A novel approach for supporting a clinical trial in Europe using EMR data

1. PAGE 16 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
PROJECT FOCUS RESEARCH & DEVELOPMENT
EMR as a highly powerful European
RWD source for R&D
The author
Adeline Meilhoc, MSC
is Vice President, RWE Solutions, IMS Health
Ameilhoc@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 of
AccessPoint (see page 10), the benefits of
using more robust insights from RWD include
lower clinical development costs and
avoidance of delays. The RWE-related solutions
all had a common theme of providing more
information about how the patients
experienced healthcare: where they were,
how they were diagnosed, how they were
treated and what outcomes they experienced.
This article discusses a novel approach for supporting a
clinical trial in Europe using EMR data to dramatically
improve a clinical trial process and outcome.
Caveat about real-world data sourcing
A core belief about RWE in IMS Health is that the RWD used
should best answer the question being asked. There is not a
single superior data source and there are always trade-offs
between breadth and depth. But critical factors to address in
designing feasible clinical trials in Europe do nicely lend
themselves to EMR data. For example:
• Finding the right population of patients, especially in
terms of inclusion/exclusion criteria. EMR data provides
the clinical variables needed to assess how many of those
patient groups actually exist.
• Evaluating the number of sites, helping weigh a trial
approach’s recruitment potential per site with other
factors such as: KOL involvement; market penetration
and regulatory strategy; production & distribution chain
constraints. The EMR data can be looked at in aggregate
to understand both the size of a site’s potential
population as well as how it compares to other sites, to
provide a relative rating.
• Defining the right populations when the literature and
KOLs do not agree. As manufacturers look to develop and
launch more innovative drugs, often the broader
understanding of the disease is still evolving. EMR data
can provide a more objective view of patient
characteristics associated with investigated conditions.
EMRs, especially when longitudinal data collection is
utilized, are informing research questions along the entire
product development continuum. RWD is used to support
DUS requirements to characterize the prescribing practices
of medicinal products during typical clinical use in
representative groups of physicians while assessing the
main reasons for the prescription.
Common primary endpoints provided by EMRs are:
• Demographic and clinical characteristics of treated
patients, including co-medication and co-morbidity
• Indication for which the product is prescribed in routine
clinical practice
• Average duration of treatment episodes and the daily
doses prescribed according to the route of administration
Case in practice: Cardiovascular disease
The ability to determine requirements for clinical trials in a
niche cardiovascular indication (statin intolerant) was
challenged by lack of consensus between experts and KOLs
regarding the exact definition of this patient population. An
analysis was therefore conducted using RWD datasets to
determine 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 number
of patients needed and (2) target and pre-select recruitment
sites. In France, the RWD sources included GPs and an
additional panel of cardiologists.

2. ACCESSPOINT • VOLUME 5 • ISSUE 10 PAGE 17
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Step 1
The first step had three key goals:
1. Characterize and quantify the number of patients to be
included 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
Noofdoctors
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
Conclusion
The use of RWD brought clarity around the statin-intolerant
definition and allowed the inclusion/exclusion criteria to be
framed. This provided an evidence base for
recommendations to enhance the clinical strategy and
ensure that the number of required sites to be involved
would not fall short. The ability to achieve this is of major
importance within the context of rising costs and limited
R&D resources and in avoiding unexpected requirements to
boost patient recruitment or complete a rescue study.
Shrinking R&D budgets and challenges for funding the new
drug development process provide impetus to explore and
utilize RWD as a source that is ripe for application to
support the achievement of efficiency savings.

3. PAGE 18 IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS
PROJECT FOCUS RESEARCH & DEVELOPMENT
DemographicDataDrugPrescriptionBiometric,MedicalDataAdditionalHealthData
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
Yes
Drug
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