Welcome to Durham!

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

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118

12

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1

118

12

Do you believe FDA accepts paperless trials? 1) Yes 2) No

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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%

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4

21

105

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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%

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15

9 8 8

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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%

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7

15

1013

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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%

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12

19

5

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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

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6

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6

Are you currently using a ‘Smart CRF’?

1. Yes

2. No

3. Don’t know

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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

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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

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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