A collaborative foundation for new diabetes insights in Germany

IMS RWE AccessPoint 9 1114 V2 07/11/2014 17:02 Page 45

InsIghts

MIXED METHODS REGISTRY CREATION

A collaborative foundation for new diabetes insights in Germany Researchers conducting analytics and epidemiological studies using electronic medical record databases frequently find themselves short of critical variables. The value from data collected through a mixed methods registry like DIAREG spans scientific and commercial applications and creates new potential for exploring relationships between perspectives, actions and outcomes.

The authors

Joshua Hiller, MBA is Senior Principal, RWE Solutions, IMS Health jhiller@imshealth.com

Laura Garcia Alvarez, PHD is Senior Consultant, RWE Solutions, IMS Health LGarciaAlvarez@uk.imshealth.com

ACCESSPOINT • VOLUME 5 ISSUE 9

PAGE 45

IMS RWE AccessPoint 9 1114 V2 07/11/2014 17:02 Page 46

InsIghts MIXED METHODS REGISTRY CREATION

Enhanced insights from a mixed methods approach Researchers conducting analytics and epidemiological studies using electronic medical record (EMR) databases frequently find themselves short of critical variables, potentially limiting the breadth of research they can perform. Although widely available EMR databases such as The Health Improvement Network (THIN), IMS® Disease Analyzer, and the Clinical Practice Research Datalink (CPRD) contain a great deal of longitudinal primary care data, it is often the case that certain types of information are missing – either because an EMR field has not been completed or because a particular field does not exist within the database.

In particular, behavioral detail such as reasons for changing therapy or the physician’s perspective of important clinical characteristics are rarely part of a structured health record and thus are not contained in mainstream EMR databases.

Typically, researchers must then decide whether to sacrifice the breadth of variables captured, and hence limit the study scope, or use a purely prospective design and sacrifice time and cost to implement an extended prospective observational study.

LEVERAGING MIXED METHODS FOR A COMPREHENSIVE RESOURCE To address these challenges, IMS Health, in partnership with AstraZeneca, has developed an innovative registry (DIAREG) of patients with type 2 diabetes mellitus (T2DM). AstraZeneca is committed to demonstrating the efficacy and benefit of its medicines in a real-world setting, especially in terms of patient-relevant outcomes.

The registry is based on the complementary methods of retrospective and prospective data collection, thereby overcoming the individual limitations of each, enabling the creation of a rich data resource for observational research in this area.

IDENTIFYING REQUIREMENTS

DIAREG

Work on DIAREG began in 2012. Understanding the key requirements for a comprehensive prospective disease registry, IMS® Disease Analyzer in Germany was selected as the core data backbone, being representative with input from physicians in general practice as well as

PAGE 46

diabetologists,i and validated with a documented history of application in published scientific studies.

Initial analysis of data variables confirmed that Disease Analyzer contained rich information on population characteristics (eg, demographics, medical history) and treatment patterns (eg, diagnosis, prescriptions, comedications, co-morbid conditions) in diabetes patients.

However, while some data existed for certain diabetesrelevant clinical parameters, such as HbA1c and body mass index (BMI), this was often recorded less frequently or sometimes not at all. Furthermore, other clinical outcomes (eg, cardiovascular events, hypoglycemic episodes, hospitalizations), physician behavior (eg, drivers of therapy decision, reasons for dose or treatment modification) and patient-reported outcomes (PRO) (eg, general quality of life, disease-specific quality of life or treatment satisfaction), were not captured as structured data within the patient record at all.

As a result of this initial analysis, a set of 27 variables were identified for their potential research value if collected, to enhance the available EMR resource.

DIAREG IS BORN The identified need for an ‘enhanced’ EMR registry took the next stage of development down two separate paths – technical and ethical – to achieve an optimal solution. technical implementation To facilitate technical implementation of the registry, IMS Health worked closely with the EMR software vendor responsible for collecting the data underpinning Disease Analyzer. Together, they designed and created the capability for a retrieve form data capture window (or ‘pop up’) to be triggered in the physician office during the patient visit, based on a set of criteria available within the patient EMR (eg, diagnosis code, existence of prior antidiabetic treatment, etc). Every time an eligible patient was identified through the trigger, the physician completed an electronic case report form (eCRF) in the ‘pop-up’ window to provide the required additional clinical data. i

Becher H, Kostev K, Schröder-Bernhardi D. Validity and representativeness of the Disease Analyzer patient database for use in pharmacoepidemiological and pharmacoeconomic studies. Int J Clin Pharmacol Ther, 2009; 47: 617-626

1,071

22%

patients

changed therapy

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

IMS RWE AccessPoint 9 1114 V2 07/11/2014 17:02 Page 47

FIGURE 1: CUSTOMIZEd EMR ANd REGISTRY dATA COHORT

eCRF pop-up

IMS Disease Analyzer

Enhanced Disease Cohort PRO

Double hash algorithm applied for data anonymization Patient characteristics of interest programmed into EMR database to trigger eCRF

Since patient EMR was used as the basis for including or excluding a patient from the registry, the potential impact of subjective selection was reduced. Consecutive new patients continued to be triggered for inclusion in the registry until the physician reached a pre-defined cap, thus providing a framework for random sample selection. Data collected from the retrieve form data capture window eCRF is currently being linked back to the EMR using a hash de-id process that removes protected health information (PHI) prior to extraction to the IMS Health database.

In addition to the enhanced clinical data collection, a second phase of the registry build involved the introduction of PROs to provide a further layer of information. These are collected via paper-based questionnaires handed to patients at the physician site where they are filled in and returned for entry into an electronic database. An additional hash algorithm has been deployed for one-way linkage of the PRO data to the EMR and eCRF (Figure 1).

Ethical implementation From an ethical perspective, it was essential to ensure that the registry was developed in accordance with sound observational research practices. To that end, a Scientific Advisory Board was created to provide guidance on the methods for site identification, eCRF review, inclusion of PROs, use of patient informed consent, and submissions for ethics approval. The Committee is made up of six independent academic researchers and physicians who have no affiliation with either AstraZeneca or IMS Health.

Patients participating in the registry have given informed consent for inclusion of their information from EMR, as well as the eCRF and PRO questionnaire. The registry protocol was reviewed and approved by the Ethics Committee, Nordrhein, Germany (Ethikkommission der Ă„rztekammer Nordrhein) under the name of DIAREG.

EMR and PRO linked to disease-specific data at patient level creating enhanced patient record

UNIQUELY GRANULAR OBSERVATIONAL RESEARCH As of September 2014, DIAREG has been collecting data for more than 18 months. The registry currently contains eCRF questionnaires, with comprehensive, longitudinal data variables, for 1,071 diabetes patients, enabling granular observational research. A subset analysis of these patients (n=824) shows that 77% were enrolled by GPs, the remainder being recruited by diabetologists.

Based on data from half of the cohort, average length of time with T2DM is 12.3 years (median 11 years). Twentytwo percent of patients (n=181/824) in the registry have experienced a change to their anti-diabetes therapy at least once within the last year, mostly by the GP (57%) but also by diabetologists, who were responsible for 35% of therapy changes. For 152 patients (84% of the therapy modification population), this took the form of a dose adjustment to their existing therapy, mainly due to insufficient control of HbA1c (Figure 2). A change of drug was recorded for 60 patients (33%) for the same reason.

Overall, doctors have reported high expectations of HbA1c reduction when deciding on a new treatment regimen.

A total of 475 patients (58%) self-monitored their blood glucose levels, with 30% checking their blood sugar more than twice a day. Visits to other specialists were recorded for 43% of 824 patients, the most frequently visited being ophthalmologists (57%) for diagnosis of retinopathies.

Of the 824 patients in the subset, 43 experienced at least one hypoglycemic event, four of whom required hospitalization (Figure 3).

ENABLING EVIDENCE-BASED CONNECTIONS The data captured in DIAREG enables researchers to identify and explore associations across measures that have not been collected before in a sustainable and integrated manner. continued on next page

ACCESSPOINT • VOLUME 5 ISSUE 9

PAGE 47

IMS RWE AccessPoint 9 1114 V2 07/11/2014 17:02 Page 48

InsIghts MIXED METHODS REGISTRY CREATION

FIGURE 2: MOST THERAPY AdJUSTMENTS ARE dUE TO POOR HbA1C CONTROL Number of patients with at least one dose adjustment

Reason for therapy adjustment

39

Other

9

Co-medication

13

Weight gain

84% (n=152)

10

Patient request

16% (n=29)

22

Hypoglycemic events

16

Microvascular complications

Yes

No

Macrovascular complications

2

Change of substance combination

23 112

Insufficient HbA1c reduction 0

20

40

60

80

100

120

Source: Disease registries including Patient Reported Outcomes - IMS速 DIAREG

By allowing comparison of clinical parameters at a patient level, it provides evidence of associations from a real-world setting that previously could only be identified anecdotally or through market research.

As an example, the capture of BMI and HbA1c

measurements without DIAREG was recorded in 61.9% and 42.3% of the population respectively. With DIAREG, the capture of these critical lab measurements increases to 83.3% and 77.6% respectively (Figure 4).

FIGURE 3: PATIENTS EXPERIENCING A HYPOGLYCEMIC EVENT Type of hypoglycemic event Number of patients having a hypoglycemic event

1

Hypoglycemia requiring hospitalization

3 2 1

Hypoglycemia requiring assistance

2 6 3 7

Blood sugar <70 mg/dl measured by patient

12 15 4 9

Hypoglycemia with glucose consumption

13 17 0

2

4

Number of events per patient

6

8

4 or more

10

3

12

2

14

16

1

N= 824 Patients, of which 43 had at least 1 hypoglycemic event as reported in DIAREG Source: Disease registries including Patient Reported Outcomes - IMS速 DIAREG

PAGE 48

IMS HEALTH REAL-WORLD EVIDENCE SOLUTIONS & HEOR

IMS RWE AccessPoint 9 1114 V2 07/11/2014 17:02 Page 49

FIGURE 4: dIAREG ENAbLES INCREASEd CAPTURE OF CRITICAL MEASUREMENTS

42.3%

HbA1c

41.0%

61.9%

Height/Weight

57.0%

Blood pressure

% 0

15.7%

10

20

30

40

Already in DA

50

22.4%

29.0% 60

Update in DIAREG

70

16.7%

80

14.0% 90

100

Missing

DIAREG: n=407 patients Source: Disease registries including Patient Reported Outcomes - IMS® DIAREG

FIGURE 5: CATEGORIES OF dATA ENHANCEd THROUGH A MIXEd METHOdS APPROACH

Information in IMS® Disease Analyzer

Information in IMS® DIAREG

Documented type of diabetes

Confirmation of type 2 diabetes diagnosis

Therapy duration at the treating physician

Start/duration of type 2 diabetes

Disease-relevant parameters (eg, HbA1c, blood glucose, weight/BMI, blood pressure)

Complete documentation of all disease-relevant parameters

–

Frequency and severity of hypoglycemias

–

Treatment goals (related to symptoms, laboratory parameters and complications)

–

Reasons for change of therapy and treatment goals associated with the change

Diabetes-related complications

Complete documentation of all diabetes-related complications

Referral to hospital

All stays in hospital with reasons for hospitalization, diagnosis at discharge and hospital days

Referral to specialists

All specialist consultations with diagnosis

Referral to rehabilitation

All rehabilitation measures with diagnosis

Patient education

All educational activities

–

Frequency of blood glucose self monitoring

–

Physician's estimate of the patient's therapy adherence

Prior to implementation of DIAREG, real-world information on the proportion of patients checking blood sugar, the reason for modifying treatment, the number and type of hypoglycemic events, diagnosis for specialist visits or quantity of lab measurements captured was nonexistent. Figure 5 outlines categories of data enhanced through the mixed methods approach.

comprehensive patient record allows retrospective analysis using measures that are not available in other datasets. For brand teams, the behavioral information from physicians and patients, such as reasons for switch and quality of life, creates new potential for exploring relationships between perspectives, actions and outcomes.

EXTENDED VALUE WITH MULTIPLE APPLICATIONS

The IMS® DIAREG registry is open to other collaborations.

The value from data collected through a mixed methods registry like DIAREG spans scientific and commercial applications. For researchers, the depth of detail from the

For further information please email Jhiller@imshealth.com

ACCESSPOINT • VOLUME 5 ISSUE 9

PAGE 49