An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records

Abstract Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, l...

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Autores principales:	Su H. Chu, Emily S. Wan, Michael H. Cho, Sergey Goryachev, Vivian Gainer, James Linneman, Erica J. Scotty, Scott J. Hebbring, Shawn Murphy, Jessica Lasky-Su, Scott T. Weiss, Jordan W. Smoller, Elizabeth Karlson
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/92a3cd4bff984a15bac3e86cb70bc9f3
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spelling	oai:doaj.org-article:92a3cd4bff984a15bac3e86cb70bc9f32021-12-02T18:37:10ZAn independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records10.1038/s41598-021-98719-w2045-2322https://doaj.org/article/92a3cd4bff984a15bac3e86cb70bc9f32021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98719-whttps://doaj.org/toc/2045-2322Abstract Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.Su H. ChuEmily S. WanMichael H. ChoSergey GoryachevVivian GainerJames LinnemanErica J. ScottyScott J. HebbringShawn MurphyJessica Lasky-SuScott T. WeissJordan W. SmollerElizabeth KarlsonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Su H. Chu Emily S. Wan Michael H. Cho Sergey Goryachev Vivian Gainer James Linneman Erica J. Scotty Scott J. Hebbring Shawn Murphy Jessica Lasky-Su Scott T. Weiss Jordan W. Smoller Elizabeth Karlson An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
description	Abstract Electronic health records (EHR) provide an unprecedented opportunity to conduct large, cost-efficient, population-based studies. However, the studies of heterogeneous diseases, such as chronic obstructive pulmonary disease (COPD), often require labor-intensive clinical review and testing, limiting widespread use of these important resources. To develop a generalizable and efficient method for accurate identification of large COPD cohorts in EHRs, a COPD datamart was developed from 3420 participants meeting inclusion criteria in the Mass General Brigham Biobank. Training and test sets were selected and labeled with gold-standard COPD classifications obtained from chart review by pulmonologists. Multiple classes of algorithms were built utilizing both structured (e.g. ICD codes) and unstructured (e.g. medical notes) data via elastic net regression. Models explicitly including and excluding spirometry features were compared. External validation of the final algorithm was conducted in an independent biobank with a different EHR system. The final COPD classification model demonstrated excellent positive predictive value (PPV; 91.7%), sensitivity (71.7%), and specificity (94.4%). This algorithm performed well not only within the MGBB, but also demonstrated similar or improved classification performance in an independent biobank (PPV 93.5%, sensitivity 61.4%, specificity 90%). Ancillary comparisons showed that the classification model built including a binary feature for FEV1/FVC produced substantially higher sensitivity than those excluding. This study fills a gap in COPD research involving population-based EHRs, providing an important resource for the rapid, automated classification of COPD cases that is both cost-efficient and requires minimal information from unstructured medical records.
format	article
author	Su H. Chu Emily S. Wan Michael H. Cho Sergey Goryachev Vivian Gainer James Linneman Erica J. Scotty Scott J. Hebbring Shawn Murphy Jessica Lasky-Su Scott T. Weiss Jordan W. Smoller Elizabeth Karlson
author_facet	Su H. Chu Emily S. Wan Michael H. Cho Sergey Goryachev Vivian Gainer James Linneman Erica J. Scotty Scott J. Hebbring Shawn Murphy Jessica Lasky-Su Scott T. Weiss Jordan W. Smoller Elizabeth Karlson
author_sort	Su H. Chu
title	An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
title_short	An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
title_full	An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
title_fullStr	An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
title_full_unstemmed	An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records
title_sort	independently validated, portable algorithm for the rapid identification of copd patients using electronic health records
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/92a3cd4bff984a15bac3e86cb70bc9f3
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An independently validated, portable algorithm for the rapid identification of COPD patients using electronic health records

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