Predicting COVID-19 mortality with electronic medical records

Abstract This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters th...

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Autores principales:	Hossein Estiri, Zachary H. Strasser, Jeffy G. Klann, Pourandokht Naseri, Kavishwar B. Wagholikar, Shawn N. Murphy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/215ac8e4b2a54c928e603dee8e7792d6
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spelling	oai:doaj.org-article:215ac8e4b2a54c928e603dee8e7792d62021-12-02T13:49:47ZPredicting COVID-19 mortality with electronic medical records10.1038/s41746-021-00383-x2398-6352https://doaj.org/article/215ac8e4b2a54c928e603dee8e7792d62021-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00383-xhttps://doaj.org/toc/2398-6352Abstract This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65–85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.Hossein EstiriZachary H. StrasserJeffy G. KlannPourandokht NaseriKavishwar B. WagholikarShawn N. MurphyNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Hossein Estiri Zachary H. Strasser Jeffy G. Klann Pourandokht Naseri Kavishwar B. Wagholikar Shawn N. Murphy Predicting COVID-19 mortality with electronic medical records
description	Abstract This study aims to predict death after COVID-19 using only the past medical information routinely collected in electronic health records (EHRs) and to understand the differences in risk factors across age groups. Combining computational methods and clinical expertise, we curated clusters that represent 46 clinical conditions as potential risk factors for death after a COVID-19 infection. We trained age-stratified generalized linear models (GLMs) with component-wise gradient boosting to predict the probability of death based on what we know from the patients before they contracted the virus. Despite only relying on previously documented demographics and comorbidities, our models demonstrated similar performance to other prognostic models that require an assortment of symptoms, laboratory values, and images at the time of diagnosis or during the course of the illness. In general, we found age as the most important predictor of mortality in COVID-19 patients. A history of pneumonia, which is rarely asked in typical epidemiology studies, was one of the most important risk factors for predicting COVID-19 mortality. A history of diabetes with complications and cancer (breast and prostate) were notable risk factors for patients between the ages of 45 and 65 years. In patients aged 65–85 years, diseases that affect the pulmonary system, including interstitial lung disease, chronic obstructive pulmonary disease, lung cancer, and a smoking history, were important for predicting mortality. The ability to compute precise individual-level risk scores exclusively based on the EHR is crucial for effectively allocating and distributing resources, such as prioritizing vaccination among the general population.
format	article
author	Hossein Estiri Zachary H. Strasser Jeffy G. Klann Pourandokht Naseri Kavishwar B. Wagholikar Shawn N. Murphy
author_facet	Hossein Estiri Zachary H. Strasser Jeffy G. Klann Pourandokht Naseri Kavishwar B. Wagholikar Shawn N. Murphy
author_sort	Hossein Estiri
title	Predicting COVID-19 mortality with electronic medical records
title_short	Predicting COVID-19 mortality with electronic medical records
title_full	Predicting COVID-19 mortality with electronic medical records
title_fullStr	Predicting COVID-19 mortality with electronic medical records
title_full_unstemmed	Predicting COVID-19 mortality with electronic medical records
title_sort	predicting covid-19 mortality with electronic medical records
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/215ac8e4b2a54c928e603dee8e7792d6
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Predicting COVID-19 mortality with electronic medical records

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