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Selker HP, Kwong M, Ruthazer R, et al. 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. J Clin Transl Sci. 2018;2(6):377-383doi: 10.1017/cts.2019.365.
Selker, H. P., Kwong, M., Ruthazer, R., Gorman, S., Green, G., Patchen, E., Udelson, J. E., Smithline, H. A., Baumann, M. R., Harris, P. A., Shah, R. U., Nelson, S. J., Cohen, T., Jones, E. B., Barnewolt, B. A., & Williams, A. E. (2018). 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. Journal of clinical and translational science, 2(6), 377-383. https://doi.org/10.1017/cts.2019.365
Selker, Harry P, et al. "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." Journal of clinical and translational science vol. 2,6 (2018): 377-383. doi: https://doi.org/10.1017/cts.2019.365
Selker HP, Kwong M, Ruthazer R, Gorman S, Green G, Patchen E, Udelson JE, Smithline HA, Baumann MR, Harris PA, Shah RU, Nelson SJ, Cohen T, Jones EB, Barnewolt BA, Williams AE. 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. J Clin Transl Sci. 2018 Dec;2(6):377-383. doi: 10.1017/cts.2019.365. PMID: 31404280; PMCID: PMC6676436.
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J Clin Transl Sci. 2018 Dec;2(6):377-383. doi: 10.1017/cts.2019.365.

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.

Journal of clinical and translational science

Harry P Selker, Manlik Kwong, Robin Ruthazer, Sheeona Gorman, Giuliana Green, Elizabeth Patchen, James E Udelson, Howard A Smithline, Michael R Baumann, Paul A Harris, Rashmee U Shah, Sarah J Nelson, Theodora Cohen, Elizabeth B Jones, Brien A Barnewolt, Andrew E Williams

Affiliations

  1. Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA.
  2. Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA.
  3. Division of Cardiology, Tufts Medical Center, Boston, Massachusetts, USA.
  4. Department of Emergency Medicine, Baystate Medical Center, Springfield, Massachusetts, USA.
  5. Department of Emergency Medicine, Maine Medical Center, Portland, Maine, USA.
  6. Department of Biomedical Informatics and Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  7. Division of Cardiovascular Medicine, Univerity of Utah School of Medicine, Salt Lake City, Utah, USA.
  8. Vanderbilt University Medical Center, Nashville, Tennessee, USA.
  9. Department of Emergency Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USA.
  10. Department of Emergency Medicine, Tufts Medical Center, Boston, Massachusetts, USA.

PMID: 31404280 PMCID: PMC6676436 DOI: 10.1017/cts.2019.365

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

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.

Keywords: Cohort discovery; acute coronary syndromes; clinical trial enrollment; electrocardiograph; medical device

References

  1. Ann Intern Med. 2002 Jul 16;137(2):87-95 - PubMed
  2. Med Care. 1991 Jul;29(7):610-27 - PubMed
  3. Clin Transl Sci. 2011 Feb;4(1):10-6 - PubMed
  4. Prehosp Emerg Care. 2011 Apr-Jun;15(2):139-48 - PubMed
  5. Am Heart J. 2012 Mar;163(3):315-22 - PubMed
  6. JAMA. 2012 May 9;307(18):1925-33 - PubMed
  7. Crit Pathw Cardiol. 2012 Jun;11(2):74-6 - PubMed
  8. J Am Med Inform Assoc. 2013 Jan 1;20(1):117-21 - PubMed
  9. BMJ Open. 2013 Jun 20;3(6):null - PubMed
  10. Int J Cardiol. 2015;187:705-11 - PubMed
  11. Ann Intern Med. 1997 Oct 1;127(7):538-56 - PubMed
  12. Ann Intern Med. 1998 Dec 1;129(11):845-55 - PubMed

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