Make the Most of Every Patient
Sheila RocchioVice President, Marketing & Product Management617.973.1666Fax/[email protected]
March 11, 2009Electronic Data in Clinical Trials
Integrating Electronic Integrating Electronic Patient Reported Patient Reported Outcomes with other Outcomes with other eClinical Data StreamseClinical Data Streams
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Discussion Topics
• Definitions
• Rationale for Integration
• Case Studies
• Considerations for integrationToday’s Speakers
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Definitions
• Patient Reported Outcomes (PRO) – The measurement of any aspect of a patient’s health status that comes
directly from the patient (ie, without the interpretation of the patient’s responses by a physician or anyone else), including disease symptoms, patient functioning, and quality of life (QOL) - FDA Guidance for Industry: Patient Reported Outcome Measures
• Electronic Patient Reported Outcomes (ePRO) – PRO data initially captured electronically. NOTE: Usually ePRO data is
captured as eSource - CDISC Glossary
• eSource data (electronic source data): – Source data captured initially into a permanent electronic record used for
the reconstruction and evaluation of a trial. Permanent in the context of these definitions implies that any changes made to the electronic data are recorded via an audit trail – CDISC Glossary
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Available ePRO Platforms
• Integrated Voice Response Systems (IVRS)
• Handhelds/Smart Phones
• Integrated Web Response (IWR)
• Tablets (primarily for sites)
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Reasons for Integrating eClinical Data
• Use of many different electronic data capture systems by sponsors
– EDC– IVR/IWR Randomization – ePRO– Central Labs– Central ECGs/Respiratory– Imaging – Portals
• Proliferation of technology at sites– Multiple passwords, helpdesks, and instructions
• System integration capabilities have improved– CDISC standards are well supported (ODM) – Web services/APIs exist for the purpose of integration
• System integration can provide real-time benefits for decision making e.g. randomizations, clinical monitoring, adaptive trials
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Case Study: Rescue Medication
Integration Goal: Capture more accurate rescue medication data
Data CollectionData Collection……
• Subject or Caregiver completes Evening Diary on eDiary
• Alarm sounds if needed to remind subject to complete diary before bedtime
• Diaries transmitted nightly to ePRO Server
• Evening diary includes Rescue Medication intake data
– type of rescue medication
– number of tablets
– timestamp of rescue medication intake
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Case Study: Rescue Medication (Continued)
Data TransferData Transfer……
• Data is transferred to EDC system
• Attributes of Data Transfer:
– Cumulative data set – all rescue meds recorded for all subjects
– ASCII file format
– twice monthly transfers
– data uploaded via secure FTP
– Confirmation email sent after each upload of data
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Case Study Participants
ePRO Vendor
CRO
EDC Vendor
Sponsor
Diary Data
RescueMedication
Intake
Merged Data
RescueMedication
Details
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Case Study #2: Eligibility Criteria
Integration Goal: Combine separate sources of eligibility data
Data CollectionData Collection……
• An Eligibility Review screen is often included on the eDiary
• Sites review eligibility criteria in Screening and determine if a subject can be randomized into the Treatment phase
• Examples :
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Case Study #2: Eligibility Criteria (continued)
Data TransferData Transfer……• Data is transferred to IVR system
• IVR system also receives subject data from another system
• Study Coordinator calls IVRS while subject is at the site and receives randomization decision
• Attributes of Data Transfer:– Cumulative data set – key eligibility criteria sent for all subjects
– ASCII file format
– Transfer sent within 5 minutes of eDiary transmission
– data uploaded automatically to IVRS via secure FTP
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Case Study #3: Adaptive Trial Design for Migraine Study
Four key players in the adaptive randomization:
Subject with Qualifying Headache
eDiary System
AdaptiveAlgorithm
IVRS
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Case Study #3: Adaptive Trial Design for Migraine Study
12
SubjecteDiary
AdaptiveAlgorithmIVRS
1. SubjectCall
2. Dose Regimen Code
3. Enter Dose Regimen Code
4. Dose Regimen revealed
5. Treat headache; complete post-dose assessments
6. Two-hour time point data used to update Adaptive Algorithm weekly
7. Update randomization boundaries
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Case Study #3: Adaptive Trial Design for Migraine StudyIntegration Goal: Optimizing adaptive randomization with real time data
Data CollectionData Collection……
• Subject receives eDiary and waits for qualifying headache to occur, calls IVR system for treatment code
• Subject records start of headache and intake of study drug
• A sequence of timed assessments are triggered based on treatment code
• Subject indicates achieving perceptible or meaningful pain reliefExample
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Case Study #3: Adaptive Trial Design (continued)
Data TransferData Transfer……• Data collected in timed assessments is primary endpoint
• Assessment data is transferred to Adaptive Trial Design system
• System updates algorithm based on treatment data
• Attributes of Data Transfer:– Cumulative data set – all timed assessments for all subjects
– ASCII file format
– Transfer data for each new set of 10 subjects
– data uploaded automatically via secure FTP
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Case Study #4: Peak Flow Meter Integration
Integration Goal: Combine subjective and objective data
Data CollectionData Collection……
• eDiary prompts subject to use peak flow meter twice daily
• Peak Flow Meter (eSense PiKo) captures
– Peak Expiratory Flow (PEF)
– Forced Expiratory Volume in 1 Second (FEV1)
– Time and quality of blows
• ePEF has wireless radio frequency link to the eDiary
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Case Study #4: Peak Flow Meter Integration (continued)
Data TransferData Transfer……• Peak flow Meter transfers PEF and FEV1 values to eDiary while
subject completes morning and evening diaries
• Diary data sent to ePRO portal includes both responses to diary questions and peak flow readings
• Data Summaries developed to:– Track the peak flow meter linked to each eDiary
– Track subject compliance in sending automatic vs. manual readings
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Integration Considerations and Best Practices
• Identify the integration goals– Who will benefit (sites, data management, subjects)?– Assess criticality – is the integration needed to meet study goals?
• Define cost benefit– What is the cost of the integration?– What costs are avoided because of the integration?
• Clearly define requirements– End user experience – Standards used (e.g. CDISC, other data formats)
• One way data integration is simpler than two directions– Plan for how edits to existing data will be handled
• Avoid possibility of multiple sources of conflicting data– Visit dates coming from 2 different systems
• Plan for a User Acceptance Testing of the integrated systems– Identify unusual use cases
• People need to communicate for systems to integrate