PAGE 16 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS

PROJECT FOCUS RESEARCH & DEVELOPMENT

EMR as a highly powerful EuropeanRWD source for R&D

The authorAdeline Meilhoc, MSC is Vice President, RWE Solutions, IMS HealthAmeilhoc@fr.imshealth.com

The value of RWE to improve clinical trial operations and mitigate risk– a case study of leveraging EmR data in Europe.

As discussed in another article in this issue ofAccessPoint (see page 10), the benefits ofusing more robust insights from RWD includelower clinical development costs andavoidance of delays. The RWE-related solutionsall had a common theme of providing moreinformation about how the patientsexperienced healthcare: where they were,how they were diagnosed, how they weretreated and what outcomes they experienced. This article discusses a novel approach for supporting aclinical trial in Europe using EMR data to dramaticallyimprove a clinical trial process and outcome.

Caveat about real-world data sourcingA core belief about RWE in IMS Health is that the RWD usedshould best answer the question being asked. There is not asingle superior data source and there are always trade-offsbetween breadth and depth. But critical factors to address indesigning feasible clinical trials in Europe do nicely lendthemselves to EMR data. For example:

• Finding the right population of patients, especially interms of inclusion/exclusion criteria. EMR data providesthe clinical variables needed to assess how many of thosepatient groups actually exist.

• Evaluating the number of sites, helping weigh a trialapproach’s recruitment potential per site with otherfactors such as: KOL involvement; market penetrationand regulatory strategy; production & distribution chainconstraints. The EMR data can be looked at in aggregateto understand both the size of a site’s potentialpopulation as well as how it compares to other sites, toprovide a relative rating.

• Defining the right populations when the literature andKOLs do not agree. As manufacturers look to develop andlaunch more innovative drugs, often the broaderunderstanding of the disease is still evolving. EMR datacan provide a more objective view of patientcharacteristics associated with investigated conditions.

EMRs, especially when longitudinal data collection isutilized, are informing research questions along the entireproduct development continuum. RWD is used to support

DUS requirements to characterize the prescribing practicesof medicinal products during typical clinical use inrepresentative groups of physicians while assessing themain reasons for the prescription.

Common primary endpoints provided by EMRs are:

• Demographic and clinical characteristics of treatedpatients, including co-medication and co-morbidity

• Indication for which the product is prescribed in routineclinical practice

• Average duration of treatment episodes and the dailydoses prescribed according to the route of administration

Case in practice: Cardiovascular diseaseThe ability to determine requirements for clinical trials in aniche cardiovascular indication (statin intolerant) waschallenged by lack of consensus between experts and KOLsregarding the exact definition of this patient population. Ananalysis was therefore conducted using RWD datasets todetermine specific needs for the trials.

Methodology

RWD EMR databases covering the top 5 EU (France, UK,Italy, Germany, Spain) (see Table 1) were queried in a two-stage process to (1) determine the profile and numberof patients needed and (2) target and pre-select recruitmentsites. In France, the RWD sources included GPs and anadditional panel of cardiologists.

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Step 1

The first step had three key goals:

1. Characterize and quantify the number of patients to beincluded in the clinical trials (Figure 1)

2. Validate the patient recruitment hypothesis

3. Establish the best healthcare professional and site profile able to recruit such patients (GPs, specialists,hospitals, etc)

Step 2

The second step (Figure 2) was to target and pre-select high-potential sites to include in the clinical trials.

Figure 2: Number of patients per doctors

No

of d

octo

rs

No of patients

0

50

100

150

200

250

300

1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 30

Figure 1: Criteria applied to patient selection

Patient with at least 3 years of medical historical data

Patient treated at least 1 time with statin 2 years prior index date

Patient initiated with statin within 2 years prior index date

Patient who has stopped statin treatment for at least 6 months

Patient who presents at least 1 of the following factors in their medical history • Combination of at least 2 atherothrombotic risk factors• Cerebrovascular disease• Coronary disease• Symptomatic peripheral arterial disease

ConclusionThe use of RWD brought clarity around the statin-intolerantdefinition and allowed the inclusion/exclusion criteria to beframed. This provided an evidence base forrecommendations to enhance the clinical strategy andensure that the number of required sites to be involvedwould not fall short. The ability to achieve this is of majorimportance within the context of rising costs and limitedR&D resources and in avoiding unexpected requirements toboost patient recruitment or complete a rescue study.

Shrinking R&D budgets and challenges for funding the newdrug development process provide impetus to explore andutilize RWD as a source that is ripe for application tosupport the achievement of efficiency savings.

PAGE 18 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS

PROJECT FOCUS RESEARCH & DEVELOPMENTD

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Bio

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Med

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Dat

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Variable

Gender Yes Yes Yes Yes Yes

Year of birth Yes Yes Yes Yes Yes

Socio-economic status Partial Yes No No Partial

Ethnicity No Partial No No No

Death recording No Yes Partial No No

Registration date No Yes No No Yes

“Transferred out” date No Yes No No No

Diet Partial Partial Partial No No

Exercise No Partial Partial No No

Lifestyle No Partial Partial No No

Height Yes Yes Yes Yes Yes

Weight Yes Yes Yes Yes Yes

Blood pressure Yes Yes Yes Yes Yes

Date of events (consultation)

Yes Yes Yes Yes Yes

Home visit Partial Partial Partial No No

Risk factors Yes Yes Yes Yes Yes

Medical history Yes Yes Yes Yes Yes

Signs and symptoms Yes Yes Yes Yes Yes

Drug Yes Yes Yes Yes Yes

Diagnosis Yes Yes Yes Yes Yes

Duration of script Yes Yes Yes Yes Yes

Dosage Yes Yes Yes Yes Yes

Cost Yes Partial Yes Yes Yes

Reimbursement Yes No Yes Yes No

Generic name Yes Yes Yes Yes Yes

Prescription by brand name

YesDrug safety

Yes Yes Yes

Prescription by molecule

No Yes No No Yes

Repeat Yes Yes Yes Yes Yes

Allergies Yes Yes Yes Yes Yes

Immunization Yes Yes Yes Yes Yes

Lab & X Ray exams rx Yes Yes Yes Yes Yes

Lab & X Ray exams results

Yes Yes Yes Yes Yes

Referrals Partial Yes Partial Partial Yes

Hospitalization Partial Yes Partial Partial No

Reasons for hospitalization

Partial Partial Partial Partial No

FranceFranceFrance UKUKUK ItalyItalyItaly GermanyGermanyGermany SpainSpainSpain

Table 1: IMS Health RWD EMR in Top 5 EU – collected variables