An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes. Issue 6 (December 2018)

• Record Type: Journal Article
• Authors: Selker, Harry P.; Kwong, Manlik; Ruthazer, Robin; Gorman, Sheeona; Green, Giuliana; Patchen, Elizabeth; Udelson, James E.; Smithline, Howard A.; Baumann, Michael R.; Harris, Paul A.; Shah, Rashmee U.; Nelson, Sarah J.; Cohen, Theodora; Jones, Elizabeth B.; Barnewolt, Brien A.; Williams, Andrew E.
• Is Part Of: Journal of clinical and translational science. Volume 2:Issue 6(2018)
• Page Start: 377
• Page End: 383
• Publication Date: 2018-12
• Subjects: Cohort discovery, -- clinical trial enrollment, -- acute coronary syndromes, -- medical device, -- electrocardiograph; Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2019.365
• Languages: English
• ISSNs: 2059-8661

Abstract

Abstract: Background: To identify potential participants for clinical trials, electronic health records (EHRs) are searched at potential sites. As an alternative, we investigated using medical devices used for real-time diagnostic decisions for trial enrollment. Methods: To project cohorts for a trial in acute coronary syndromes (ACS), we used electrocardiograph-based algorithms that identify ACS or ST elevation myocardial infarction (STEMI) that prompt clinicians to offer patients trial enrollment. We searched six hospitals' electrocardiograph systems for electrocardiograms (ECGs) meeting the planned trial's enrollment criterion: ECGs with STEMI or > 75% probability of ACS by the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI). We revised the ACI-TIPI regression to require only data directly from the electrocardiograph, the e-ACI-TIPI using the same data used for the original ACI-TIPI (development set n = 3, 453; test set n = 2, 315). We also tested both on data from emergency department electrocardiographs from across the US ( n = 8, 556). We then used ACI-TIPI and e-ACI-TIPI to identify potential cohorts for the ACS trial and compared performance to cohorts from EHR data at the hospitals. Results: Receiver-operating characteristic (ROC) curve areas on the test set were excellent, 0.89 for ACI-TIPI and 0.84 for the e-ACI-TIPI, as was calibration. On the national electrocardiographic database, ROC areas were 0.78 and 0.69, respectively, and with very good calibration. When tested for detection of patients with > 75% ACS probability, both electrocardiograph-based methods identified eligible patients well, and better than did EHRs. Conclusion: Using data from medical devices such as electrocardiographs may provide accurate projections of available cohorts for clinical trials.