Post on 24-Dec-2015
Smart CRF
Leonard Sacks MDDeputy director
Office of Critical Path ProgramsFDA
How familiar are you with the CDISC CDASH initiative?
1. Not familiar
2. Somewhat familiar
3. Familiar
4. Very Familiar
1 2 3 4
1
118
12
1 2 3 4
1
118
12
Do you believe FDA accepts paperless trials? 1) Yes 2) No
1 2
25
17
1 2
25
17
If your organization works with clinical trials, what approximate percentage of CRFs are fully electronic?
1. 0%
2. 1-50%
3. 51%-99%
4. 100%
1 2 3 4
4
21
105
1 2 3 4
4
21
105
How effective would you estimate your current systems are in detecting investigator fraud (e.g. fictitious patients)?
1. <25%
2. 26-50%
3. 51-75%
4. 76-99%
5. 100%
1 2 3 4 5
15
9 8 8
11 2 3 4 5
15
9 8 8
1
What percentage of clinical monitoring is spent correcting and completing the CRF (e.g. missing concomitant meds, missing AE report, missing investigator signature or date)?
1. <25%
2. 26-50%
3. 51-75%
4. 76-99%
5. 100%
1 2 3 4 5
7
15
1013
1 2 3 4 5
7
15
1013
What percentage of clinical monitoring is spent educating study staff?
1. <25%
2. 26-50%
3. 51-75%
4. 76-99%
5. 100%
1 2 3 4 5
12
19
5
1 2 3 4 5
12
19
5
Hey what’s this?
And this….
Old system
Huge bulky paper CRF Tedious for investigator- repetition, data transfer,
consistency Tedious for monitors Tedious to transport and archive All data must be transcribed Data cannot be evaluated in real-time Unable to capture images etc Numerous ancillary functions are needed e.g.
randomization, informed consent, monitoring
Borrow experience
Paper records antiquated in clinical practice At FDA, electronic submissions SPL- searchable labels Electronic Adverse Event reports Electronic health records in clinical practice
Borrow experience
Security and privacy- Experience from the financial world-online
banking Financial privacy and tax returns
Paper CRF – ? the dinosaur
Electronic CRF ~50% submissions, limited capabilities.
Smart CRF becomes the platform for clinical study
Smart CRF – Two functional tiers
1. Information entry/archival- Interface with point of data entry
investigator, laboratory, patient (in the case of a Patient Reported Outcome tool)
“smart” function auto-audits for inconsistencies in
matching fields checks for missing data prompts for additional data fields e.g.
SAE form automated carry over of information
that does not change data assembly
Smart CRF – Two functional tiers
2. Data trafficking Input from:
investigator, lab, imaging, patient (PRO’s)
Output to: Sponsor IRB/DSMB CRO FDA
Smart CRF-trial components
Envisaged to cater for all the components of a clinical trial Electronic informed consent/video interface Inclusion and exclusion Randomization Recording study procedures Recording clinical findings Laboratory data trafficking Auto monitoring Safety reporting Interactive with investigator Archival of data, images etc
Smart CRF – the stakeholders/users
Envisaged to cater for the needs of all the many users/stakeholders in a trial: Patient Health care provider/investigator Laboratory services Imaging services CRO Monitors IRB/DSMB Sponsor FDA
Functions tailored for user/stakeholder
Patient- informed consent, real-time results, safety alerts-from lab, from sponsor
Investigator- interactive-informs on data trends prompts e.g. SAE forms auto transfer of fixed data automatic population of lab fields auto-monitoring
consistency e.g. inclusion and exclusion criteria, completeness e.g. stop dates for concomitant meds
Sponsor- remote access real-time e.g. overseas sites, Immediate data analysis without waiting for transcription
CRO- monitoring function, archiving, communication
IRB- automatic submission and aggregation of SAE reports
FDA- fraud monitoring, investigator training
Other functions Fraud detection
Variance algorithms- e.g. date of birth, variance of lab data, BP measurement Corroborative data entry-independent entry from lab- eliminates transcription fraud (cannot
eliminate fraudulent specimens), independent imaging data entry Electronic date and time stamp, unable to change data without an electronic record
Safety warning- Automatic generation of SAE form Integration of all SAE reports Algorithms for iterative lab safety analysis e.g. threshold number of LFT elevations, threshold
differences between arms Notification of IRB and Sponsors They can trigger immediate alerts to investigator and patient Investigator interactive reporting of safety trends-e.g.. subcritical rise in LFTs, Creatinine
Auto-monitoring- Prompts for missing data-e.g.. stop dates, start dates informed consent- won’t allow
investigator to continue Consistency check on all dependent fields- e.g. concomitant meds, pill counts
Imaging
Smart CRF – Trial Components: Do you agree thus far with the Smart CRF as an objective?
1. Yes
2. No
3. Somewhat
1 2 3
31
6
1 2 3
31
6
Are you currently using a ‘Smart CRF’?
1. Yes
2. No
3. Don’t know
1 2 3
20 20
1 2 3
20 20
An interesting corollary-
Study results can be evaluated without un-blinding
Automated pre-specified analyses can be reviewed without knowing which arm is which
Conclusions can be made on non-inferiority or superiority prior to breaking the blind
Complementary initiatives
Embedding the case report form in the clinical electronic health record so that patients can be studied during the course of their clinical care (Electronic health Records/Clinical research)
Data standards- streamlining the format and content of data elements so they can be shared between different users e.g. laboratory, health care provider, investigator, sponsor, pharmaco-vigilance investigator
Ethical initiatives to ensure patient privacy and protect patient identity
More on data standards
CDASH Project CClinical DData AAcquisition SStandards
HHarmonization
CDISC Study Data Tabulation Model (SDTM) Standard for Exchange of Nonclinical Data
(SEND) Operational Data Model (ODM)
Has your organization taken any steps to implementing any CDISC standards?
1. Yes
2. No
3. Don’t know
1 2 3
33
21 2 3
33
2
If you have implemented a CDISC standard, which one is most valuable to your organization?
1. Lab Standard
2. SDTM (Study Data Tabulation Model)
3. ODM (Operational Data Model)
4. SEND (Standard for Exchange of Non-clinical Data) 1 2 3 4
2
27
61
1 2 3 4
2
27
61
Clinical Data Interchange Standards Consortium Principles
Lead development of standards that improve process efficiency while supporting the scientific nature of clinical research
Creating flexible, intelligible and navigable submissions Importance of data quality structure and content Global multidisciplinary functionality Emphasize data sharing and minimize duplication Provide education on CDISC Avoid promoting individual organizations or vendors
CDISC
CDISC
Challenges
Addressing source documentation as required by regulation
Preventing automatic (default) carryover of changeable data e.g. concomitant meds
Ensure investigator ownership of data- they take responsibility
Regulations and Guidances
21CFR11 “Electronic records, electronic signatures” Procedures
to ensure signer cannot repudiate the signed record as genuine
For validation of accuracy, ability to detect alterations, time stamped audit trails
For limitation of access to, and generation of accurate copies of electronic records
Checks on authority to sign and access records To verify identity of electronic signature
Regulations and Guidances Guidance: Part 11, electronic records; Electronic signatures-scope and
application (August 2003) Enforce
Limiting system access to authorized individuals Use of operational system checks Use of authority checks Determination that persons who develop, maintain or use electronic systems
have the education, training and experience to perform their assigned tasks Establishment of, and adherence to written policies that hold individuals
accountable for actions initiated under their electronic signatures Appropriate controls over systems documentation Controls for open systems corresponding to controls for closed systems Requirement related to electronic signatures
Enforcement discretion Validation of computerized systems Requirements related to computer-generated time-stamped audit traisl Legacy systems (those in place prior to Aug 97) Copies of records and record retention
Computerized systems used in clinical investigations (May 2007)
DSI- Examples of problems
Backdating of monitoring reports Gaps in performance of source verification On site source data not retained at closeout
of study Procedures recorded prior to actual patient
visit Delays in signing electronic data capture
Conclusions
Smart CRFs offer enormous opportunities to expedite clinical trials
They can be tailored to the needs of all parties involved in the clinical trial
They can perform numerous trial functions They can simplify study monitoring They can improve safety monitoring through
real-time surveillance They save trees, and space and
gasoline…..and so on.
Questions
What still needs to be done to move forward? How to we set about determining electronic
reliability? Are there standards that need to be
articulated for the myriad functions of the ECF?
Other ideas?
How many years away is a Smart CRF?
1. <3
2. 3-7
3. 7-10
4. >10
5. Not going to happen
Is there market value for the Smart CRF?
1. Yes
2. No
3. Not Sure
Is SCDM the forum for discussion on the development and adoption of Smart CRF objective?
1. Yes
2. No
3. Not Sure