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
AbstractBackground: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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2018-12-01
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| Series: | Journal of Clinical and Translational Science |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2059866119003650/type/journal_article |
| _version_ | 1811156865324679168 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Harry P. Selker |
| collection | DOAJ |
| description | AbstractBackground: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. |
| first_indexed | 2024-04-10T04:58:12Z |
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| id | doaj.art-8bd59bd4855e4a5e9caa5de989b57fee |
| institution | Directory Open Access Journal |
| issn | 2059-8661 |
| language | English |
| last_indexed | 2024-04-10T04:58:12Z |
| publishDate | 2018-12-01 |
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| series | Journal of Clinical and Translational Science |
| spelling | doaj.art-8bd59bd4855e4a5e9caa5de989b57fee2023-03-09T12:29:41ZengCambridge University PressJournal of Clinical and Translational Science2059-86612018-12-01237738310.1017/cts.2019.365An example of medical device-based projection of clinical trial enrollment: Use of electrocardiographic data to identify candidates for a trial in acute coronary syndromesHarry P. Selker0Manlik Kwong1Robin Ruthazer2Sheeona Gorman3Giuliana Green4Elizabeth Patchen5James E. Udelson6Howard A. Smithline7Michael R. Baumann8Paul A. Harris9Rashmee U. Shah10Sarah J. Nelson11Theodora Cohen12Elizabeth B. Jones13Brien A. Barnewolt14Andrew E. Williams15Tufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USAInstitute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USADivision of Cardiology, Tufts Medical Center, Boston, Massachusetts, USADepartment of Emergency Medicine, Baystate Medical Center, Springfield, Massachusetts, USADepartment of Emergency Medicine, Maine Medical Center, Portland, Maine, USADepartment of Biomedical Informatics and Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee, USADivision of Cardiovascular Medicine, Univerity of Utah School of Medicine, Salt Lake City, Utah, USAVanderbilt University Medical Center, Nashville, Tennessee, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USA Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USADepartment of Emergency Medicine, University of Texas Health Science Center at Houston, Houston, Texas, USADepartment of Emergency Medicine, Tufts Medical Center, Boston, Massachusetts, USATufts Clinical and Translational Science Institute, Tufts University, Boston, Massachusetts, USAAbstractBackground: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.https://www.cambridge.org/core/product/identifier/S2059866119003650/type/journal_articleCohort discoveryclinical trial enrollmentacute coronary syndromesmedical deviceelectrocardiograph |
| spellingShingle | 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 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 Cohort discovery clinical trial enrollment acute coronary syndromes medical device electrocardiograph |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | example of medical device based projection of clinical trial enrollment use of electrocardiographic data to identify candidates for a trial in acute coronary syndromes |
| topic | Cohort discovery clinical trial enrollment acute coronary syndromes medical device electrocardiograph |
| url | https://www.cambridge.org/core/product/identifier/S2059866119003650/type/journal_article |
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