integration of epro by sheila rocchio, mba
DESCRIPTION
The Integration of ePRO with other eClinical Systems A review of what sponsors and sites need to know about ePRO, including the multiple types of ePRO systems, the history and rationale for using ePRO, and the integration of ePRO with other eClinical systems.TRANSCRIPT
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
