Dataset Information

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

ABSTRACT:

Background

We compared outcomes in inpatients and outpatients, pre-COVID-19, who were infected with either coronavirus or influenza.

Methods

Using deidentified electronic health records data from the Geisinger-Regeneron partnership, we compared patients with RT-PCR-positive tests for the 4 common coronaviruses (229E, HKU1, NL63, OC43) or influenza (A and B) from June 2016 to February 2019.

Results

Overall, 52 833 patients were tested for coronaviruses and influenza. For patients ≥21 years old, 1555 and 3991 patient encounters had confirmed positive coronavirus and influenza tests, respectively. Both groups had similar intensive care unit (ICU) admission rates (7.2% vs 6.1%, P = .12), although patients with coronavirus had significantly more pneumonia (15% vs 7.4%, P < .001) and higher death rate within 30 days (4.9% vs 3.0%, P < .001). After controlling for other covariates, coronavirus infection still had a higher risk of death and pneumonia than influenza (odds ratio, 1.64 and 2.05, P < .001), with no significant difference in ICU admission rates.

Conclusions

Common coronaviruses cause significant morbidity, with potentially worse outcomes than influenza. Identifying a subset of patients who are more susceptible to poor outcomes from common coronavirus infections may help plan clinical interventions in patients with suspected infections.

SUBMITTER: Li D

PROVIDER: S-EPMC7797744 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

Li Dadong D Wolk Donna M DM Cantor Michael N MN

The Journal of infectious diseases 20210601 11

<h4>Background</h4>We compared outcomes in inpatients and outpatients, pre-COVID-19, who were infected with either coronavirus or influenza.<h4>Methods</h4>Using deidentified electronic health records data from the Geisinger-Regeneron partnership, we compared patients with RT-PCR-positive tests for the 4 common coronaviruses (229E, HKU1, NL63, OC43) or influenza (A and B) from June 2016 to February 2019.<h4>Results</h4>Overall, 52 833 patients were tested for coronaviruses and influenza. For pat ...[more]

PMID: 33011809

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Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

Dataset Information

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

Background

Methods

Results

Conclusions

Publications

Comparing Clinical Characteristics of Influenza and Common Coronavirus Infections Using Electronic Health Records.

Similar Datasets

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