Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.

<h4>Background</h4>Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how th...

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Autores principales:	Jimmy Phuong, Stephanie L Hyland, Stephen J Mooney, Dustin R Long, Kenji Takeda, Monica S Vavilala, Kenton O'Hara
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6e144c9fded34c128ca7b68f881df3da
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spelling	oai:doaj.org-article:6e144c9fded34c128ca7b68f881df3da2021-12-02T20:07:52ZSociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.1932-620310.1371/journal.pone.0258339https://doaj.org/article/6e144c9fded34c128ca7b68f881df3da2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258339https://doaj.org/toc/1932-6203<h4>Background</h4>Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies.<h4>Methods and findings</h4>We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics.<h4>Conclusions</h4>Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.Jimmy PhuongStephanie L HylandStephen J MooneyDustin R LongKenji TakedaMonica S VavilalaKenton O'HaraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258339 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jimmy Phuong Stephanie L Hyland Stephen J Mooney Dustin R Long Kenji Takeda Monica S Vavilala Kenton O'Hara Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
description	<h4>Background</h4>Despite increased testing efforts and the deployment of vaccines, COVID-19 cases and death toll continue to rise at record rates. Health systems routinely collect clinical and non-clinical information in electronic health records (EHR), yet little is known about how the minimal or intermediate spectra of EHR data can be leveraged to characterize patient SARS-CoV-2 pretest probability in support of interventional strategies.<h4>Methods and findings</h4>We modeled patient pretest probability for SARS-CoV-2 test positivity and determined which features were contributing to the prediction and relative to patients triaged in inpatient, outpatient, and telehealth/drive-up visit-types. Data from the University of Washington (UW) Medicine Health System, which excluded UW Medicine care providers, included patients predominately residing in the Seattle Puget Sound area, were used to develop a gradient-boosting decision tree (GBDT) model. Patients were included if they had at least one visit prior to initial SARS-CoV-2 RT-PCR testing between January 01, 2020 through August 7, 2020. Model performance assessments used area-under-the-receiver-operating-characteristic (AUROC) and area-under-the-precision-recall (AUPR) curves. Feature performance assessments used SHapley Additive exPlanations (SHAP) values. The generalized pretest probability model using all available features achieved high overall discriminative performance (AUROC, 0.82). Performance among inpatients (AUROC, 0.86) was higher than telehealth/drive-up testing (AUROC, 0.81) or outpatient testing (AUROC, 0.76). The two-week test positivity rate in patient ZIP code was the most informative feature towards test positivity across visit-types. Geographic and sociodemographic factors were more important predictors of SARS-CoV-2 positivity than individual clinical characteristics.<h4>Conclusions</h4>Recent geographic and sociodemographic factors, routinely collected in EHR though not routinely considered in clinical care, are the strongest predictors of initial SARS-CoV-2 test result. These findings were consistent across visit types, informing our understanding of individual SARS-CoV-2 risk factors with implications for deployment of testing, outreach, and population-level prevention efforts.
format	article
author	Jimmy Phuong Stephanie L Hyland Stephen J Mooney Dustin R Long Kenji Takeda Monica S Vavilala Kenton O'Hara
author_facet	Jimmy Phuong Stephanie L Hyland Stephen J Mooney Dustin R Long Kenji Takeda Monica S Vavilala Kenton O'Hara
author_sort	Jimmy Phuong
title	Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
title_short	Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
title_full	Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
title_fullStr	Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
title_full_unstemmed	Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.
title_sort	sociodemographic and clinical features predictive of sars-cov-2 test positivity across healthcare visit-types.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/6e144c9fded34c128ca7b68f881df3da
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Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types.

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