Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset

Abstract As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health r...

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Autores principales:	Mike D. Rinderknecht, Yannick Klopfenstein
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/25648bc749dd41e6a0c177a1bd17e663
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spelling	oai:doaj.org-article:25648bc749dd41e6a0c177a1bd17e6632021-12-02T16:17:26ZPredicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset10.1038/s41746-021-00482-92398-6352https://doaj.org/article/25648bc749dd41e6a0c177a1bd17e6632021-07-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00482-9https://doaj.org/toc/2398-6352Abstract As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased. Non-random train-test splits by time were repeated 100 times and led to a ROC AUC of 0.861 [0.838, 0.883] and a precision-recall AUC of 0.434 [0.414, 0.485] (median and interquartile range). The interpretability analysis confirmed evidence on major risk factors (e.g., older age, higher BMI, male gender, diabetes, and cardiovascular disease) in an efficient way compared to clinical studies, demonstrating the model validity. Such personalized predictions could enable fine-graded risk stratification for optimized care management.Mike D. RinderknechtYannick KlopfensteinNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-14 (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 Mike D. Rinderknecht Yannick Klopfenstein Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
description	Abstract As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased. Non-random train-test splits by time were repeated 100 times and led to a ROC AUC of 0.861 [0.838, 0.883] and a precision-recall AUC of 0.434 [0.414, 0.485] (median and interquartile range). The interpretability analysis confirmed evidence on major risk factors (e.g., older age, higher BMI, male gender, diabetes, and cardiovascular disease) in an efficient way compared to clinical studies, demonstrating the model validity. Such personalized predictions could enable fine-graded risk stratification for optimized care management.
format	article
author	Mike D. Rinderknecht Yannick Klopfenstein
author_facet	Mike D. Rinderknecht Yannick Klopfenstein
author_sort	Mike D. Rinderknecht
title	Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
title_short	Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
title_full	Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
title_fullStr	Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
title_full_unstemmed	Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
title_sort	predicting critical state after covid-19 diagnosis: model development using a large us electronic health record dataset
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/25648bc749dd41e6a0c177a1bd17e663
work_keys_str_mv	AT mikedrinderknecht predictingcriticalstateaftercovid19diagnosismodeldevelopmentusingalargeuselectronichealthrecorddataset AT yannickklopfenstein predictingcriticalstateaftercovid19diagnosismodeldevelopmentusingalargeuselectronichealthrecorddataset
_version_	1718384219840839680

Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset

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