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Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study

BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test c...

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Autores principales: Klann, Jeffrey G, Strasser, Zachary H, Hutch, Meghan R, Kennedy, Chris J, Marwaha, Jayson S, Morris, Michele, Samayamuthu, Malarkodi Jebathilagam, Pfaff, Ashley C, Estiri, Hossein, South, Andrew M, Weber, Griffin M, Yuan, William, Avillach, Paul, Wagholikar, Kavishwar B, Luo, Yuan, Omenn, Gilbert S, Visweswaran, Shyam, Holmes, John H, Xia, Zongqi, Brat, Gabriel A, Murphy, Shawn N
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119395/
https://www.ncbi.nlm.nih.gov/pubmed/35476727
http://dx.doi.org/10.2196/37931
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author Klann, Jeffrey G
Strasser, Zachary H
Hutch, Meghan R
Kennedy, Chris J
Marwaha, Jayson S
Morris, Michele
Samayamuthu, Malarkodi Jebathilagam
Pfaff, Ashley C
Estiri, Hossein
South, Andrew M
Weber, Griffin M
Yuan, William
Avillach, Paul
Wagholikar, Kavishwar B
Luo, Yuan
Omenn, Gilbert S
Visweswaran, Shyam
Holmes, John H
Xia, Zongqi
Brat, Gabriel A
Murphy, Shawn N
author_facet Klann, Jeffrey G
Strasser, Zachary H
Hutch, Meghan R
Kennedy, Chris J
Marwaha, Jayson S
Morris, Michele
Samayamuthu, Malarkodi Jebathilagam
Pfaff, Ashley C
Estiri, Hossein
South, Andrew M
Weber, Griffin M
Yuan, William
Avillach, Paul
Wagholikar, Kavishwar B
Luo, Yuan
Omenn, Gilbert S
Visweswaran, Shyam
Holmes, John H
Xia, Zongqi
Brat, Gabriel A
Murphy, Shawn N
author_sort Klann, Jeffrey G
collection PubMed
description BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)–based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as “admitted with COVID-19” (incidental) versus specifically admitted for COVID-19 (“for COVID-19”). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.
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spelling pubmed-91193952022-05-20 Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study Klann, Jeffrey G Strasser, Zachary H Hutch, Meghan R Kennedy, Chris J Marwaha, Jayson S Morris, Michele Samayamuthu, Malarkodi Jebathilagam Pfaff, Ashley C Estiri, Hossein South, Andrew M Weber, Griffin M Yuan, William Avillach, Paul Wagholikar, Kavishwar B Luo, Yuan Omenn, Gilbert S Visweswaran, Shyam Holmes, John H Xia, Zongqi Brat, Gabriel A Murphy, Shawn N J Med Internet Res Original Paper BACKGROUND: Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)–based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE: The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS: From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as “admitted with COVID-19” (incidental) versus specifically admitted for COVID-19 (“for COVID-19”). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS: EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS: A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research. JMIR Publications 2022-05-18 /pmc/articles/PMC9119395/ /pubmed/35476727 http://dx.doi.org/10.2196/37931 Text en ©Jeffrey G Klann, Zachary H Strasser, Meghan R Hutch, Chris J Kennedy, Jayson S Marwaha, Michele Morris, Malarkodi Jebathilagam Samayamuthu, Ashley C Pfaff, Hossein Estiri, Andrew M South, Griffin M Weber, William Yuan, Paul Avillach, Kavishwar B Wagholikar, Yuan Luo, The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), Gilbert S Omenn, Shyam Visweswaran, John H Holmes, Zongqi Xia, Gabriel A Brat, Shawn N Murphy. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 18.05.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Klann, Jeffrey G
Strasser, Zachary H
Hutch, Meghan R
Kennedy, Chris J
Marwaha, Jayson S
Morris, Michele
Samayamuthu, Malarkodi Jebathilagam
Pfaff, Ashley C
Estiri, Hossein
South, Andrew M
Weber, Griffin M
Yuan, William
Avillach, Paul
Wagholikar, Kavishwar B
Luo, Yuan
Omenn, Gilbert S
Visweswaran, Shyam
Holmes, John H
Xia, Zongqi
Brat, Gabriel A
Murphy, Shawn N
Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title_full Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title_fullStr Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title_full_unstemmed Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title_short Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study
title_sort distinguishing admissions specifically for covid-19 from incidental sars-cov-2 admissions: national retrospective electronic health record study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119395/
https://www.ncbi.nlm.nih.gov/pubmed/35476727
http://dx.doi.org/10.2196/37931
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