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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

  • Harry P. Selker (a1) (a2), Manlik Kwong (a1) (a2), Robin Ruthazer (a1), Sheeona Gorman (a1), Giuliana Green (a1), Elizabeth Patchen (a2), James E. Udelson (a3), Howard A. Smithline (a4), Michael R. Baumann (a5), Paul A. Harris (a6), Rashmee U. Shah (a7), Sarah J. Nelson (a8), Theodora Cohen (a1) (a2), Elizabeth B. Jones (a9), Brien A. Barnewolt (a10) and Andrew E. Williams (a1)...

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.

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Copyright

This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-ncnd/4.0/), which permits noncommercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.

Corresponding author

*Address for correspondence: H. P. Selker, MD, MSPH, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, #63, Boston, MA 02111, 617-636-5009, USA. Email: hselker@tuftsmedicalcenter.org

References

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1.Hripcsak, G, Albers, DJ. Next-generation phenotyping of electronic health records. Journal of the American Medical Informatics Association 2013; 20(1): 117121.
2.Rubbo, B, et al. Use of electronic health records to ascertain, validate and phenotype acute myocardial infarction: A systematic review and recommendations. International Journal of Cardiology 2015; 187: 705711.
3.Gronski, L, et al. Utility of daily troponin orders for identifying acute myocardial infarction patients for quality improvement. Critical Pathways in Cardiology 2012; 11(2): 7476.
4.Coloma, PM, et al. Identification of acute myocardial infarction from electronic healthcare records using different disease coding systems: A validation study in three European countries. BMJ Open 2013; 3(6): e002862.
5.Selker, HP, et al. Patient-specific predictions of outcomes in myocardial infarction for real-time emergency use: A thrombolytic predictive instrument. Annals of Internal Medicine 1997; 127(7): 538556.
6.Selker, HP, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial. Annals of Internal Medicine 1998; 129(11): 845855.
7.Selker, HP, Beshansky, JR, Griffith, JL. TPI Investigators. Use of the electrocardiograph-based thrombolytic predictive instrument to assist thrombolytic and reperfusion therapy for acute myocardial infarction. A multicenter, randomized, controlled, clinical effectiveness trial. Annals of Internal Medicine 2002; 137(2): 8795.
8.Selker, HP, et al. Emergency medical service predictive instrument-aided diagnosis and treatment of acute coronary syndromes and ST-segment elevation myocardial infarction in the IMMEDIATE trial. Prehospital Emergency Care 2011; 15(2): 139148.
9.Selker, HP, et al. Study design for the Immediate Myocardial Metabolic Enhancement during Initial Assessment and Treatment in Emergency Care (IMMEDIATE) Trial: A double-blind randomized controlled trial of intravenous glucose, insulin, and potassium for acute coronary syndromes in emergency medical services. American Heart Journal 2012; 163(3): 315322.
10.Selker, HP, et al. Out-of-hospital administration of intravenous glucose-insulin-potassium in patients with suspected acute coronary syndromes: The IMMEDIATE randomized controlled trial. JAMA 2012; 307(18): 19251933.
11.Selker, HP, Griffith, JL, D’Agostino, RB. A tool for judging coronary care unit admission appropriateness, valid for both real-time and retrospective use. A time-insensitive predictive instrument (TIPI) for acute cardiac ischemia: a multicenter study. Medical Care 1991; 29(7): 610627.
12.Selker, HP, et al. Random treatment assignment using mathematical equipoise for comparative effectiveness trials. Clinical and Translational Science 2011; 4(1): 1016.

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