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Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records

OBJECTIVES: Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algori...

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Autores principales: Somani, Sulaiman, Yoffie, Stephen, Teng, Shelly, Havaldar, Shreyas, Nadkarni, Girish N, Zhao, Shan, Glicksberg, Benjamin S
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374370/
https://www.ncbi.nlm.nih.gov/pubmed/34423260
http://dx.doi.org/10.1093/jamiaopen/ooab068
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author Somani, Sulaiman
Yoffie, Stephen
Teng, Shelly
Havaldar, Shreyas
Nadkarni, Girish N
Zhao, Shan
Glicksberg, Benjamin S
author_facet Somani, Sulaiman
Yoffie, Stephen
Teng, Shelly
Havaldar, Shreyas
Nadkarni, Girish N
Zhao, Shan
Glicksberg, Benjamin S
author_sort Somani, Sulaiman
collection PubMed
description OBJECTIVES: Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. MATERIALS AND METHODS: We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. RESULTS: We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing “STEM” has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). DISCUSSION AND CONCLUSION: In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.
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spelling pubmed-83743702021-08-20 Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records Somani, Sulaiman Yoffie, Stephen Teng, Shelly Havaldar, Shreyas Nadkarni, Girish N Zhao, Shan Glicksberg, Benjamin S JAMIA Open Research and Applications OBJECTIVES: Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. MATERIALS AND METHODS: We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. RESULTS: We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing “STEM” has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). DISCUSSION AND CONCLUSION: In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR. Oxford University Press 2021-08-19 /pmc/articles/PMC8374370/ /pubmed/34423260 http://dx.doi.org/10.1093/jamiaopen/ooab068 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Research and Applications
Somani, Sulaiman
Yoffie, Stephen
Teng, Shelly
Havaldar, Shreyas
Nadkarni, Girish N
Zhao, Shan
Glicksberg, Benjamin S
Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title_full Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title_fullStr Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title_full_unstemmed Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title_short Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records
title_sort development and validation of techniques for phenotyping st-elevation myocardial infarction encounters from electronic health records
topic Research and Applications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8374370/
https://www.ncbi.nlm.nih.gov/pubmed/34423260
http://dx.doi.org/10.1093/jamiaopen/ooab068
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