PAGE 38 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS

INSIGHTS COMMERCIAL AND MARKET ACCESS

John Rigg, PHD Principal and Head of Predictive Analytics, RWE Solutions, IMS HealthJohn.Rigg@uk.imshealth.com

How predictive analytics can helpfind the rare disease patientEarly and accurate diagnosis of diseases is key to optimizingoutcomes but particularly challenging in rare disorders, whichoften go undetected for years. new tools leveraging real-worlddata and innovation in advanced analytics are creatingopportunities for dramatic improvements in identifying hard-to-findpatients. Pioneering examples in studies of a rare multi-systemdisease and a cardiovascular condition demonstrate excitingpotential, signaling a role for their use in broader strategies toaccelerate effective treatment.

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Although exact definitions vary, rare diseases are thoseaffecting a small percentage of people.1 In the EU this isconsidered to mean no more than 5 cases per 10,000individuals, and in the USA fewer than 200,000 individualsat any one time. But with somewhere in the region of 7,000rare diseases already identified, collectively their burden isconsiderable: there are now an estimated 350 millionsufferers worldwide – more than AIDS and cancer puttogether.2 And with, on average, five new rare diseasesbeing described in the medical literature each week,3

patient numbers continue to grow.

Often genetic and frequently chronic, life-threatening anddebilitating,1,3 most rare diseases cannot be cured.However, significant developments in precision medicinei

have seen notable breakthroughs in recent years, includinghighly targeted therapies addressing causal factors ratherthan symptoms alone. With more opportunities fortreatment and over 450 orphan drugsii in development, theoutlook for many individuals is increasingly hopeful.4

Yet even as progress continues, patients face tremendousbarriers in benefiting from these innovations. In particularthey struggle to obtain a timely and accurate diagnosis,which is difficult to deliver for the diverse range of raredisorders with widely varying signs and symptoms.1

Delays arise from misdiagnoses, multiple consultations andadministration of inappropriate interventions (Figure 1).

These delays all too often leave rare diseases undetecteduntil a stage when even exceptional treatments are lesseffective. Given the progressive nature of many rarediseases, the consequences of late diagnosis can beimmense and include physical deterioration, unnecessarystress and in some cases death.8 There are also significantfinancial implications, both for the individuals involved andsociety as a whole, reflecting the multiplicity of physicianvisits and greater use of costly diagnostic procedures.9

Furthermore, as observed by Kole and Faurisson,10 late-stage diagnosis can impede knowledge building in rarediseases, preventing improved understanding of the earlymanifestations of particular conditions.

As a key element of broader efforts to improve awarenessand management of rare diseases, solutions to enableearlier detection are imperative. In the words of one patientwho experienced a five-year delay in being diagnosed withsarcoidosis: “As for advice about getting a diagnosis, I wishI had some magic formula for others.”11 In fact, theopportunity to find one has never been better.

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Identifying patients to accelerate treatment

Diagnosis for Rare Diseases

Based on a European survey covering 8 rare diseases5

are initially misdiagnosed. This has resulted in

over 40%of patients

Patients visit an average of

7.3 physiciansprior to an accurate diagnosis.6 5.6 years6

7.6 years

A patient with a rare disease to receive the correct diagnosis

waits on average

30% of patients have received three or more misdiagnoses7

1 out of 6 patients going through surgery and

1 out of 10 patients receivingpsychiatric treatment

Figure 1: Delays in diagnosis for rare diseases

i The branch of medicine that enables healthcare services and treatment tailored to the specific genetic makeup of the individual (IMS HealthRWE Dictionary; http://rwedictionary.com/)

iI Medicinal products intended for diagnosis, prevention or treatment of life-threatening or debilitating rare diseases(http://www.eurordis.org/about-orphan-drugs)

PAGE 40 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS

INSIGHTS COMMERCIAL AND MARKET ACCESS

a groundbreaking, real-world approachAlongside the science that is revolutionizing the treatmentof rare diseases are two broader, parallel developments: theexpansion of real-world data (RWD) and innovation in theanalytical methods that can be used to interrogate the data.When brought together and supported by clinical insightthese allow for the development of screening algorithms,presenting a major opportunity to help detect new,undiagnosed patients.

1. Expansion of RWD: The exponential increase inelectronic healthcare information includes rich datafrom sources such as primary care Electronic MedicalRecords (EMRs), hospitals, claims information andpatient registries. Detailed RWD on symptomology,diagnoses, consultations, treatment history, lab tests,etc, is routinely collected from patients and anonymized.Coupled with the growing ability to integrate data fromdifferent sources, there is now an unparalleled datafoundation for finding undiagnosed patients.

2. Innovation in analytics: The field of advanced analyticshas evolved greatly in industries such as finance andconsumer goods. Sophisticated predictive techniques andalgorithms that revolutionized facial recognition systemsin on-line search engines are now helping to solvecomplex problems in healthcare. Machine learningiii

technology, for example, can be used to identifycomplex, subtle patterns in the data of diagnosedpatients to assist in detecting new sufferers of a disease.Pattern recognition techniques leveraging machinelearning have successfully found ‘the needle in thehaystack’ of undiagnosed patients with rare conditions.

Demonstrating dramatic improvements in detectionIMS Health has conducted pioneering research whichdemonstrates the power of RWD in helping to solve theproblem of under diagnosis in rare diseases. This isillustrated in the following two recent case studies: the firstshows how potentially undiagnosed patients can be found

using screening tools based on advanced analytics; thesecond how RWD can be used to identify potential healthsystem barriers to diagnosis. Both approaches arecomplementary, tackling patient-level and system-widechallenges respectively.

Case study 1Finding patients at high risk of a rare multi-systemdisease earlier Highly promising results from a recent study in a raremulti-system disease demonstrate the application of ascreening algorithm in the UK. Specifically, the focus wasto detect patients with this particular condition, which issubstantially under diagnosed and where up to 40% ofidentified patients are diagnosed late, often by decades.Potentially undiagnosed patients were identified fromroutinely collected primary care and administrative data byvirtue of advanced machine learning methods incorporatingclinical expertise which revealed patterns in the data thatwere predictive of disease presence.

The analysis was conducted in two stages leveraging de-identified EMRs. Firstly, analytics experts developed ascreening algorithm using classical statistical methodscombined with clinical expertise. Secondly, they appliedadvanced machine learning methods to refine and optimizethe algorithm.

A test (‘blind’) sample of 70,000 randomly selectedpatients was risk-scored by the initial algorithm withoutknowing which patients had a confirmed diagnosis for thedisease. This produced a high-risk group containing 8% ofconfirmed cases. The test sample was then risk scored bythe refined algorithm, exploiting machine learningtechniques. This produced a prevalence of the confirmeddiagnosis in the highest risk group of 20.5%. Given thatonly 0.7% of patients in the test sample actually had thedisease, the evidence suggests that the algorithm could beused to dramatically increase the odds of finding high-riskpatients earlier (Figure 2).

Confirmed Diagnosis

Potentially UndiagnosedCandidates For Screening

Potential Undiagnosed

Potential Diagnosisvia Algorithm

DiagnosedCases

50

50

39

84

45

Source: IMS Health

Figure 2: Potentially undiagnosed patients identified for screening

1008060

200

40

120140160180200220240

N

umbe

r of

Pat

ient

s

Center Carrying Out Diagnostic Procedure

Source: IMS Health

TertiaryCenter

1

TertiaryCenter

2

TertiaryCenter

3

TertiaryCenter

4

TertiaryCenter

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230

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Number of Patients Diagnosed by Center

Figure 3: Variation in disease incidence by tertiary center

iii A collection of advanced, data-driven statistical methods which can be used to identify complex patterns in data.

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Case study 2Identifying health system barriers causing under diagnosisA second case study is an analysis which was conducted todetermine, in a complex, multi-center diagnosis pathway,whether a lengthy diagnosis process could be a causalfactor in late presentations of a potentially fatal rarecardiac disease. If detected early, the condition could bereversible or manageable with treatment.

In a process involving literature and data profiling, a cohortselection algorithm was developed leveraging secondarycare data in a major EU country. The selected cohorttriangulated well with literature incidence and demographicvalues and enabled health service usage and diagnosispatterns to be investigated for a period of more than fiveyears (April 2009 to October 2014).

The analysis revealed a high number of events (21 for theaverage patient) for three years ahead of a formaldiagnosis, with over 90% of patients being known to thehospital system within the three-year time frame. It alsodemonstrated wide variation in the types of diagnosticpathways followed to reach a tertiary center initially.Patients were found to see on average three differenthospital centers in the three years pre-diagnosis and fivedifferent specialty types. Furthermore, the study identifiedsubstantial variability in the incidence rate per 100Kpopulation, being higher in regions feeding in to theleading diagnosis center (Figure 3). This suggestedchallenges of under diagnosis in other parts of the country.

Insights from this research were positively received byleading clinical experts in the field as a novel and previouslyunseen perspective on their patient population. The studygenerated hypotheses for further work and served as a basis

for building a rich pool of RWD to inform this therapy area,in association with academic and clinical institutions.

These studies illustrate the power of techniques enabled bythe use of RWD and analytics to facilitate broader efforts toreach patients suffering from a rare disease with treatmentthat could, potentially, be curative.

Impressive results but challenges remain

Evidence now exists to show how the application ofadvanced analytics to large-scale RWD can help identifyundiagnosed patients with rare diseases. These screeningalgorithms can form an important part of the portfolio ofstrategies to bring the right treatment, including innovativenew therapies, to patients with rare diseases.

The initial results are impressive: the growing availabilityof RWD creates a rich foundation for screening algorithmswhile developments in advanced machine learning andpredictive analytics, such as signal detection theory, enablethe distinction between ‘signal’ and ‘noise’ to makealgorithms accurate and cost-effective. However, there arechallenges to address before they can be employed to flaghigh-risk patients from medical records alone: patientconfidentiality has to be protected; underlying data must besufficiently broad to reach a critical mass of these hard-to-find patients; and clinicians must be willing to embraceresults from screening algorithms.

Nevertheless, if there is serious intent to developtreatments to improve the lives of patients with rarediseases, then there should be equally serious efforts tofind those patients. RWD and predictive analytics canundoubtedly play an important role in helping to achievethis goal.

1 What is a rare disease? EURORDIS. Rare Diseases Europe. http://www.eurordis.org/content/what-rare-disease2 Global Genes. Available at: https://globalgenes.org/who-we-are-2/ Accessed 6 Dec 20153 Medicines for rare diseases. European Medicines Agency.

http://www.ema.europa.eu/ema/index.jsp?curl=pages/special_topics/general/general_content_000034.jsp4 PhRMA. A Decade Of Innovation in Rare Diseases 2005-2015. PhRMA, 2015. Available at:

http://www.phrma.org/sites/default/files/pdf/PhRMA-Decade-of-Innovation-Rare-Diseases.pdf Accessed 6 December 20155 EURORDIS – Rare Diseases Europe. Survey of the delay in diagnosis for 8 rare diseases in Europe (‘EURORDISCARE2’). Available at:

www.eurordis.org/IMG/pdf/Fact_Sheet_Eurordiscare2.pdf6 Engel PA, Bagal S, Broback M, Boice N. Physician and patient perceptions regarding physician training in rare diseases: The need forstronger educational initiatives for physicians. Journal of Rare Disorders, 2013; 1(2): 1-15.http://www.journalofraredisorders.com/pub/IssuePDFs/Engel.pdf

7 Limb L, Nutt S, Sen A. Experiences of rare diseases: An insight from patients and families. Rare Disease UK. December, 2010. Available at:http://www.raredisease.org.uk/documents/RDUK-Family-Report.pdf Accessed 6 December, 2015

8 EURORDIS. Voice of 12,000 patients. Experiences and Expectations of Rare Disease Patients on Diagnosis and Care in Europe. A report basedon the EurordisCare2 and EurordisCare3 Surveys. EURORDIS, 2009. Available at: http://www.eurordis.org/publication/voice-12000-patients

9 Rare Disease Impact Report: Insights from patients and the medical community. Shire, April 2013. Available at:http://www.geneticalliance.org.uk/docs/e-update/rare-disease-impact-report.pdf and https://www.shire.com/newsroom/2013/april/shire-launches-report Accessed 6 December, 2015

10 Kole A, Faurisson F. Rare diseases social epidemiology: Analysis of inequalities. Advances in Experimental Medicine and Biology, 2010;686: 223-50

11 Inspire. The road to diagnosis: Stories from patients with rare diseases. 2011. https://www.inspire.com/static/inspire/reports/inspire-rare-disease-day-report-2011.pdf