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. 2023 Sep 26;18(9):e0291580.
doi: 10.1371/journal.pone.0291580. eCollection 2023.

Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study

Corinne M Hohl  1   2 , Amber Cragg  1 , Elizabeth Purssell  1   3 , Finlay A McAlister  4   5 , Daniel K Ting  1   2 , Frank Scheuermeyer  1   6 , Maja Stachura  1   7 , Lars Grant  8   9 , John Taylor  1 , Josephine Kanu  1 , Jeffrey P Hau  1 , Ivy Cheng  10   11 , Clare L Atzema  10   12   13 , Rajan Bola  1 , Laurie J Morrison  11   13 , Megan Landes  11   14 , Jeffrey J Perry  15   16 , Rhonda J Rosychuk  17 ; Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) investigators for the Network of Canadian Emergency Researchers; Canadian Critical Care Trials Group
Affiliations

Comparing methods to classify admitted patients with SARS-CoV-2 as admitted for COVID-19 versus with incidental SARS-CoV-2: A cohort study

Corinne M Hohl et al. PLoS One. .

Erratum in

Abstract

Introduction: Not all patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection develop symptomatic coronavirus disease 2019 (COVID-19), making it challenging to assess the burden of COVID-19-related hospitalizations and mortality. We aimed to determine the proportion, resource utilization, and outcomes of SARS-CoV-2 positive patients admitted for COVID-19, and assess the impact of using the Center for Disease Control's (CDC) discharge diagnosis-based algorithm and the Massachusetts state department's drug administration-based classification system on identifying admissions for COVID-19.

Methods: In this retrospective cohort study, we enrolled consecutive SARS-CoV-2 positive patients admitted to one of five hospitals in British Columbia between December 19, 2021 and May 31,2022. We completed medical record reviews, and classified hospitalizations as being primarily for COVID-19 or with incidental SARS-CoV-2 infection. We applied the CDC algorithm and the Massachusetts classification to estimate the difference in hospital days, intensive care unit (ICU) days and in-hospital mortality and calculated sensitivity and specificity.

Results: Of 42,505 Emergency Department patients, 1,651 were admitted and tested positive for SARS-CoV-2, with 858 (52.0%, 95% CI 49.6-54.4) admitted for COVID-19. Patients hospitalized for COVID-19 required ICU admission (14.0% versus 8.2%, p<0.001) and died (12.6% versus 6.4%, p<0.001) more frequently compared with patients with incidental SARS-CoV-2. Compared to case classification by clinicians, the CDC algorithm had a sensitivity of 82.9% (711/858, 95% CI 80.3%, 85.4%) and specificity of 98.1% (778/793, 95% CI 97.2%, 99.1%) for COVID-19-related admissions and underestimated COVID-19 attributable hospital days. The Massachusetts classification had a sensitivity of 60.5% (519/858, 95% CI 57.2%, 63.8%) and specificity of 78.6% (623/793, 95% CI 75.7%, 81.4%) for COVID-19-related admissions, underestimating total number of hospital and ICU bed days while overestimating COVID-19-related intubations, ICU admissions, and deaths.

Conclusion: Half of SARS-CoV-2 hospitalizations were for COVID-19 during the Omicron wave. The CDC algorithm was more specific and sensitive than the Massachusetts classification, but underestimated the burden of COVID-19 admissions.

Trial registration: Clinicaltrials.gov, NCT04702945.

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Conflict of interest statement

Drs. Perry and Atzema have peer reviewed mid-career salary support awards from the Heart and Stroke Foundation of Ontario. Dr. Hohl is supported by a Michael Smith Foundation Health Professional Investigator Award. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Flow diagram of enrolled patients.
ED = emergency department; SARS-CoV-2 = Severe acute respiratory syndrome coronavirus 2.
Fig 2
Fig 2. Adjusted odds of ventilation, critical care admission or mortality among 1,651 SARS-CoV-2 positive patients who were hospitalized primarily for COVID-19 versus with incidental SARS-CoV-2 determined using either clinician decision, the CDC algorithm or the Massachusetts methods.
CI = confidence interval. Models for each outcome were adjusted for age, sex, presenting hospital, secondary immunodeficiency (i.e., active malignant neoplasm, transplant recipient, moderate/severe liver disease), obesity, Omicron subvariant, illicit substance use, and COVID-19 vaccinations. The reference standard is hospitalized with incidental SARS-CoV-2. Adjusted odds ratios and 95% confidence intervals for factors included in each regression are presented in S9–S11 Tables (blue circle = S9 Table, green circle = S10 Table, red circle = S11 Table).
Fig 3
Fig 3. Potential adjustment to the CDC algorithm suggested by Adjei et al. to improve sensitivity for resource intensive cases for the purposes of health care resource planning [6].
*Text taken directly from Adjei et al. with the exception of the italicized addition.
See this image and copyright information in PMC

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