PREDICTORS OF PERITONITIS AMONG CANADIAN PERITONEAL

DIALYSIS PATIENTS

By

Sharon J. Nessim, MD

A thesis submitted in conformity with the requirements for the degree of Master of

Science

Graduate Department of the Institute of Medical Science

University of Toronto

© Copyright by Sharon J. Nessim (2009)

ii

Predictors of Peritonitis among Canadian Peritoneal Dialysis Patients

Sharon J. Nessim

Master of Science, Institute of Medical Science, University of Toronto, 2009

Abstract

Despite the decreasing incidence of peritoneal dialysis (PD) peritonitis over time, its

occurrence is still associated with adverse outcomes. This thesis focuses on

determining factors associated with PD peritonitis in order to facilitate identification of

patients at risk.

Using data collected in a multicentre Canadian database between 1996 and 2005,

the study population comprised 4,247 incident PD patients, of whom 1,605 had at

least one peritonitis episode. Variables independently associated with peritonitis

included age [rate ratio (RR) 1.04 per decade increase, 95% CI 1.01­1.08], Black

race (RR 1.37, 95% CI 1.00­1.88) and having transferred from hemodialysis (RR

1.24, 95% CI 1.11­1.38). There was an interaction between gender and diabetes

(p=0.011), with an increased peritonitis risk only among female diabetics (RR 1.27,

95% CI 1.10­1.47). Choice of continuous ambulatory PD vs. automated PD did not

influence peritonitis risk. These results contribute to our understanding of peritonitis

risk among PD patients.

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Acknowledgements

I would like to thank several people, without whom this thesis would not have been

possible. First and foremost, I am extremely grateful to my supervisor, Dr. Vanita

Jassal, for her invaluable guidance, advice and support throughout my Master’s

degree. I am also indebted to my thesis committee members, Dr. Joanne Bargman,

Dr. Peter Austin and Dr. Jan Hux, who provided insight into the clinical importance of

the project, the study design and the intricacies of the data analysis and

interpretation. I thank you all for your interest, your time and your patience. I would

also like to thank Dr. Ken Story, Dr. Alex Kriukov and Dr. Rosane Nisenbaum for

assistance and advice regarding the statistical methods used in this thesis.

This work was generously supported by a Kidney Foundation of Canada Fellowship

Award, as well as by an Educational Fellowship from Baxter Healthcare. The

University of Toronto Clinician Scientist Program provided additional financial

support.

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Table of Contents

Abstract………………………………………………………………………………………ii

Acknowledgements…………………………………………………………………………iii

List of tables……………………………………………………………………………vii, viii

List of figures………………………………………………………………………………..ix

List of Abbreviations…………………………………………………………………………x

1. INTRODUCTION……………………………………………………………………….....1

1.1 Rationale………………………………………………………………………………1

1.2 Research objectives….…………………...…………………………………………2

1.3 Hypotheses…………………………………………………………………………...2

2. BACKGROUND…………………………………………………………………………..4

2.1 End­stage renal disease and dialysis options….…………………………………4

2.1.1 Renal replacement therapy: Peritoneal dialysis vs. hemodialysis…..….4

2.1.2 Peritoneal dialysis submodalities: CAPD and APD…..………………….7

2.2 Peritonitis in peritoneal dialysis patients………………………………………….9

2.2.1 Definition……………………………………………………………………..9

2.2.2 Incidence and outcomes…..………………………………………………10

2.2.3 Risk factors common to all PD patients……..…………………………..10

2.2.4 Peritonitis prevention strategies……………………………..….………..11

2.3 Predictors of peritonitis…………………………………………………….………16

2.3.1 Current knowledge regarding peritonitis risk……………………………16

2.3.2 Age…………………………………………………………………………..19

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2.3.3 Peritoneal dialysis submodality: CAPD vs. APD..………………………21

2.4 Statistical methodology used to study occurrence of peritonitis………………23

3. METHODOLOGY………………………………………………………………….........28

3.1 Data sources………………………………………………………………………..28

3.2 Patient population…………………………………………………………………..29

3.3 Model covariates……………………………………………………………………29

3.4 Outcomes……………………………………………………………………………30

3.5 Statistical Analyses…………………………………………………………………31

4. RESULTS………………………………………………………………………………...33

4.1 Patient cohort……...………………………………………………………………..33

4.2 Independent predictors of PD peritonitis…......................................................34

4.3 Interactions……………………………………………...…………………………..35

4.4 Era effect…………………………………………………………..………………..36

4.5 Comparison of peritonitis modeling strategies………………………………….36

4.6 Sensitivity analysis for peritonitis relapse/recurrence exclusion criteria……..37

5. DISCUSSION……………………………………………………………………………38

5.1 General discussion………………………………………………………………...38

5.2 Impact on nephrology practice……………………………………………………44

5.3 Limitations……………………………………………………………………..……45

5.4 Conclusions………………………………………………………………..……….48

5.5 Future directions……………………………………………………………………48

6. ILLUSTRATIONS…...............................................................................................50

6.1 Tables…………………………………………………………………………….....51

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6.2 Figures………………………………………………………………………………68

7. REFERENCES………………….............................................................................71

8. APPENDIX…………………………………………………………………………........85

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List of Tables

Table 1. Summary of studies testing the association between age and peritonitis……………………………………………………………………51

Table 2. Summary of studies on the association between CAPD vs. APD and peritonitis……………………………………………………………………52

Table 3. Distribution of peritonitis episodes within the patient cohort…………..53

Table 4. Baseline demographic characteristics for the entire patient cohort…..54

Table 5. Comparison of POET cohort vs. CORR data…………………………...55

Table 6. Multivariable negative binomial model for the outcome of peritonitis...56

Table 7. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch…………………..57

Table 8. Multivariable Andersen­Gill model for the outcome of peritonitis……..58

Table 9. Multivariable Andersen­Gill model for the outcome of peritonitis in the subgroup of patients with no submodality switch……………………….59

Table 10. Interaction between diabetes and peritonitis by gender………………..60

Table 11. Testing for interactions between each variable and era………………..61

Table 12. Association between age and peritonitis by era…………………………62

Table 13. Comparison of results of multivariable negative binomial model and Andersen­Gill model for peritonitis………………………………………..63

Table 14. Multivariable negative binomial model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)……………………………………………………………………….64

Table 15. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria) ……………………65

viii

Table 16. Multivariable Andersen­Gill model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)………………………………………………………………………66

Table 17. Multivariable Andersen­Gill model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria) …………………...67

ix

List of Figures

Figure 1. Illustration of intraluminal and periluminal entry of organisms into the peritoneal cavity…………………………………………………………….68

Figure 2. Flow diagram of patient cohort from POET database…………………..69

Figure 3. Distribution of patients in POET database by province………………...70

x

List of Abbreviations

ANZDATA Australia and New Zealand Dialysis and Transplant Registry

APD Automated peritoneal dialysis

CAPD Continuous ambulatory peritoneal dialysis

CI Confidence interval

CKD Chronic kidney disease

CNS Coagulase­negative staphylococcus

CORR Canadian Organ Replacement Register

ESRD End­stage renal disease

GN Glomerulonephritis

HD Hemodialysis

HR Hazard ratio

PD Peritoneal dialysis

POET Peritonitis Organism Exit sites Tunnel infections

RCT Randomized controlled trial

RR Rate ratio

RRF Residual renal function

SD Standard deviation

USRDS United States Renal Data System

1

INTRODUCTION

1.1 Rationale

Despite improvements in the management of patients with chronic kidney disease

(CKD), there was a 34% increase in the number of Canadian patients reaching end­

stage renal disease (ESRD) between 1997 and 2006 (1). When renal transplantation

is not an immediate option, the only remaining renal replacement therapy option for

patients who reach ESRD is dialysis. There are currently two forms of dialysis

available: hemodialysis (HD) and peritoneal dialysis (PD). While HD is the more

commonly utilized modality, PD has the advantage of being a home­based therapy

(relative to HD, in which the majority of patients are required to come to hospital for

treatments thrice weekly).

One of the most concerning complications associated with PD is infection of the

peritoneal space – known as peritonitis. To date, little is known about how best to

study the occurrence of peritonitis and what factors predict its occurrence.

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1.2 Research Objectives

The thesis will address the following research questions:

(1) Among incident Canadian peritoneal dialysis patients, what patient­ and dialysis­

related factors are associated with peritonitis? Specifically:

(i) Is increasing age associated with an increased risk of peritonitis?

(ii) Does choice of PD submodality (continuous ambulatory PD (CAPD) vs.

automated PD (APD)) affect peritonitis risk?

(2) Are the results of rate and time­to­event analyses comparable in the study of the

occurrence of peritonitis?

1.3 Hypotheses

This thesis seeks to identify whether there are baseline demographic characteristics

among incident PD patients that predict the occurrence of peritonitis. Two variables

for which the current literature is inconsistent include age and PD submodality. It is

hypothesized that increasing age is associated with a higher risk of peritonitis, and

that the two submodalities of PD, CAPD and APD, are similar with regard to

peritonitis risk.

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It is generally believed that it is vital to incorporate the amount of time spent on

dialysis into the way in which peritonitis as an outcome variable is defined, such that

the most appropriate analyses for the occurrence of peritonitis would involve defining

peritonitis as a either a rate or time­to­event. We hypothesized that both rate and

time­to­event analyses are effective tools to study peritonitis, and would yield similar

predictors of peritonitis with similar risk estimates.

4

2. BACKGROUND

2.1 End­stage renal disease and dialysis options

2.1.1 Renal replacement therapy: Peritoneal dialysis vs. hemodialysis

Chronic kidney disease is a growing medical problem in the general population. The

increased prevalence of renal disease has largely resulted from the rising life

expectancy (2), coupled with an increasing prevalence of diabetes mellitus (3). While

the greater awareness of the presence of renal disease among general practitioners

has led to earlier referral of patients to nephrologists and better preventive care (4,

5), progression to ESRD remains a major problem. In Canada, the number of

individuals starting renal replacement therapy has increased from 3,958 in 1997 to

5,321 in 2006 (1).The optimal mode of renal replacement therapy in these patients is

renal transplantation (6). However, given the relatively low living kidney donation rate,

the long waiting time for a deceased donor kidney and the ineligibility for transplant in

some patients, dialysis is frequently the only available treatment option.

There are two dialysis modalities, hemodialysis (HD) and peritoneal dialysis (PD).

Hemodialysis is a form of renal replacement therapy in which the patient’s blood is

passed across a filter that allows for removal of accumulated toxins and electrolytes

via diffusion, and removal of excess fluid via ultrafiltration. Using this filter (known as

the dialyzer), the dialysis machine attempts to reproduce normal kidney function. In

5

order for HD to be effective, patients generally require a minimum of 12 hours per

week connected to the machine. This is usually achieved by having patients come to

hospital for 4­hour treatment sessions three times per week. Some HD patients can

be trained to dialyze themselves at home, but home HD remains an option for only a

minority of patients. Access to the patient’s bloodstream for these treatments requires

some form of vascular access, with options including a tunneled intravenous dialysis

catheter or an arteriovenous fistula or graft.

Peritoneal dialysis is the other form of dialysis. Rather than passing a patient’s blood

through an artificial dialyzer as occurs on HD, PD utilizes the patient’s own peritoneal

membrane as the ‘filter’ over which diffusion and ultrafiltration occur. A permanent PD

catheter is inserted through which an electrolyte­balanced, glucose­rich dialysis fluid

(known as dialysate) is infused into and drained from the peritoneal cavity. Once the

dialysate is instilled, there is diffusion of uremic toxins and electrolytes down their

concentration gradient from the bloodstream, across the peritoneal membrane and

into the fluid. Simultaneously, the high glucose concentration in the dialysate creates

an osmotic gradient for the movement of fluid from the bloodstream into the

peritoneal cavity. Once sufficient time has passed for diffusion and ultrafiltration to

occur, the dialysate is then drained from the peritoneal cavity and fresh dialysate is

instilled. The procedure of filling and draining the peritoneal cavity with dialysate is

repeated several times per day.

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Survival outcomes for patients on HD vs. PD have been compared in several studies.

Some have reported better outcomes with PD (7­9), while others have shown better

outcomes with HD (10­12) or no difference between the two modalities (13­16). The

inconsistency in the literature may relate to several factors. Firstly, the study

populations varied widely, as demographic characteristics of patients differed among

Canada, the United States and the European countries in which studies were

conducted. Secondly, the time period over which the studies were carried out ranged

from as early as 1987 to as recently as 1999, such that advances in dialytic therapies

over that time period could have differentially affected outcome. Thirdly, some studies

compared incident PD and HD patients, while others focused on prevalent patients.

Finally, the follow up time was variable across studies. The latter two considerations

are particularly important as it appears that the relationship between dialysis modality

and outcome changes over time, such that the survival advantage for PD reported in

some studies was only seen in incident cohorts during the first two years after

initiation of dialysis (7­9).

The biggest limitation in determining the effect of dialysis modality on outcome is the

observational nature of the studies used to try to answer this question. As such, they

cannot fully account for the fact that patients who go on PD, a home­based modality,

frequently differ systematically from those who go on in­centre HD despite attempts

to adjust for potential confounders. In order to answer this question while avoiding

systematic bias, an attempt was made to do a large­scale randomized controlled trial

(RCT) of HD vs. PD (17). Unfortunately, this RCT was unsuccessful because of

7

difficulty in recruitment, with only 5% of eligible patients having no preference for PD

or HD and agreeing to be randomized to either modality. In the absence of an RCT,

physician opinion varies widely. While some physicians have a bias favoring either

HD or PD, most physicians believe that HD and PD are equally effective renal

replacement therapy options and that the decision should be left to the patient when

possible. Hence, the majority of patients who receive pre­dialysis care are given

information on both modalities, and are offered a choice.

In most countries, HD is the most frequently used therapy, although this varies widely

by region (3, 18­21). In Canada, 82% of prevalent dialysis patients in 2006 were on

HD with the remaining 18% on PD (1). Since PD is a home­based therapy, it provides

greater independence to patients and allows for less utilization of hospital­based

resources relative to in­centre HD. As a result, in 2005, the Ministry of Health in

Ontario made several recommendations in an attempt to increase the prevalence of

PD utilization to 30% (22). It is clear that a better understanding of the complications

associated with PD would aid in achieving this target.

2.1.2 Peritoneal dialysis submodalities: CAPD and APD

Once a decision is made to initiate PD, a choice must be made between the two

forms of PD: continuous ambulatory peritoneal dialysis (CAPD) and automated

peritoneal dialysis (APD).

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In CAPD, patients are taught how to manually fill and drain their peritoneal cavity via

their PD catheter using aseptic technique. A typical patient performs 4 exchanges

daily, usually in the early morning, mid­day, evening, and before bedtime. For

example, 2 L of dialysate is instilled at 8 am and left to dwell until 12 pm. This fluid is

then drained, and another 2 L would be instilled to dwell until 4 pm, and so on. The

net result is typically 4 exchanges of fluid, with dialysate in the peritoneal cavity

throughout the 24­hour period.

In APD, patients use an automated cycler to perform their exchanges during the

night. Patients on APD connect their PD catheter to the cycler before going to bed.

The cycler, which is programmable to the patient’s specifications, then instills

dialysate into the peritoneal cavity, and will exchange the specified volume of fluid at

preset intervals during the night while the patient sleeps. For example, the cycler may

be programmed to provide four 2 L exchanges over a 9­hour period during the night.

In the morning, the patient will disconnect from the cycler, and carry on with his or her

daily activities. The choice of CAPD vs. APD in some instances may be guided by the

transport characteristics of the patient’s peritoneal membrane, but is often left to the

discretion of the patient based on lifestyle and usual daily activities. In the event that

a patient is unable to perform the manual exchanges required for CAPD due to visual

or cognitive impairment or impaired manual dexterity, APD is usually the submodality

of choice as connections to the cycler can be performed by a family member or

visiting nurse.

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2.2 Peritonitis in peritoneal dialysis patients

2.2.1 Definition

The peritoneal cavity into which dialysate is infused in PD patients is a sterile

environment. Infection of the peritoneal space is known as peritonitis. In the general

population, peritonitis is an extremely rare occurrence, and usually results from a

perforated abdominal viscus with movement of organisms from the bowel lumen into

the peritoneal space. In contrast, peritonitis is a well described complication among

patients on PD. The ISPD has defined PD Peritonitis as at least 2 of 3 of the

following: (i) clinical symptoms or signs suggestive of peritoneal inflammation, (ii)

effluent cell count with greater than 100 white blood cells per µL, of which at least

50% are neutrophils, and (iii) a positive effluent Gram stain or culture (23).

Entry of organisms into the peritoneal cavity in PD patients can occur via several

mechanisms. The two most common mechanisms include introduction of organisms

into the lumen of the catheter by touch contamination at the time of catheter

connections, and periluminal entry from the exit site along the outside wall of the

catheter through the subcutaneous tunnel and into the peritoneal cavity (Figure 1).

Peritonitis episodes can also result from transmigration of organisms across the

intestinal wall, and rarely from bacteremia with seeding of the peritoneal cavity or

transvaginal migration of organisms.

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2.2.2 Incidence and outcomes

In the early years after introduction of PD, peritonitis occurred once in every 9­10

patient­months (24, 25). Since that time, however, the frequency of peritonitis as a

complication has continued to decline (26­30), with current peritonitis rates as low as

1 in every 41 patient­months (29).

Despite the decreasing peritonitis rates over time, the occurrence of peritonitis

remains a concern given its association with adverse outcomes. Specifically, PD

peritonitis is associated with increased mortality (31, 32) and hospitalization (33). In

addition, PD­related infections are the most frequent reason for discontinuation of

PD, accounting for 28% of transfers to HD in one study (34). While HD patients do

not get peritonitis, those with tunneled dialysis catheters are instead prone to

catheter­related bacteremia, such that overall infection rates between PD and HD are

similar (35). Despite the comparable infection risk, the occurrence of peritonitis and

the associated potential for adverse outcomes has led some nephrologists to avoid

recommending PD.

2.2.3 Risk factors common to all PD patients

There are several factors common to all PD patients that favor the occurrence of

peritonitis. Firstly, all patients have PD catheters that allow for communication

11

between the non­sterile external environment and the sterile peritoneal cavity. This

allows for both intraluminal and periluminal entry of organisms. A second factor

common to all PD patients is the use of dialysate containing a high concentration of

glucose. Since glucose is an excellent growth medium for bacteria, the introduction of

even a small inoculum of organisms may be enough to cause peritonitis.

Furthermore, it has been shown that ESRD patients have impaired host immunity

(36­39), as well as abnormal peritoneal immune function (40­42). The reason for the

impaired peritoneal immunity is thought to be the unphysiologic nature of

conventional PD solutions, including their high glucose concentration,

hyperosmolarity, acidic pH and the formation of glucose degradation products during

the heat sterilization of the dialysate bags. Under normal circumstances, local

peritoneal immunity plays an important role in the prevention and clearance of PD

peritonitis. When exposed to conventional dialysate, however, there is abnormal

leukocyte recruitment in response to inflammatory stimuli (42) and impaired

phagocytic function (40).

2.2.4 Peritonitis Prevention Strategies

With these common risk factors in mind, several modifications to PD practice have

been made over time in order to reduce peritonitis risk. The first major advance was

the introduction of improved PD connectology. The initial catheter connection method

involved conventional ‘spike’ connection systems. In the 1980s, it was hypothesized

that disconnect systems using a Y–set would be superior to spike connection

12

systems for the prevention of peritonitis. The “flush before fill” technique using a Y

connection system allowed for drainage of the spent dialysate into an empty drainage

bag, followed by flushing of the tubing with some fresh dialysate before infusion of

the remainder of the fresh dialysate into the patient. It was hypothesized that flushing

the tubing before filling the peritoneal cavity would reduce the risk of infusing

organisms introduced during the connection procedure into the peritoneal cavity. As

predicted, the use of this Y­set resulted in important reductions in the rate of

peritonitis in several studies (24, 43­45). Subsequently, a more advanced form of

disconnect system known as the double­bag system was also shown to be superior

to standard spike connection systems (45). While the double bag (or twin bag)

system was hypothesized to further reduce peritonitis risk relative to standard Y­sets

by having one fewer connection, studies comparing these two disconnect systems

have not consistently shown a benefit of one over the other (45­48). Based on the

available data, the 2005 International Society for Peritoneal Dialysis (ISPD)

guidelines suggest to avoid spiking of dialysis bags in CAPD patients, and to instead

use a double­bag system with the “flush before fill” technique to reduce the risk of

contamination (49).

The second major advance was the introduction of antibacterial ointments applied to

the PD catheter exit site or nares to reduce bacterial colonization. The risk associated

with bacterial colonization was first recognized by Luzar et al who studied S. aureus

nasal carriage in patients on CAPD. In this study, it was found that S. aureus nasal

carriers had exit site infection rates that were four times higher than non­carriers (50).

13

The most likely explanation for this is that patients who have S. aureus colonization in

their nares are more likely to have S. aureus colonization at their PD catheter exit

site. The corollary of this is that eradication of colonization would reduce peritonitis

occurring via periluminal migration of bacteria along the catheter tunnel into the

peritoneal cavity. Supporting this hypothesis, it has since been shown that application

of antibacterial ointments to the nares or catheter exit site not only reduces the risk of

exit site infection (30, 51­53) but also of peritonitis (30, 53). The most studied

ointment is mupirocin, which is a topical antibacterial agent with excellent activity

against Gram positive organisms (54). The use of an agent active against Gram

positive bacteria is appropriate since S. aureus is the most common organism

causing exit site infection, accounting for 42% of episodes in one study of Canadian

patients (26). While the majority of the studies on ointments for peritonitis prophylaxis

have involved use of mupirocin, there are more recent data to suggest that

gentamicin cream applied to the catheter exit site is an excellent alternative (30). One

advantage of the latter agent over mupirocin is the provision of Gram negative

coverage, particularly against Pseudomonas species. Based on these data, the 2005

ISPD guidelines for PD­related infections suggested using one of the following

regimens: (1) exit site mupirocin daily in all patients or only in S. aureus nasal

carriers, (2) intranasal mupirocin for 5­7 days each month in nasal carriers, or (3) exit

site gentamicin cream daily in all patients (49).

Other strategies that have been adopted over time to reduce risk of peritonitis and

exit site infection include use of downward­directed catheter tunnels (55), and

14

administration of prophylactic antibiotics at the time of catheter insertion (56, 57).

Prophylactic antibiotics have also been recommended prior to colonoscopies with

polypectomy based on several case reports of peritonitis with enteric organisms

occurring shortly after such procedures (49, 58­61).

In addition to the above strategies, several studies have looked at different PD

catheter designs. While modifications to the extraperitoneal segment of the catheter

have not led to reductions in peritonitis (62­68), the data on use of single vs. double

cuff catheters are conflicting (69­71). As a result, no definitive recommendations have

been made as to the optimal PD catheter type for the prevention of peritonitis (49).

Given that impaired peritoneal immunity is thought to be due in part to the

bioincompatibility of the standard dialysate solutions used, several studies have

sought to determine whether use of newer, more biocompatible PD solutions might

be associated with a lower peritonitis risk. The improved biocompatibility of these

solutions relates to their more neutral pH and their lower concentration of glucose

degradation products. In a small randomized crossover study comparing

biocompatible solutions with conventional solutions, the use of biocompatible

dialysate for 6 months was associated with enhanced phagocytic activity of peritoneal

macrophages, reduced constitutive inflammatory stimulation and better preservation

of the mesothelial cell integrity (72). Three observational studies to date have

reported a lower peritonitis rate with biocompatible solutions as compared with

standard solutions (73­75), with another showing no effect (76). The only RCT of

15

conventional vs. biocompatible PD fluids that included data on infectious outcomes

did not show a difference in peritonitis rates between the groups (77), although

peritonitis was a secondary endpoint and the study was therefore not powered for

this outcome. Given the cost associated with these biocompatible solutions, further

randomized controlled studies are required to clarify whether the improved peritoneal

immunity translates into reduced peritonitis risk.

The importance of proper patient training in the prevention of PD­related infections

has also been studied. In one RCT, 620 PD patients were randomly assigned to

receive either enhanced training using an adult learning theory­based curriculum or a

non­standardized conventional training program. Those who received the enhanced

training had significantly fewer exit site infections and peritonitis episodes (78).

While the frequency of peritonitis has been dramatically reduced with the

incorporation of these preventive strategies, peritonitis still occurs. And despite the

presence of the aforementioned risk factors in all PD patients, it remains unclear why

some patients never develop peritonitis while others go on to have multiple episodes.

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2.3 Predictors of peritonitis

2.3.1 Current knowledge regarding peritonitis risk

Several observational studies have attempted to identify factors that might predict the

occurrence of peritonitis in PD patients. Among demographic characteristics, two

American studies have identified an association between Black race and peritonitis,

with a 26­32% increased risk (55, 79). In addition, a large observational study from

Australia and New Zealand reported a 76% increased risk of peritonitis among

Aboriginal patients (80). While each of these observational studies adjusted for a

wide range of patient­ and dialysis­related factors, neither accounted for

socioeconomic characteristics which may have varied widely by racial category.

Another risk factor that has been associated with peritonitis is diabetes mellitus, with

a 13 to 64% increased peritonitis risk among diabetics (79, 81, 82). This is not

surprising as diabetic patients with renal disease have been shown to have an

increased risk of infections in general (35, 83). Obesity has also been linked with a

higher risk of peritonitis, with one study reporting a hazard ratio (HR) of 1.08 per 5

kg/m 2 increase in body mass index (80), and another reporting a HR of 1.29 in

patients with a body mass index ≥ 30 (84). This higher risk may relate to the

increased risk of dialysate leak among obese PD patients (85), in that leaks may

predispose to peritonitis. In addition, an abdominal pannus may overlie the exit site

and impair proper exit site care.

17

In addition to the above predictors, current or recent use of immunosuppressive

agents for previous transplantation or glomerulonephritis (GN) has been associated

with an increased risk of PD peritonitis (86). The increased risk among patients with

failed transplants has been confirmed in some studies (87, 88) but not in others (55,

89, 90). While immunosuppression would be expected to be associated with an

increased infection risk, the observational nature of these studies raises the

possibility of residual confounding, as patients who have received a renal transplant

are frequently the healthiest patients within a dialysis population. The improved

general health of these transplant patients may offset any potential adverse infection

risk associated with immunosuppression. Alternatively, one might hypothesize that

the absence of an association between immunosuppression and peritonitis in some

studies could reflect the relatively low doses of immunomodulatory agents used in

patients with failed transplants who have returned to dialysis.

Among biochemical parameters that are routinely measured in PD patients, a low

serum albumin at the time of initiation of PD has been shown to be associated with a

shorter time to first peritonitis, with HRs ranging from 1.35­1.67 per 10 g/L decrease

in baseline albumin (81, 91). The relationship between hypoalbuminemia and

peritonitis may be either causative or associative. Specifically, one could hypothesize

that malnutrition may directly increase risk of infection as a result of weaker immunity.

In contrast, it is possible that a low albumin at the time of PD initiation is simply a

18

marker of a more inflamed patient with greater comorbidity who is more predisposed

to infectious complications.

Another biochemical variable of relevance is residual renal function (RRF) (82). In a

recent study, each additional 1 ml/min/1.73 m 2 of residual glomerular filtration rate

was independently associated with a 21% lower risk of peritonitis. The reduced risk

associated with RRF may relate to the better solute clearance achieved (particularly

of middle molecules), as it is known that uremia impairs host immunity (36­39).

Alternatively, RRF may simply be a marker of a healthier patient with either less

comorbidity or a shorter dialysis vintage, and therefore a patient less likely to develop

peritonitis.

In addition to known demographic and biochemical associations with peritonitis,

having a first peritonitis episode is associated with an increased risk of developing a

subsequent episode. In one study, a peritonitis episode occurring within the first 6

months after initiation of PD was associated with a shorter time to subsequent

peritonitis, with a HR of 2.15 (79). A second study showed a RR of 2.08 for peritonitis

in the patients who had a prior episode (55). This increased risk of subsequent

peritonitis likely relates in part to patient factors that predispose to the development of

peritonitis. However, an additional consideration may be the formation of a biofilm on

the PD catheter over time (92­94). This biofilm is thought to consist of bacteria that

attach to the PD catheter and become surrounded by an impenetrable glycocalyx

matrix coat. It is hypothesized that the presence of a biofilm may predispose to the

19

development of peritonitis, and put patients at increased risk of subsequent infection

with the same organism due to difficulty in eradicating the organism. This hypothesis

was tested in a cohort of 198 patients with multiple peritonitis episodes (95). In this

study, 80% of patients had at least one repeat infection with the same organism, and

79% of patients had more than half of their peritonitis episodes caused by the same

organism, suggesting that bacterial biofilm formation on the peritoneal catheters may

be an additional factor playing a role in the frequency of peritonitis in some patients.

Two important variables for which data have been inconsistent in regard to peritonitis

risk include age and choice of CAPD vs. APD. These will be discussed below.

2.3.2 Age

The question of whether increasing age is associated with a higher risk of peritonitis

is an important one given the increasing number of older patients reaching ESRD (3).

Answering this question is particularly relevant as some nephrologists are reluctant to

offer PD as a dialysis option to elderly patients. This was evidenced by a recent study

in which North American nephrologists from several centers were asked to assess

medical eligibility for HD, PD and renal transplantation among patients with advanced

CKD (96). In this study, the most frequently cited reason for not offering PD to a

patient was older age.

20

The relationship between age and peritonitis among PD patients has been

investigated in several observational studies. The largest study to have looked at this

studied 11,975 American patients on PD between 1994 and 1997. With age treated

as a categorical variable, age ≤ 44 years was associated with an increased risk of

peritonitis relative to those aged 65 to 74, with a HR of 1.09 at 9 months (79). In

contrast, in a large, multicenter cohort of 3,162 patients from Australia and New

Zealand followed from 1999­2003, age ≥ 65 was associated with an increased

hazard of peritonitis relative to those under 25, 25­44, and 45­64 years of age (80).

Two other studies of Spanish PD patients reported a higher peritonitis rate in older

patients (97, 98), while another study demonstrated an increased peritonitis risk

among non­diabetic patients greater than 70 years of age (99). Several other studies

have reported no association between age and peritonitis (82, 100­103). These

studies are summarized in Table 1.

There are several possible explanations for the inconsistent relationship between age

and peritonitis across studies. The first is that age has been variably defined in these

studies. For example, some studies used age as a categorical variable (with various

arbitrary cutoffs used to define ‘elderly’), while others used age as a continuous

variable. Another contributing factor is the varying size of the studies to date, such

that some of the smaller studies may have been underpowered to detect an

association, should one have been present. Furthermore, the studies that have

assessed the association between age and peritonitis span nearly two decades.

Since PD technique has changed significantly over time, it is difficult to know whether

21

improvements in technique would have had an impact on the relationship between

age and peritonitis. For example, elderly patients who have less manual dexterity

may be more likely to have breaks in aseptic technique, such that introduction of the

‘flush before fill’ technique and prophylactic ointments might offer greatest benefit in

this population and offset the increased risk. Thus, the best assessment of the

relationship between peritonitis and age would involve studying age as a continuous

variable in a large, contemporary cohort of PD patients.

2.3.3 Peritoneal dialysis submodality: CAPD vs. APD

With regard to choice of PD submodality, understanding the risk of peritonitis with

CAPD vs. APD is important as it has implications for our recommendations to

patients when they are choosing between these two options. CAPD was originally

proposed to be associated with an increased risk of peritonitis in the early years of

PD before advances in connectology. In contrast, the potential association between

APD and a higher peritonitis risk has been attributed to the presence of a high

comorbidity burden among a subset of APD patients, who may have been pre­

selected for this modality on the basis of an inability to perform CAPD. If CAPD and

APD are equivalent in terms of infectious risk, it would reinforce our practice of

offering both options to patients initiating PD when feasible. In the absence of large,

contemporary RCTs comparing the outcomes of the two submodalities, we are left to

rely mostly on observational data to try to answer this question.

22

Several studies have addressed the relationship between use of CAPD vs. APD and

peritonitis, including one RCT and several observational studies. The clinical trial

conducted by de Fijter et al included 97 patients enrolled from 1988 to 1991, and

randomized them to CAPD using a Y­connector or APD. In this study, there were 54

peritonitis episodes among 25 CAPD patients, as compared with 31 episodes among

19 APD patients. This corresponded to a peritonitis rate that was significantly higher

among CAPD patients relative to APD patients (0.94 vs. 0.51 episodes per patient­

year) (104). Two other observational studies that have looked at the peritonitis risk

associated with CAPD vs. APD have also suggested that CAPD is associated with a

higher peritonitis risk, with HRs ranging from 1.72 to 2.08 (97, 100). However, in the

largest observational study to have addressed this question, using data from 11,975

American PD patients, CAPD was associated with a 6% lower risk of peritonitis

relative to APD (79). Furthermore, another study of 1,205 Scottish PD patients found

no difference between CAPD and APD in terms of peritonitis risk (105). These

studies are summarized in Table 2.

While the only RCT to have addressed this question suggested that CAPD was

associated with a higher peritonitis risk than APD, this study was conducted

approximately 20 years ago at a time when peritonitis rates and PD practice were

significantly different from what they are at present. As a result, the external validity

of the study (or generalizeability) may be limited. In addition, some of the apparent

inconsistency among these studies may relate in part to the fact that some of the

studies in which CAPD was associated with a higher peritonitis rate included patients

23

who were on PD before the adoption of the improved connectology systems, which

greatly reduced the risk of contamination at the time of an exchange (24, 43­45). The

implementation of such systems would have preferentially benefited CAPD patients.

Thus, studying a contemporary cohort of patients who initiated dialysis after adoption

of these more advanced connectology systems is vital to understanding the current

peritonitis risk with CAPD vs. APD.

2.4 Statistical methodology used to study occurrence of peritonitis

Some of the variability in the predictors of peritonitis that have been identified may

relate to the patient populations studied, the varying sizes of the cohorts studied, the

different variables included in the multivariable models in each study and the different

eras over which data were collected. However, an additional complicating factor is

the type of analysis chosen to assess for variables associated with peritonitis.

The choice of the type of multivariable regression model depends on the way in

which the outcome variable, peritonitis, is defined. The simplest way to define

peritonitis would be as a dichotomous variable, such that a patient either had or did

not have a peritonitis episode. It is apparent that this definition of peritonitis is limited

by the fact that it does not take into account the amount of time the patient is on PD

(which represents the time at risk). As a result, if all patients are followed for a very

short time, one would not be able to distinguish between those patients who would

24

never have developed peritonitis and those who were destined to get peritonitis after

a certain amount of ‘exposure time’ on PD. It is clear, then, that the definition of

peritonitis as an outcome variable should incorporate the amount of time a patient is

on PD.

The first method of defining peritonitis that incorporates time involves calculating a

peritonitis rate for each patient – that is, the number of episodes of peritonitis

experienced by a patient divided by the follow­up time. Models in which one can

determine the relationship between a variable and the peritonitis rate include the

Poisson model and the negative binomial model. The Poisson model is best used for

a stochastic process when events occur independently of one another. One

assumption inherent in Poisson modeling is that the mean is equal to the variance.

The negative binomial model is a variant of the Poisson model that does not make

the assumption that the mean is equal to the variance, and as a result, it provides a

better fit if there is overdispersion of the data (106, 107).

The second option for defining peritonitis with incorporation of time at risk is to study

the time­to­peritonitis. The most frequently used model is the Cox proportional

hazards model (as long as the hazard associated with each variable is proportional

over time). This allows for determination of the predictors of a shorter time to

peritonitis. One limitation of this type of analysis is that it allows one to look only at

predictors of time­to­first peritonitis, such that one would not be able to utilize all data

on subsequent peritonitis episodes in the same patient. Performing a time­to­event

25

analysis incorporating multiple events occurring over time is possible, but to date, this

analytic tool has not been used for the study of peritonitis.

There are three time­to­event modeling approaches that have been used to study

multiple events within an individual: the Andersen­Gill model, the marginal (WLW)

model and the conditional (PWP) probability model (108). The Andersen­Gill model is

a variant of the Cox model that allows for incorporation of data on recurrent events.

One condition of this model is the assumption of independence of events within a

subject. Based on simulated data, this model gives nearly unbiased estimates of the

treatment effect even when an important covariate has been omitted. The marginal

model is a model that was first used by Wei, Lin and Weissfeld to analyze bladder

cancer data in the context of multiple recurrences per subject. Limitations of this

model include potential violation of the proportional hazards assumption, and biased

estimates of effect when covariates are omitted. The conditional model, proposed by

Prentice, Williams and Peterson, allows for variation in the underlying intensity

function from event to event. However, the conditional model is even more

susceptible to biased estimates when an important covariate is omitted. When

choosing between these three models for the study of PD peritonitis, the absence of

important covariates in many large dialysis datasets would favor the use of the

Andersen­Gill model.

The utility of both rate and time­to­event analyses in the study of peritonitis was

identified in the early years of PD therapy. The first insight into the optimal statistical

26

modeling of peritonitis came in 1981 when Corey studied a cohort of 129 Toronto PD

patients and determined that the distribution of peritonitis was random based on the

goodness of fit provided by the Poisson regression model (109). A later study

proposed a variant of this strategy in the form of a ‘mixed effects’ Poisson model,

which could incorporate not only the ‘fixed effects’ corresponding to information

collected across individuals, but also a random effect due to individuals (110).

Another study looked at using a life­table analysis to model peritonitis, and showed

that the peritonitis probability curve constructed with only the first episode of

peritonitis was almost identical to that constructed from all episodes of peritonitis

(111). The authors concluded that this finding further supported the random

distribution of peritonitis among patients, and suggested that analyzing the time­to­

first peritonitis was an accurate means of expressing the probability of developing

peritonitis.

The majority of studies to date that have looked at predictors of the occurrence of

peritonitis have either performed a peritonitis rate analysis or a time­to­first peritonitis

analysis. While readers of the literature tend to interpret these studies

interchangeably, there are little comparative data to date to suggest whether

peritonitis rate analyses and time­to­event analyses are equivalent as analytic tools in

the study of peritonitis. Two studies have compared modeling strategies. In the first

study of a small number of pediatric patients, a tight correlation was demonstrated

between a peritonitis rate analysis using a negative binomial model and a time to first

peritonitis analysis (112). A second study also showed similar predictors of peritonitis

27

and parameter estimates using a negative binomial model and a time­to­first

peritonitis model (80). It is not known whether modeling peritonitis using a rate

analysis and an Andersen­Gill model for multiple events would yield similar

predictors, and whether the estimates of risk would be congruent. Demonstration of

such congruency would be relevant to the interpretation of published studies and to

the design of future studies.

28

3. METHODOLOGY

3.1 Data sources

The study included Canadian PD patients for whom data were available through the

Peritonitis Organism Exit sites Tunnel infections (POET) database (Baxter

Healthcare). The POET Clinical Monitoring System is a software program designed

to organize and analyze PD patient data to identify and monitor the causes of

infection, catheter complications, and therapy transfers.

The POET software was offered to all PD centers in Canada that were using Baxter

PD products (representing an estimated 85% of Canadian PD centers). No financial

or other incentive was provided to the individual centers for use of the software.

Installation of the software was documented for 56 Canadian centers. To create the

database, Canadian centers that used POET in a consistent manner to track

infectious and non­infectious complications were asked to contribute their data.

Specifically, centers that reported data for at least 1 year and had cumulative data for

at least 20 adult patients in their program were invited to contribute their de­identified

data. Twenty five centers met these criteria, and were included in the database.

These Canadian centers ranged in size from 48–803 patients per center, and

reported on patients between 1990 and 2005. Data collection was performed by the

PD nurses in the majority of centers.

29

Prior to study initiation, research ethics board approval was obtained from the

University Health Network.

3.2 Patient population

The database included both prospectively­collected data on incident PD patients as

well as data on prevalent patients that were retrospectively entered into the database

when a given center started using the POET software. In order to distinguish

prospectively­collected data from retrospectively­collected data, the 25 Canadian

centers contributing to the database were contacted to determine the exact time

when their center started using the POET software for data collection. In order to

avoid a survivorship bias related to inclusion of prevalent patients and a recall bias

related to retrospective data collection, we included only incident patients in whom

data were collected prospectively. The time period for prospective data collection was

from January 1, 1996 until September 12, 2005.

3.3 Model covariates

Demographic data used in the current study included age, gender, race, diabetic

status, GN as a cause of ESRD, modality before PD start (new to dialysis, transfer

from HD, failed transplant) and PD submodality (CAPD vs. APD). Data available for

the latter variable included the submodality at the time of initiation of PD, and the

30

submodality as of the most recent data entry. Detailed information on all submodality

switches during a patient’s time on PD was not available. As a result, in order to

reduce confounding by modality switching, a secondary analysis was performed after

exclusion of any patient who switched from one PD submodality to another (CAPD to

APD or vice versa).

While the database included extensive comorbidity data, the majority of these

comorbidities (with the exception of diabetes mellitus) appeared to be under­reported

when compared with CORR data and were therefore excluded from the analysis (5).

This is not surprising as comorbidities are known to be under­reported in

administrative data (113).

Given that the prospective cohort included patients who initiated PD over a 10­year

period, we defined two eras of patients in order to assess for an era effect: an earlier

cohort consisting of those who initiated PD between January 1, 1996 and December

31, 2000, and a more contemporary cohort consisting of those who initiated PD

between January 1, 2001 and September 12, 2005.

3.4 Outcomes

The primary outcome was the occurrence of peritonitis, which was defined in two

ways: (1) as a peritonitis rate, and (2) as a time­to­peritonitis incorporating all

peritonitis episodes.

31

In order to focus on independent peritonitis events, relapsing or recurrent episodes

were excluded. Standard ISPD definitions were used, with a relapse defined as an

episode occurring within 4 weeks of completion of therapy of a prior infection with

negative culture or the same organism, and a recurrence defined as an episode

occurring within 4 weeks of completion of therapy of a prior infection but with a

different organism (49). While we did not have data on the duration of antibiotic

therapy, it was standard practice over the time period of the study to treat peritonitis

with a minimum of 2 weeks of antibiotics, with some patients being treated with up to

4 weeks of antibiotics. The primary analyses were performed based on the

conservative assumption of 4 weeks of antibiotic therapy, such that all peritonitis

episodes occurring within 60 days of a previous episode were excluded. In order to

ensure that the assumption about the antibiotic duration did not affect the results, we

performed a sensitivity analysis, with repetition of the analyses after exclusion of

peritonitis episodes occurring within 45 days of a prior episode (assuming 2 weeks of

antibiotic therapy).

3.5 Statistical Analyses

Continuous variables were reported as mean ± SD. Two models were used to assess

the predictors of peritonitis. In the first, potential predictors were tested using a

multivariable negative binomial model that modeled the number of peritonitis

episodes per patient, using the duration of follow up as an offset variable. In the

32

second model, peritonitis outcome was reported as the time to each peritonitis event,

and analyzed using an Andersen­Gill model for ordered multiple events. This model

allowed information on all events to be included with the assumption that each event

was independent. A priori selected variables for inclusion as covariates included age,

gender, race, diabetic status, GN as a cause of ESRD, modality before PD start (new

to dialysis, transfer from HD, failed transplant) and PD modality (CAPD vs. APD).

To assess whether the era of PD initiation influenced the relationship between each

variable and peritonitis, we tested an interaction term between era and each variable

as a method of initial screening. If the interaction was found to be statistically

significant, then subsequent analyses were performed to determine the relationship

between that variable and peritonitis in each of the two eras. Statistical significance

was defined as a p value of <0.05. All statistical analyses were performed using SAS

(version 9.1).

33

4. RESULTS

4.1 Patient cohort

The entire cohort consisted of 6,544 patients, including 4,247 incident patients in

whom data were collected prospectively and 2,297 prevalent patients in whom data

were retrospectively entered. After exclusion of prevalent patients, the study sample

consisted of 4,247 incident PD patients, of whom 1,605 had 3,058 episodes of

peritonitis. The remaining 2,642 patients had no peritonitis episodes. Of the 3,058

peritonitis episodes, 503 were excluded as they occurred within 60 days of a prior

episode and were assumed to be recurrent or relapsing events. Consequently, the

analyses included data for 2,555 peritonitis episodes seen amongst 4,247 patients

with a total of 7,319 years of follow­up. A flow diagram of the patient cohort included

in the analyses is illustrated in Figure 2. The distribution of peritonitis episodes within

the cohort is shown in Table 3. Eight provinces contributed data to the POET

database, with the largest number of patients coming from Ontario. The distribution of

patients by province is shown in Figure 3.

When all 3,058 peritonitis episodes were counted, the overall peritonitis rate was 1

episode in 26 patient­months on PD. This decreased to 1 episode in 33 patient­

months after exclusion of recurrent or relapsing events. The median time on PD was

1.37 years with an interquartile range of 0.62 to 2.43 years. Of the 4,247 patients

included in the study, 1,445 (34.0%) were still being followed at the end of the data

34

collection period (median follow­up time 2 years), 18.4% of patients in the cohort died

after a median time on PD of 1.31 years, 27.2% transferred to HD after a median

time on PD of 0.93 years, and 12.2% received a renal transplant after a median time

on PD of 1.21 years.

Demographic characteristics of the patients are presented in Table 4. A comparison

between the Canadian POET cohort and prevalent Canadian PD patients from the

2006 CORR data is shown in Table 5 (1).

4.2 Independent predictors of PD peritonitis

For the analysis in which peritonitis was modeled as a count, the negative binomial

model was used. In the multivariable negative binomial model, variables

independently associated with a higher peritonitis rate included age (rate ratio (RR)

1.04 per decade increase, 95% confidence interval (CI) 1.01­1.08, p=0.010), Black

race (RR 1.37, 95% CI 1.00­1.88, p=0.05) and transfer from HD to PD (RR 1.24, 95%

CI 1.11 ­1.38, p<0.001). Predictors of a lower peritonitis rate included having GN as

the cause of ESRD (RR 0.87, 95% CI 0.75­1.00, p=0.05) (Table 6). An interaction

between gender and diabetes was identified and is reported in detail below (section

4.3).

The relationship between use of CAPD vs. APD and peritonitis was assessed in the

subset of 3,180 patients who did not switch submodalities during their time on PD

35

(Table 7). In this subgroup, CAPD was not associated with a higher peritonitis rate

than APD (RR 1.03, 95% CI 0.91­1.16, p=0.66).

Using the multivariable Andersen­Gill Cox model, variables associated with a shorter

time to peritonitis included age (HR 1.03 per decade increase, 95% CI 1.01­1.06,

p=0.025), Black race (HR 1.47, 95% CI 1.15­1.88, p=0.002) and transfer from HD to

PD (HR 1.24, 95% CI 1.13­1.35, p<0.001). Having GN as the cause of ESRD was

associated with a longer time to peritonitis (HR 0.86, 95% CI 0.76­0.97, p=0.015)

(Table 8). Among the 3,180 patients with no submodality switch, use of CAPD was

not associated with a shorter time to peritonitis than use of APD (HR 1.02, 95% CI

0.92­1.13, p=0.69) (Table 9).

4.3 Interactions

In both analyses, a significant interaction between gender and diabetes was seen

(p=0.011 in the negative binomial analysis and p = 0.002 in the Andersen­Gill model).

When the association between diabetes and peritonitis by gender was assessed, it

was found that female diabetics were at increased risk of peritonitis (RR 1.27, 95% CI

1.10­1.47, p=0.001), while male diabetics were not (RR 0.99, 95% CI 0.87­1.13,

p=0.88) (Table 10).

36

4.4 Era effect

Initial screening for an era effect for each of the variables revealed that the only

significant interaction was for the relationship between age and era (p=0.001) (Table

11). Because of the presence of an interaction, the relationship between age and

peritonitis in each era was assessed (Table 12). In this analysis, it was found that the

higher peritonitis risk associated with increasing age in the overall analysis was

entirely accounted for by those initiating dialysis prior to the year 2001 (RR 1.11

between 1996 and 2000, 95% CI 1.06­1.17, p<0.001), with no relationship between

age and peritonitis thereafter (RR 1.00 between 2001 and 2005, 95% CI 0.95­1.04,

p=0.83) (114).

4.5 Comparison of peritonitis modeling strategies

The relationship between peritonitis and each of the variables in the multivariable

model was tested in both a negative binomial “rate” model and an Andersen­Gill time­

to­event model. These results are compared in Table 13. The two models yielded

similar predictors of peritonitis, and with comparable estimates of risk.

37

4.6 Sensitivity analysis for peritonitis relapse/recurrence exclusion criteria

For the main analyses, any peritonitis episode occurring within 60 days of a prior

episode was excluded as a recurrent or relapsing episode based on the assumption

of a maximum of 4 weeks of antibiotic therapy. Since some patients were likely to

have received shorter courses of antibiotics, we repeated the analyses after

exclusion of any peritonitis episode occurring within 45 days of a prior episode in

order to exclude any bias introduced by this assumption. In these analyses, the

results did not change appreciably, with identification of similar predictors of

peritonitis and similar risk estimates in both the negative binomial model and the

Andersen­Gill model (Tables 14­17).

38

5. DISCUSSION

5.1 General discussion

Using a large multicentre database of Canadian patients initiating PD between 1996

and 2005, the work contained within this thesis highlights several novel and

confirmatory findings. Predictors of PD peritonitis identified in this study included

Black race, transfer from HD to PD, and being a female diabetic. Increasing age was

only associated with an increased risk of peritonitis among those initiating PD before

the year 2001. In contrast to several prior studies, we found that choice of CAPD vs.

APD did not influence the peritonitis risk. Furthermore, these results were similar

regardless of modeling strategy, suggesting that both rate analyses and time­to­event

analyses are comparable analytic tools for studying the occurrence of PD peritonitis.

Prior to this study using the POET database, the two largest observational studies to

have looked at variables associated with peritonitis utilized the USRDS database (79)

and the ANZDATA registry (80). The former analysis included 11,975 American

patients on PD between 1994 and 1997. Covariates included age, gender, race,

cause of ESRD, comorbidities, PD submodality, number of entry­period

hospitalization days, entry­period hematocrit and peritonitis during the entry period.

Unfortunately, as a result of the method of data collection, patients who did not

survive their first 9 months on PD were excluded, as were those with secondary­pay

Medicare insurance patients or those insured by health maintenance organizations.

39

Furthermore, peritonitis episodes occurring in the first 3 months on PD were not

captured, nor could the database capture whether a patient had one or more

peritonitis episodes during the 6­month entry period. The ANZDATA analysis, which

included data on 3,162 patients from Australia and New Zealand commencing PD

between 1999 and 2003, was more comprehensive in that it was able to capture data

on all new PD starts from the time of PD initiation. Covariates included age, gender,

race, comorbidities, body mass index, timing of nephrology referral and peritoneal

transport status. Similar to the ANZDATA registry, advantages of the POET database

include the multi­center nature of the database, the inclusion of a relatively

contemporary PD cohort and the availability of data from the first day of initiation of

PD.

Among the variables that have been linked to peritonitis, the data on age have been

conflicting (79, 80, 82, 97, 99­103). As discussed, several factors may be responsible

for this variability. Firstly, different results may reflect the varying age cutoffs used to

define ‘elderly’ in the studies. Secondly, many of the studies that have looked at the

effect of age on peritonitis have been small, single­center studies with limited

statistical power. Thirdly, the era in which the patients received dialysis is quite

variable, with some studies reporting on patients who were on PD in the late 1980s,

and others reporting on more contemporary PD cohorts. The importance of the latter

issue relates to the major advances in PD connectology (24, 45­48, 115) and exit site

care (30, 51­53) that occurred over this time period. While increasing age was

associated with a higher peritonitis rate in our overall analysis, we have identified an

40

era effect for age, such that increasing age is only associated with peritonitis among

those who initiated PD before the year 2001. The lack of association between

increasing age and peritonitis in recent years may reflect the fact that the “flush

before fill” technique and the use of topical antibacterial agents provide an added

‘safety net’ to contamination in elderly patients who may have impaired vision or

dexterity. Importantly, there was no era effect for any of the other predictor variables,

suggesting that their association with peritonitis is not related to the year in which the

patient initiated PD.

The finding that Black race is associated with a greater risk of peritonitis is consistent

with previous American studies (55, 79). The basis for the increased risk is unclear,

but could relate to genetic differences or to socioeconomic factors that are not

captured in most large databases. While the higher peritonitis rate among African

American patients has contributed to increased technique failure rates (116), the

survival of Black patients on PD remains at least as good as that of Caucasian

patients (116, 117).

The increased peritonitis rate associated with transfer from HD to PD has not been

previously reported. We hypothesize that this increased risk may be attributable to

two high risk groups. The first group includes those who were ‘crash starts’ on HD

with little pre­dialysis care and subsequently chose to transfer to PD. These patients

would likely be sicker, with poorer nutritional status, a greater degree of inflammation,

more rapid loss of RRF and an increased susceptibility to infection and adverse

41

outcomes (118­121). The second group includes those who had been on HD for

years and exhausted all vascular access options. For the latter group, the lack of

RRF at the time of transfer to PD may contribute to their peritonitis risk given that loss

of RRF is an independent predictor of peritonitis (82). Since we do not have

information on dialysis vintage prior to transfer, we cannot determine with certainty

which group of patients accounted for the increased peritonitis risk. Nevertheless,

physicians caring for PD patients should be aware of the higher peritonitis rate

among those transferring from HD.

It is not surprising that diabetes has been previously reported to be associated with a

higher peritonitis rate (79, 81, 82) as it is known that diabetic patients with renal

disease are at higher risk of infection in general (35, 83). However, in this study, we

found for the first time a significant interaction between gender and diabetes, such

that the higher peritonitis rate was present only among female diabetics. This is of

particular interest as several large US studies have demonstrated a higher incidence

of death on PD among women, in particular among female diabetic patients (7, 11,

15). In one study using USRDS data, Bloembergen et al noted a differential effect of

gender on PD outcomes, with women at significantly higher risk of death due to

infection than men (11, 122). In a subsequent comparison of PD and HD outcomes

by Vonesh et al, female diabetic patients were one of the few subgroups in which PD

was associated with a higher risk of death than HD (15). Furthermore, Collins et al

reported a higher risk of all­cause death for female diabetics ≥ 55 years of age on PD

as compared with HD (7). In cause­specific analyses in the latter study, it was found

42

that these patients had a significantly higher risk of infectious death on PD. A smaller

single center study subsequently reported that infection was the second leading

cause of death among older diabetic women on PD (123). Our finding that female

diabetics have the highest peritonitis rates therefore suggests that the higher risk of

infection­related death in this group may be mediated in part through a higher risk of

PD peritonitis. While the basis for the increased peritonitis risk among female

diabetics requires further study, loss of RRF may play a role, as it has been shown

that diabetes is associated with a greater decline in RRF (124­127). Furthermore,

there is one study demonstrating a more rapid loss of residual kidney function among

female dialysis patients (124), although the data on gender and RRF are conflicting

(124­127).

With regard to the lower peritonitis rate among patients with GN as the cause of

ESRD, the results are not surprising. While use of immunosuppressive agents in this

subgroup of patients may increase infection risk (86), these patients tend to be

younger and have fewer comorbidities. Since we were not able to adjust for

comorbidities other than diabetes, the reduced peritonitis risk among patients with

glomerulonephritis is likely due to residual confounding.

While the majority of patients initiate PD either as their initial modality or after transfer

from HD, some patients start PD after failure of their renal transplant. There are data

to suggest that those who return to dialysis after graft loss are at increased risk of

adverse outcomes, including death (87, 128) and peritonitis (86­88). Other studies

43

have refuted these findings (89, 90). The theoretical basis for an increased peritonitis

risk includes the use of immunosuppressive therapy, and the long renal disease

vintage in the majority of these patients. While having a failed transplant was not

associated with an increased peritonitis risk in our analysis, this group accounted

only for 3% of PD starts so that this dataset was likely underpowered to detect a

difference, should it have been present.

Several previous studies have addressed the issue of whether the use of CAPD vs.

APD has an effect on peritonitis risk. The majority of studies have found that CAPD is

associated with a higher risk of peritonitis (97, 100, 104), including the only RCT to

have studied the relationship between modality and peritonitis risk (104). However,

there are also observational data to suggest a lower risk of peritonitis on CAPD

relative to APD (79), or no difference between the two submodalities in terms of

peritonitis risk (105). The apparent inconsistency among these studies may relate in

part to the fact that some of the studies in which CAPD was associated with a higher

peritonitis rate included patients who were on PD before the adoption of the improved

PD connectology systems, which greatly reduced the risk of contamination at the

time of an exchange. In our study, which included a larger and more contemporary

cohort of patients than in most of the previous studies, there was no association

between peritonitis and the use of CAPD vs. APD. These data support the generally

accepted practice of having the choice between CAPD and APD guided by patient

preference if the patient is capable of performing both modalities.

44

With regard to the optimal modeling approach to study the occurrence of peritonitis,

there are little comparative data. Most studies have reported either peritonitis rates or

time to first peritonitis. Two studies have compared modeling using a negative

binomial ‘rate’ model and a time­to­first peritonitis analysis, with both demonstrating

similar predictors (80, 112). However, one of the limitations of the time to peritonitis

analyses reported to date is that all studies using this type of modeling have only

incorporated time from initiation of dialysis until the first peritonitis episode. In our

analyses, we have used an Andersen­Gill model which allows for modeling of time to

peritonitis with the incorporation of all events occurring in each patient. Using this

type of modeling, information on all peritonitis episodes can be included. Based on

the similar results between the rate and time­to­event analyses in our study, we

conclude that both are appropriate analytic methods in the assessment of factors

related to peritonitis.

5.2 Impact on nephrology practice

While peritonitis rates among PD patients have declined over time, the occurrence of

peritonitis remains a major concern for both patients considering PD as well as the

nephrologists looking after them. The inconsistent data on the relationship between

increasing age and peritonitis may be a contributing factor in regard to the concern

over offering PD to older patients (96). The data presented in this thesis suggest that

among a contemporary cohort of PD patients, peritonitis risk does not increase with

45

increasing age and should therefore not be a limiting factor in the selection of PD as

a dialysis modality.

Furthermore, we have shown in this observational study that peritonitis risk is similar

between CAPD and APD. In the absence of strong evidence suggesting a difference

in occurrence of peritonitis between CAPD and APD, peritonitis risk should not

contribute to the decision­making when selecting between these submodalities.

Finally, we have for the first time identified diabetes among women and transfer from

HD to PD as predictors of peritonitis. While these do not represent modifiable risk

factors, an awareness of the increased risk in these patients should heighten the

vigilance among the members of treating team.

5.3 Limitations

Our study has several limitations. As with most large datasets, the data have not

been validated against patient charts. Since this was a clinical database, the data

entry was performed by the PD nurses and not trained data collectors. As such, the

accuracy and reproducibility of the data entry cannot be verified. The variables most

likely to be entered accurately include basic demographics such as age, race and

gender, as well as easy to identify comorbidities such as diabetes, and the well­

defined outcome of peritonitis. Some comorbidity data such as the presence of

cardiovascular disease or lung disease are more difficult to define, and hence may be

46

less accurately reported or under­reported. As a result, for the purpose of our

analyses, we elected to study only those variables that were most likely to have

complete and accurate data entry.

In order to reduce some of the bias inherent in observational data, we studied only a

sub­population of the entire cohort in the database. Specifically, we chose to include

only those patients in whom data were prospectively entered in order to avoid bias

related to retrospective data entry. We also chose to exclude prevalent patients as

inclusion of these patients in the analysis might have introduced a survivor bias.

With regard to the data source, we cannot exclude the possibility of a selection bias

in that the 25 centers included in the database had to have met the following criteria:

(i) were among the 85% of Canadian centers using Baxter PD products, (ii) agreed to

use the POET software for data collection, and (iii) had consistent data entry for a

minimum of 20 patients. Despite this limitation, the database included centers of

varying sizes in both academic and community hospitals with good representation

from almost all Canadian provinces.

While the multivariable regression models incorporated several potentially important

variables, as with many database studies, the covariates were limited to those for

which data were available. We acknowledge that there are important variables for

which we did not have information. Firstly, we did not have data on biochemical

parameters such as serum albumin and RRF ­ both of which have been linked with

47

peritonitis risk. In addition, we did not have data on dialysis vintage. This would have

been particularly relevant among patients who transferred from HD in order to

distinguish whether the increased peritonitis risk was largely attributed to ‘crash

starts’ on dialysis or long term HD patients. We also did not have information on S.

aureus nasal carriage, or on use of prophylactic ointments that are known to reduce

peritonitis risk. Other important variables that could not be adjusted for included those

that pertain to socioeconomic status, as these might have influenced peritonitis risk.

Finally, while the POET database provided information on race, there was no

separate category for Aboriginal race. This would have been of interest, as this is a

highly prevalent population in several Canadian provinces, with significant differences

in demographics and comorbidities, and increased frequency of technique failure

(129). While large multicenter datasets have the advantage of greater power to

detect clinically meaningful associations, their limited ability to adjust for all potentially

important variables leads to the inevitable possibility of residual confounding.

Since we did not have detailed information on all switches between CAPD and APD

during a patient’s time on PD, we tested the association between the PD modality

and peritonitis by performing the analysis in a subgroup of patients who did not

switch between CAPD and APD during their time on PD. Despite this, the number of

patients in this subgroup was still larger that the majority of studies that have tested

this association. While these data are reassuring, it is important to note that there are

many factors that influence the choice of CAPD vs. APD. While we adjusted for basic

patient demographics and diabetic status, we did not adjust for other comorbidities

48

that may have differed between the patient groups, nor could we adjust for non­

medical factors contributing to modality selection. As a result, we cannot exclude the

possibility of residual confounding due to variables that were not included in our

model.

5.4 Conclusions

In conclusion, our study has, for the first time, identified transfer from HD to PD as an

independent risk factor for PD peritonitis. In addition, there was an interaction

between gender and diabetes such that a higher peritonitis risk was only seen among

female diabetics. In contrast to previous studies, the choice of CAPD vs. APD did not

affect the risk of peritonitis. We have also found that age is not a risk factor for

peritonitis in a contemporary cohort of PD patients. Finally, we have demonstrated

that rate analyses and time­to­event analyses are both appropriate analytic tools to

study the occurrence of PD peritonitis.

5.5 Future directions

In light of the newly identified predictors of peritonitis, future studies can be directed

at trying to understand the basis for the increased risk and potential strategies to

reduce risk in these patients. For example, the finding that patients transferring from

HD to PD are at increased risk of peritonitis should be explored further in order to

49

determine whether those at increased risk are ‘crash starts’ on HD or those with a

long dialysis vintage, and whether more extensive training among these patients

might be warranted. With respect to the consistent association between female

diabetics and adverse outcomes, including increased peritonitis risk, future studies

are needed to explore the basis for this increased risk, including whether hormonal

mediators, residual renal function or other factors are responsible for this important

finding. Given the increasing incidence of ESRD among Canadian Aboriginal patients

and their higher risk of PD technique failure, further study is warranted to examine

the risk of PD­related infectious complications in this population. Finally, the POET

database can also be used to further explore other aspects of PD peritonitis risk,

including the relationship between PD catheter type and peritonitis, and the

microbiology and outcomes of infectious complications.

50

6. ILLUSTRATIONS

6.1 Tables

6.2 Figures

51

Table 1. Summary of studies testing the association between age and peritonitis

AUTHOR

(reference)

DATA

SOURCE

# OF

PTS

ERA Definition

of age

Statistical

analysis

RESULTS

Oo (79) USA

(USRDS)

11,975 1994­1997 0­44

45­64

65­74

≥75

Cox PHM 0­44: HR 1.09 (1.01­1.19)

45­64: HR 1.01 (0.94­1.09)

65­74: reference group

≥75: 1.07 (0.99­1.16)

Lim (80) Australia

and New

Zealand

(ANZDATA)

3,162 1999­2003 0­24

25­44

45­64

≥65

NBM

Cox PHM

0­24: RR 0.9 (0.66­1.22)

25­44: RR 0.83 (0.70­1.00)

45­64: RR 0.88 (0.77­1.01)

≥65: reference group

Rodriguez­ Carmona (97)

Spain 328 1989­1998 Continuous

variable

Rate model (not specified)

Increased risk of 0.005 episodes

per patient­year per year older

Han (82) Korea 204 2000­2005 Continuous

variable

Cox PHM No effect of age (HR 0.99, 95% CI

0.98­1.01)

Huang (100) Taiwan 177 1993­2000 Continuous

variable

Cox PHM No effect of age (HR 1.00, p=0.94)

De Vecchi (99) Italy 156 1985­1995 ≥70 vs 40­60t­test 0.52 vs. 0.37 episodes/patient­

year (p<0.002)

Kadambi (101) USA 493 1994­2000 <50

50­64

≥65

ANOVA No difference (p=NS)

Li (103) Hong Kong 328 2000­2004 ≥65 vs <65 Life­table

analysis

No difference (p=0.75)

Holley (102) USA 206 1979­1992 ≥60 vs 18­49Poisson

model

No difference (p=NS)

Perez­

Contreras (98)

Spain 381 1993­1996 ≥65 vs <65 t­test 0.72 vs. 0.55 episodes/patient­

year (p=0.01)

PHM = proportional hazards model, NBM = negative binomial model, ANOVA =

analysis of variance

52

Table 2. Summary of studies testing the association between CAPD vs. APD and peritonitis

AUTHOR Country # OF PTS

ERA STATISTICAL ANALYSIS

RESULTS

De Fijter (104) Holland 97 1988­1991 t­test Life­table analysis

APD better: RR 0.54 (p=0.03) 11 vs. 18 months to first peritonitis (p=0.06)

Oo (79) USA (USRDS)11,975 1994­1997 Cox PHM CAPD better: HR 0.94 (p=0.008)

Kavanagh (105) Scotland (Scottish Renal Registry)

1,205 1999­2002 Poisson model No difference (p=0.21)

Rodriguez­ Carmona (97)

Spain 328 1989­1998 Rate model (not

specified)

APD better: CAPD associated with excess of 0.2 episodes/patient­year (p=0.008)

Huang (100) Taiwan 177 1993­2000 Cox PHM APD better: HR 0.58, p=0.05

53

Table 3. Distribution of peritonitis episodes within the patient cohort

Number of peritonitis episodes

Number of patients (total n=4,247)

Percentage of patients

0 2642 62.21% 1 1047 24.65% 2 327 7.70% 3 141 3.32% 4 52 1.22% 5 23 0.54% 6 8 0.19% 7 2 0.05% 8 0 0 9 4 0.09% 10 1 0.02%

54

Table 4. Baseline demographic characteristics for the entire patient cohort

n=4,247

Age (mean, years) 59 ± 16 Gender (% male) 55% Race (%)

Caucasian Black Asian Other

82% 2% 6% 10%

Modality (% on CAPD) Initial Most recent

74% 52%

Modality before PD start (%): New to dialysis Transfer from HD Failed transplant Other/unknown

58% 24% 3% 15%

Cause of ESRD: Diabetes Mellitus Hypertension Glomerulonephritis Cystic kidney disease Other

35% 17% 15% 5% 27%

Diabetic 40%

CAPD = continuous ambulatory peritoneal dialysis, PD = peritoneal dialysis, HD = hemodialysis, ESRD = end­stage renal disease

55

Table 5. Comparison of patient demographics between POET and CORR

POET database

Study patients 1996­2005

CORR data

Prevalent patients 2006

Age (mean, years) 0­19 20­44 45­64 65­74 75+

1% 19% 38% 24% 17%

1% 15% 39% 25% 20%

Gender (% male) 55% 56% Cause of ESRD:

Diabetes Mellitus Hypertension Glomerulonephritis Cystic kidney disease Other

35% 17% 15% 5% 27%

31% 18% 19% 6% 26%

CORR = Canadian organ replacement register, ESRD = end­stage renal disease

56

Table 6. Multivariable negative binomial model for the outcome of peritonitis

n = 4,247 patients NEGATIVE BINOMIAL MODEL

Rate ratio 95% CI p value Age (per decade increase) 1.04 1.01­1.08 0.010

Black 1.37 1.00­1.88 0.05

Asian 0.89 0.74­1.08 0.24

Diabetes female male

1.27 0.99

1.10­1.47 0.87­1.13

0.001 0.88

Glomerulonephritis 0.87 0.75­1.00 0.05

Transfer from HD 1.24 1.11­1.38 <0.001

Failed transplant 1.27 0.95­1.69 0.12

HD = hemodialysis, CI = confidence interval

57

Table 7. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch

n = 3,180 patients NEGATIVE BINOMIAL MODEL

Rate ratio 95% CI p value Age (per decade increase) 1.04 1.00­1.08 0.034

Black 1.26 0.87­1.82 0.24

Asian 0.88 0.70­1.10 0.26

Diabetes female male

1.31 0.95

1.11­1.54 0.81­1.11

0.001 0.53

Glomerulonephritis 0.90 0.76­1.07 0.24

Transfer from HD 1.31 1.15­1.49 <0.001

Failed transplant 1.12 0.77­1.63 0.57

CAPD vs. APD 1.03 0.91­1.16 0.66

CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, HD = hemodialysis, CI = confidence interval

58

Table 8. Multivariable Andersen­Gill model for the outcome of peritonitis

n = 4,247 patients ANDERSEN­GILL MODEL

Hazard ratio

95% CI p value

Age (per decade) 1.03 1.01­1.06 0.025

Black 1.47 1.15­1.88 0.002

Asian 0.91 0.78­1.06 0.23

Diabetes female male

1.31 1.02

1.17­1.48 0.91­1.14

<0.001 0.75

Glomerulonephritis 0.86 0.76­0.97 0.015

Transfer from HD 1.24 1.13­1.35 <0.001

Failed transplant 1.18 0.93­1.49 0.17

HD = hemodialysis, CI = confidence interval

59

Table 9. Multivariable Andersen­Gill model for the outcome of peritonitis in the subgroup of patients with no submodality switch

n = 3,180 patients ANDERSEN­GILL MODEL

Hazard ratio

95% CI p value

Age (per decade) 1.03 1.00­1.06 0.06

Black 1.34 1.00­1.81 0.05

Asian 0.91 0.75­1.11 0.36

Diabetes female male

1.37 0.98

1.19­1.57 0.85­1.12

<0.001 0.73

Glomerulonephritis 0.88 0.76­1.02 0.09

Transfer from HD 1.29 1.16­1.44 <0.001

Failed transplant 0.96 0.70­1.32 0.81

CAPD vs. APD 1.02 0.92­1.13 0.69

CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, HD = hemodialysis, CI = confidence interval

60

Table 10. Association between diabetes and peritonitis by gender

n = 4,247 patients NEGATIVE BINOMIAL MODEL ANDERSEN­GILL MODEL

Rate ratio 95% CI p value Hazard ratio

95% CI p value

Diabetes female male

1.27 0.99

1.10­1.47 0.87­1.13

0.001 0.88

1.31 1.02

1.17­1.48 0.91­1.14

<0.001 0.75

CI = confidence interval

61

Table 11. Interaction between each variable and era in the multivariable negative binomial model

INTERACTION TERM WITH ERA p value

Age 0.001

Black 0.28

Asian 0.85

Diabetes x Gender 0.58

Glomerulonephritis 0.87

Transfer from HD 0.41

Failed transplant 0.33

*CAPD vs. APD 0.24

*subgroup of 3,180 patients who did not switch between CAPD and APD during their time on PD

HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis

62

Table 12. Association between age and peritonitis by era.

N = 4,247 patients NEGATIVE BINOMIAL MODEL

ANDERSEN­GILL MODEL

Rate ratio

95% CI p value Hazard ratio

95% CI p value

OVERALL (n=4,247 patients) 1.04 1.01­1.08 0.010 1.03 1.01­1.06 0.025

1996­2000 (n=1,494 patients) 1.11 1.06­1.17 <0.001 1.08 1.04­1.23 <0.001 2001­2005 (n=2,753 patients) 1.00 0.95­1.04 0.83 0.99 0.95­1.03 0.61

CI = confidence interval

63

Table 13. Comparison of results of multivariable negative binomial model and Andersen­Gill model for peritonitis

N = 4,247 patients NEGATIVE BINOMIAL MODEL

ANDERSEN­GILL MODEL

Rate ratio

95% CI p value Hazard ratio

95% CI p value

Age (per decade) 1.04 1.01­1.08 0.010 1.03 1.01­1.06 0.025

Black 1.37 1.00­1.88 0.05 1.47 1.15­1.88 0.002

Asian 0.89 0.74­1.08 0.24 0.91 0.78­1.06 0.23

Diabetes female male

1.27 0.99

1.10­1.47 0.87­1.13

0.001 0.88

1.31 1.02

1.17­1.48 0.91­1.14

<0.001 0.75

Glomerulonephritis 0.87 0.75­1.00 0.05 0.86 0.76­0.97 0.015

Transfer from HD 1.24 1.11­1.38 <0.001 1.24 1.13­1.35 <0.001

Failed transplant 1.27 0.95­1.69 0.12 1.18 0.93­1.49 0.17

*CAPD vs. APD 1.03 0.91­1.16 0.65 1.02 0.92­1.13 0.69

*subgroup of 3,180 patients who did not switch between CAPD and APD during their time on PD

HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval

64

Table 14. Multivariable negative binomial model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)

n = 4,247 patients NEGATIVE BINOMIAL MODEL

Rate ratio 95% CI p value Age (per decade increase) 1.04 1.01­1.08 0.009

Black 1.36 0.98­1.89 0.06

Asian 0.89 0.74­1.08 0.26

Diabetes female male

1.28 0.98

1.10­1.48 0.86­1.12

<0.001 0.77

Glomerulonephritis 0.87 0.75­1.00 0.05

Transfer from HD 1.25 1.12­1.40 <0.001

Failed transplant 1.32 0.98­1.77 0.06

HD = hemodialysis, CI = confidence interval

65

Table 15. Multivariable negative binomial model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)

n = 3,180 patients NEGATIVE BINOMIAL MODEL

Rate ratio 95% CI p value Age (per decade increase) 1.04 1.00­1.08 0.040

Black 1.23 0.84­1.81 0.29

Asian 0.87 0.69­1.10 0.24

Diabetes female male

1.32 0.94

1.11­1.56 0.80­1.10

0.001 0.43

Glomerulonephritis 0.90 0.76­1.07 0.24

Transfer from HD 1.33 1.17­1.52 <0.001

Failed transplant 1.17 0.80­1.70 0.43

CAPD vs. APD 1.02 0.91­1.15 0.71

HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval

66

Table 16. Multivariable Andersen­Gill model for the outcome of peritonitis (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)

n = 4,247 patients ANDERSEN­GILL MODEL

Hazard ratio

95% CI p value

Age (per decade) 1.03 1.01­1.06 0.020

Black 1.48 1.16­1.88 0.001

Asian 0.90 0.77­1.06 0.20

Diabetes female male

1.33 1.01

1.19­1.49 0.91­1.13

<0.001 0.80

Glomerulonephritis 0.86 0.77­0.97 0.015

Transfer from HD 1.24 1.14­1.36 <0.001

Failed transplant 1.21 0.97­1.52 0.10

HD = hemodialysis, CI = confidence interval

67

Table 17. Multivariable Andersen­Gill model for the outcome of peritonitis in the subgroup of patients with no submodality switch (sensitivity analysis using 45 day relapse/recurrence exclusion criteria)

n = 3,180 patients ANDERSEN­GILL MODEL

Hazard ratio

95% CI p value

Age (per decade) 1.03 1.00­1.06 0.09

Black 1.33 1.00­1.79 0.05

Asian 0.91 0.76­1.10 0.33

Diabetes female male

1.37 0.98

1.19­1.57 0.85­1.12

<0.001 0.63

Glomerulonephritis 0.88 0.76­1.01 0.07

Transfer from HD 1.31 1.18­1.46 <0.001

Failed transplant 0.98 0.72­1.33 0.91

CAPD vs. APD 1.02 0.93­1.13 0.69

HD = hemodialysis, CAPD = continuous ambulatory peritoneal dialysis, APD = automated peritoneal dialysis, CI = confidence interval

68

6.2 Figures

Figure 1. Illustration of intraluminal (dashed arrow) and periluminal (solid arrow) entry of organisms into the peritoneal cavity

Peritoneum

Peritoneal cavity

PD catheter

69

Figure 2. Flow diagram of patient cohort from POET database

2,297 prevalent patients excluded

4,247 incident patients

2,642 patients without peritonitis

6,544 total patients

1,605 patients with peritonitis (3,058 episodes)

2,642 patients without peritonitis

1,605 patients with peritonitis (2,555 episodes)

503 recurrent or relapsing episodes excluded

4,247 patients with 2,555 episodes of peritonitis

70

Figure 3. Distribution of Patients in POET Database by Province

Distribution of Patients in POET Database by Province

Ontario BC Manitoba Nova Scotia Quebec Alberta Saskatchewan Newfoundland

71

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8. APPENDICES

8.1 Appendix A: Peer reviewed publications arising from this work (with permission from Clin J Am Soc Nephrol)

(1) Nessim SJ, Bargman JM, Austin PC, Story K, Jassal SV. Impact of age on peritonitis in peritoneal dialysis patients: an era effect. Clin J Am Soc Nephrol 4(1): 135­41, 2009

(2) Nessim SJ, Bargman JM, Austin PC, Nisenbaum R, Jassal SV. Predictors of peritonitis among patients on peritoneal dialysis: results of a large, prospective Canadian database. Clin J Am Soc Nephrol epub ahead of print 2009 Apr 30.