Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification release_qliafdeftzcq5iv7wlpptcjmum

by Saarang Panchavati, Carson Lam, Nicole S. Zelin, Emily Pellegrini, Gina Barnes, Jana Hoffman, Anurag Garikipati, Jacob Calvert, Qingqing Mao, Ritankar Das

Published in Healthcare technology letters by Institution of Engineering and Technology (IET).

2021   Volume 8, Issue 6, p139-147


Diagnosis and appropriate intervention for myocardial infarction (MI) are time-sensitive but rely on clinical measures that can be progressive and initially inconclusive, underscoring the need for an accurate and early predictor of MI to support diagnostic and clinical management decisions. The objective of this study was to develop a machine learning algorithm (MLA) to predict MI diagnosis based on electronic health record data (EHR) readily available during Emergency Department assessment. An MLA was developed using retrospective patient data. The MLA used patient data as they became available in the first 3 h of care to predict MI diagnosis (defined by International Classification of Diseases, 10th revision code) at any time during the encounter. The MLA obtained an area under the receiver operating characteristic curve of 0.87, sensitivity of 87% and specificity of 70%, outperforming the comparator scoring systems TIMI and GRACE on all metrics. An MLA can synthesize complex EHR data to serve as a clinically relevant risk stratification tool for MI.
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Type  article-journal
Stage   published
Date   2021-08-31
Language   en ?
DOI  10.1049/htl2.12017
PubMed  34938570
PMC  PMC8667565
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ISSN-L:  2053-3713
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