9% with
Promogran intervention.
Notably, the reported outcomes also favorably correlated with Redekop et al (2003) and
further strengthened the position of cost-effectiveness of skin substitutes. Redekop et al
(2003) investigated the economic impact and the cost-effectiveness of standard care versus
Apligraf skin substitute along the standard care for the treatment of DFUs. Their findings
agree with the results in the present CUA, where a 1 year Markov model showed lower costs
with the Apligraf plus standard care compared to the standard care only, 4,656 euros (38,541
NOK) and 5,310 euros (43,955 NOK) respectively at 1999 values. Moreover, the evidence we
found for patients treated with IDRT intervention was much lower compared to Redekop et al
(2003). For illustration, Redekop et al (2003) indicated a much higher improvement in
avoided infections with the Apligraf by 67% and avoided amputation by 63% versus IDRT
3.8% and 4.4% respectively. Then again, patients treated with Apligraf versus the standard
care stayed 24% more in the ulcer-free state, whilst with IDRT intervention individuals were
ulcer-free 36% more compared to standard care alone. What’s more, the percentage of
amputations was lower in this CUA both for standard care and for the IDRT intervention
4.83% vs 0.65%, respectively. Finally, the current CUA and the Redekop et al (2003) study
demonstrated relatively low incremental costs between the intervention and the standard care
at 1 year point.
Most recently Guest et al (2018) assessed the likelihood of cost-effectiveness of collagen
based wound dressings compared to the standard care in the UK setting over four months.
Thus, the effectiveness of collagen based dressings was estimated and pooled from five RCTs
including Promogran, Apligraf and IDRT technology. Due to the short horizon time, the
transitions to different health states were not followed adequately. The analysis was based on
130 patients with DFUs that were treated in a clinical practice in the UK. Hence, the cohort of
patients was heterogeneous compared to the recruited patients in the US based RCTs which
could imply bias in results; whether the effectiveness of collagen –based dressings is the same
for the UK cohort. Regardless of the limitations, provided that the healing rate with the
intervention is equal or more than 0.20, it can be concluded to be a cost-effective option from
the NHS perspective.
Allenet et al (2000) evaluated the cost-effectiveness of Dermagraft skin substitute with the
standard care in the treatment of DFUs in France. ICER for Dermagraft treatment was 38,784
53
franks (in 1999 year values) meaning that an additional cost is required per one more ulcer
healed. On the other hand, the current CUA revealed that the cost of DFU management with
IDRT intervention is less costly and has more benefits.
Flack et al (2008) compared the cost-effectiveness of Vacuum Assisted Closure (VAC)
therapy with the traditional wound dressings and advanced wound dressings for the DFUs in
the US. A Markov model simulated the outcomes of the CUA over one year horizon.
Although this study did not directly compare the traditional dressings with the skin
substitutes, the total cost per year for the standard care and advanced wound dressings were
available. Specifically, the total cost per 1 year for advanced wound dressings was of 61,757
US dollars (398,969 NOK) versus 118,274 NOK in this study. The total cost of standard care
per 1 year was 79,951 US dollars /502,636 NOK) compared to 118,865 NOK in the current
analysis. In order to explain a great difference in costs it is important to note that the monthly
cost estimates were much higher in Flack et al (2008). It was evaluated that the monthly cost
of treating the “DFU” state with a skin substitute was 3,718 US dollars, whereas the standard
care accounted for 7,210 US dollars. The latter included home care cost per month, the cost of
standard wound dressing and the nurse time involved in the dressing change. Their analysis
considered only the direct medical costs from the US payer perspective either the National
Health Service or insurer. Similarly, this CUA evaluated costs from the health care provider’s
perspective in Norway.
In sum, the aforementioned CEA studies were censored at 1 year. Thus, the long-term costs
associated with the post-amputation states were not included in the resources such as home
care or management of healed ulcer with an amputation in the nursing facility.
Strengths 7.3
In line with an aim of the current cost-utility study, this analysis has contributed to the wider
knowledge of cost-effective treatments for patients with non-healing neuropathic DFUs in
Scandinavia and specifically in Norway. Second, this is the first cost-utility examination of
IDRT technology for an indication of DFUs notwithstanding its proven efficacy in treatment
of burn wounds for the last few decades. Third, there are no existing cost-effectiveness
analyses (CEA) of DFU management in the Norwegian setting. Therefore, with this analysis,
I believe to suggest an innovative solution to the Norwegian medical start-ups who have
54
developed technology specifically for DFU treatment or wound treatment in general. A
revised method was used by the recent CEA, specifically a Markov transition model for DFUs
proposed by Cheng et al (2017). The latter method was adapted to the Norwegian context by
utilizing the best available published data. When more specific data on DFUs or other types of
ulcers becomes available, the model can be readily populated to examine the cost-
effectiveness of different interventions in the field of ulcers. Finally, the management of
chronic wounds is a concern area in Norway. The parliament has released a suggestion in
2017 with a goal to reduce the number of individuals with chronic wounds and related
amputations (Micaelsen et al, 2017). Hence, this CUA analysis could be of great value to the
health care providers trying to achieve optimal care within the current budget and also to
some extent is applicable to the health policy makers.
Limitations 7.4
With the means of PSA, the parameter uncertainty has been examined and outlined in the
Section 6.6; however, this does not factor in other types of uncertainty. From the beginning of
Markov model conceptualization and throughout the entire design process, certain
assumptions were made due to the lack of data specific to the problem as well as the need for
simplification of the model. As a consequence, the model was exposed to uncertainty and
limitations which will be discussed in this chapter.
First, the three key factors of face validity are deliberated including structure, evidence and
problem formulation. Since this CUA was not reviewed by the panel of experts, this
component has not been discussed. According to the systematic review by Netten (2006), the
Persson et al (2000) Markov model is as a comprehensive description of the natural history of
patients with DFUs. That said Cheng et al (2017) adapted the basic structure of the model for
simulation of neuropathic DFUs in Australia with further extensions by adding minor and
major amputation states. This CUA tailored the more recent DFU model by Cheng et al
(2017) which is believed to describe the natural history of DFUs more accurately in line with
the latest research and expert opinion (Botros et al, 2018). Particularly, Cheng et al model was
extended by adding two tunnel states minor and major surgeries to account for the difference
in costs associated with these surgeries in support of current evidence in the literature (Botros
et al, 2018). Overall, the guidelines of management of DFUs are congruent across western
countries with standard care as described in Section 2.4 being a dominant treatment option.
55
The best resources available were used as input in the model with an intended application to
determine the costs and QALYs for management of patients with non-healing neuropathic
DFUs in Norway using the IDRT technology adjunct to the standard care. With regards to
problem formulation, it was assumed that the chosen time horizon, population, interventions,
outcomes and assumptions coincided with both Norwegian health care providers’ interest as
well as health policy makers (Eddy et al, 2012). Due to these arguments, the face validity of
the structure, evidence and problem formulation of DFU model seems to be trustworthy. Yet
it is advisable to collect specific feedback from the panel of experts for them to consider
whether a more superior model structure could have been ratified.
Internal validity of the model was ensured by incorporating individual checks to eliminate the
possibility of errors in mathematical calculations. In particular, the Markov model integrated
checks to reassure the probabilities sum up to 1 and validation of VBA code to accurately
perform equations as well as maintaining the most current documentation of the code. To
avoid the errors in the code, the programmer explained the code step-by-step to other people
who searched for errors. Nevertheless, the model was not checked by others in depth and
thereby this could have affected the internal validity of the model (Eddy et al, 2012).
Cross validation of the outcomes of the model was not viable due to the lack of CUA studies
analyzing the same research problem. Different DFU models addressed a broad range of
outcomes over 12 months for different populations and comparing distinct interventions.
Moreover, various methods and data sources from previously published models were utilized
whereby decreasing the value of cross validation. That is to say, the results and sources used
to populate the model are incomparable due to dependable use of sources across studies. As
for healthcare modelling, it is a common practice to adapt existing frameworks and to utilize
the same exiting data sources.
The state transition Markov model was built using input from CEAs that were conducted
along the RCTs; therefore this could have compromised this model’s external validity due to
the following arguments. Generalization of results of a pharmaceutical study is more
straightforward than that of a health intervention. On one hand, the standard care comprises of
many various components that are dependent on an initial DFU assessment by clinical
professionals; a well-organized treatment plan may not be well executed by nurses and
professionals as is a common case in a real world. Especially, such remarks on poor
competency in management of DFUs have been highlighted in the Norwegian setting
56
(Micaelsen et al, 2017). Thus, the results might be negatively affected due to the lack of
specialized DFU professionals to provide standard care per se. However, the need for
multidisciplinary teams for patients with DFUs has been identified and some Norwegian
hospitals have successfully established these recommendations in practice. In other words, the
results of this CUA could vary from hospital to hospital and municipality to municipality
conditional on the extent of implemented guidelines for DFUs.
On the other hand, patient compliance is an important issue in clinical practice; thereby
compliance depends on multiple aspects entailing treatment characteristics, health care
system, socioeconomic status, and also factors related to patient characteristics (Silva et al,
2011). Levels of compliance are lower for patients with chronic conditions compared to acute
diseases. Gottlieb (2000) found that compliance decreases with an increasing pharmaceutical
dose. Thus, in case of a health intervention such as management of DFUs that requires multi-
dimensional care it can be inferred that DM patients with DFUs are challenged to be
persistent with the treatment regimen. In fact, a failure to maintain glycosylated hemoglobin
(HbA1c) levels (in combination with other risk factors) in a first place affects DM patients to
develop DFUs (Rubeaan et al, 2015). On the whole, the results of this CUA study might not
be generalizable to other country contexts and DFU patients, especially if health care system
organization is significantly different from the Norwegian national health care system.
Another argument pertains to meticulous patient selection in RCTs that is not customary in
the clinical practice. For instance, recruited patients were thoroughly screened for adequate
blood circulation in the foot and signs of infection among other aspects that may be perceived
as crucial in achieving the best outcomes in DFU management. As mentioned in Section 1.4,
DFU is a challenging condition and a big part of successful treatment depends on the initial
assessment, suggesting that in practice a more heterogeneous group of patients receive
treatment compared to patients enrolled in RCTs. Hence, the findings of this study are less
generalizable also due to the fact that RCTs included individuals from the US population
which might differ from Norwegian population.
An advantage of using data based on previously conducted RCTs, evaluated as the first class
quality evidence, is due to rigorous randomization procedure, meaning that a selection bias is
not an issue.
57
With regards to fragmented DFU care in Norway combined with a complex reimbursement
scheme towards health interventions, the estimation of costs was burdensome. The
reimbursement scheme in Norway is a shared responsibility between hospitals and
municipalities, thus funding depends on the different types of procedures and required care. In
particular, hospitals get reimbursed for treating outpatients and inpatients with DFUs by
HELFO albeit services outside hospitals are funded locally. Moreover, DFU is a complex
chronic disease that may require additional community care that is covered by the
municipality. This analysis estimated costs merely based on Norwegian DRGs, thus only one
fraction of costs were accounted for the provision of standard care. That said, medical costs in
community care has been captured only for post minor/major amputations and infected post
minor amputation states because it was based on the cost estimates used by Cheng et al (2017)
CEA study. It is very likely that some costs have been missed when estimating other health
states. The impact of underestimated costs does not really affect the results; on the contrary, it
would make the IDRT intervention more cost-effective.
Limited data is available on different severity levels of infection, peripheral artery disease
risk, ischemia risk, treatment efficacy with IDRT technology and utilities in patients with
non-healing neuropathic DFUs. Therefore, in order to synthesize input parameters, indirect
links and assumptions were fabricated. Furthermore, Norwegian specific data on incidences
of DFU, infection and amputation defining different age groups and gender were unavailable.
Thus, this CUA adapted transition probabilities based on incidences of aforementioned
clinical events from the CEAs based on US RCT studies to simulate the conditions in Norway
(Marston et al, 2003; Veves et al, 2001). Therefore, this has introduced uncertainty in the
model because the Norwegian population with DFUs might not be comparable to other
populations from various countries. In particular, since patient level data on IDRT technology
was inaccessible (Driver et al, 2015), the most relevant and recent set of transition
probabilities emerged from Flack et al (2008) study. Similarly, utilities were adapted from the
study in Netherlands and evaluated by public aged 18 -70 years old. The actual health-related-
quality-of-life weights were not provided; instead Redekop et al (2003) generated health
utilities to be readily used in DFU cost-effectiveness studies.
Disutility of undiagnosed infection was not considered because it remains a challenge in the
clinical practice and hence information of proportion of undiagnosed individuals is not
reported in the literature. It is known that a DFU infection is one of the leading factors of
58
lower limb amputations (Botros et al, 2018) and undoubtedly enhances the discomfort in
patients as a result of poor DFU management. IDRT technology as many other skin
substitutes have indications to be used only on uninfected patients, thus false negative
diagnoses would lead to the utilization of expensive resources on unintended population. As a
consequence, this might amount to an increase in costs and also unwanted adverse events,
thereby possibly affecting the cost-effectiveness results of the model.
This Markov model is based on the input from Flack et al (2008) that synthesized the
outcomes of the two US RCTs on Apligraf and Dermagraft skin substitutes. Flack et al (2008)
conducted cost-effectiveness study on a population aged 50-65 year old with neuropathic
DFUs, and the same age population was used in this CUA. A CUA study by Cheng et al
(2017) on DFUs in Australian setting found higher cost-savings and higher QALYs for age
group 75+ compared to other age groups (35-54 and 55-74). There was a small difference in
costs between age groups 35-54 and 55-74 albeit with no idiosyncrasy in gained QALYs.
These findings align with the DFU cost-effectiveness study in Sweden that compared the
optimal care against the standard care. Ragnarson Tennvall et al (2000) reported that the
optimal care was more costly for the youngest cohorts albeit reduced costs in a cohort older
than 85 years old. It can be conceivably hypothesized that the current outcomes are subject to
change given the age group, specifically in the oldest cohort. Hence, the findings of this CUA
should be treated with caution because it might not be representative of other age groups
restricting its applicability in clinical practice.
Implications 7.5
The state transition Markov model represents the natural history of diabetic foot ulcer disease
including the main complications such as infection and amputation. It is indicated for the
Norwegian patient population with non-healing neuropathic DFUs aged between 50-65 years
entailing males and females with DM Type I and Type II. It has been demonstrated that the
IDRT technology adjunct to the standard care versus standard care alone result in cost savings
and better health gains for the selected Norwegian population. This CUA may influence
changes in the current health policy in Norway or Scandinavia in general.
59
Recommendations for future research 7.6
Recent studies have escalated the challenge of poor healing rate of DFUs in clinical practice.
Therefore, the consideration of adjunctive treatment options to supplement the standard care
might be a good solution for chronic DFUs that do not heal within 6 weeks. Currently there is
a relatively small amount of cost-effectiveness analyses evaluating the benefits of bio-
engineered skin substitutes along the standard care. Notably, the current Markov state model
of DFUs is the first cost-utility study of IDRT technology and is a great basis for further
research exploring alternative advanced wound dressings and matrices for the target
population.
Additional research should be focused on improving the understanding and diagnosis of DFU
infections. In the management of DFUs with skin substitutes, it is imperative to reduce the
uncertainty of false negative infections due to the following reasons. First, treating patients
with IDRT would be beyond this technology’s indication thereby exposing patients to adverse
events that refer to lower QALY health states and increased likelihood of amputation. Second,
following the latter argument the costs of treatment per person would increase given an
inappropriate application of skin substitute.
It is recommended to investigate cost-effectiveness of patient sub-groups with different co-
morbidities. The findings of this CUA is restricted to the patient group with chronic
neuropathic DFUs without a sign of infection nor other comorbidities, therefore, further
research should examine the impact of added complexity on cost-effectiveness. Also the
results of EVPI and EVPI for parameters indicate that the society would benefit from future
research investigating the utilities of health states in more details as this group of parameters
indicated the highest monetary value.
60
8 Conclusion
In conclusion, the findings of this cost-utility study suggest that implementing Integra Dermal
Regeneration Template adjunct to standard care by adhering to the Norwegian DFU
guidelines is cost-effective in patients with non-healing neuropathic DFUs. An insight has
been gained with regard to the benefits of the intervention such as avoided infections and
amputations that are associated with hospitalizations, enhanced healing rate and more time
spent in ulcer-free state. This cost-utility analysis has highlighted the importance of having a
treatment strategy in place for individuals with chronic neuropathic diabetic ulcers when the
conventional therapy fails. This paper has provided further evidence of cost-effectiveness of
using bio-engineered skin substitutes as adjunct treatment. What’s more, the devised Markov
model was adapted to the Norwegian setting despite the lack of specific epidemiological data
and patients’ utilities. Incentivizing cost-effective IDRT intervention in hospitals and
municipalities in Norway will ease the burden of both patients with DFUs and on the
Norwegian health care system. Provided that recently the Norwegian parliament put forward a
proposition to the government to focus on the management of chronic wounds and the need to
untangle the complexity around funding. Therefore, this research is relevant, timely and may
have policy implications.
61
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Appendix A
Figure 9. The molecular biology of chronic wounds and delayed healing in diabetes.
Table 11. A summary of RCTs and CEAs examining skin substitutes, becaplermin, HBOT, VAC and optimal care treatments
for management of DFUs.
Author Intervention Comparator Perspective Source Effectiveness outcomes LOE
Marston et al
(2003)
Dermagraft
dressing
Standard care US payer RCT Higher wound closure 30%
vs 18.3% over 12 weeks
I
Cazzell et al
(2015)
Oasis SIS dressing Standard care US payer RCT Higher wound closure 54%
vs 32% over 12 weeks
I
Driver et al
(2015)
Integra Dermal
Regeneration
Template dressing
Standard care US payer RCT Higher wound closure 51%
vs 32% over 16 weeks
I
Veves et al
(2001)
Apligraf Standard care US payer RCT Higher complete wound
closure 56% vs 38% over
12 weeks
I
Lavery et al
(2014)
Grafix Standard care US payer RCT Higher wound closure 62%
vs 21% over 12 weeks;
Fewer AEs (44% vs 66%)
and infections (18% vs
26.2%)
I
Guest et al
(2018)
Collagen-based
dressings
Standard care UK NHS CEA Probability of healing at 4
months 0.53; QALYs at 4
months 0.163 per patient;
N/A
70
Guest et al
(2017)
Oasis SIS dressing Standard care US payer CEA At 12 months with SIS -
ulcer free months higher
by 42%, probability of
healing by 32%, 3%
decrease in probability of
infection and 1% decrease
in probability of
amputation;
N/A
Redekop et al
(2003)
Apligraf Good wound
care
Societal CEA At 1 year, cost with
Apligraf EUR4,656 and
with SC EUR5,310; With
Apligraf ulcer free time
increased by 1.53 months,
reduced risk of amputation
(6.35 vs 17.1%;
N/A
Ghatnekar et al
(2002)
Promogran Good wound
care
Health care
provider (UK,
France,
Germany,
Switzerland)
CEA At 3 months, 26% of
ulcers healed with
Promogran and 20% with
SC; at 1 year, months
spent in healed state 3.41
(GWC) and 3.75 (SC);
cost saving in all 4
countries;
N/A
Allenet et al
(2000)
Dermagraft Standard care French health
care provider
CEA ICER 38,784 FF; Average
cost for 52 weeks with SC
47,418 FF vs 54,384FF
with Dermagraft; total
number of ulcers healed
69.35% vs 76.38%;
N/A
Flack et al
(2008)
VAC Standard care
and
Advanced
dressings
US payer CEA At 1 year VAC vs SC
showed improved healing
rate (61% vs 59%), more
QALYs (0.54 vs 0.53),
overall lower cost (52,830
vs 61,757); VAC dominant
compared to SC;
Persson et al
(2000)
Becaplermin Good wound
care
Swedish NHS CEA At 1 year, with
becaplermin increased
time spent at healed state
by 24%, reduced
amputation probability by
9%;
Ragnarson
Tennvall et al
(2001)
Optimal care Standard care Swedish NHS CEA ICER risk group 3, 24-69
years equals $5,087; ICER
for risk group 3, 70-84
years equals $4,045
N/A
71
Ghatnekar et al
(2001)
Becaplermin Good wound
care
Swedish NHS CEA At 1 year with becaplermin
24% longer in ulcer free
state, decreased probability
of an amputation by 9%;
cost saving in the UK,
Switzerland and Germany;
in France added $19 per
ulcer free month;
N/A
Cheng et al
(2017)
Optimal care Standard care Australian
health care
provider
CEA 5-year cost savings of
$9,100.11 for 35-54 years;
$9,391.6 for 55-74 years;
$12,397.97 for 75+; 0.13
QALYs for two young
cohorts and 0.16 QALYs
for 75+
N/A
Kantor et al
(2001)
Becaplermin or
platelet releasate
(PR)
Standard care US payer CEA Incremental cost for PR vs
SC ($414.40) and
incremental cost for
becaplermin vs SC
($36.59) for increasing
healing of DFU by 1%
N/A
Dougherty
(2008)
Platelet rich plasma
(PRP) gel
Standard care CEA PRP cost $15,159 and 2.87
QALYs; SC cost 33,214
and 2.70 QALYs;
N/A
Chuck et al
(2008)
HBOT (hyperbaric
oxygen therapy)
Standard care Canadian payer CEA 12 year costs CND$40,695
with HBOT vs
CND$49,786 for SC;
3.64QALYs vs 3.01
QALYs;
N/A
SC – standard care;
GWC – good wound care;