Predicting severe outcomes in Covid-19 related illness using only patient demographics, comorbidities and symptoms.

Autor: Ryan C; University of Michigan Medical School, Ann Arbor, MI, USA., Minc A; University of Michigan Medical School, Ann Arbor, MI, USA., Caceres J; University of Michigan Medical School, Ann Arbor, MI, USA., Balsalobre A; University of Puerto Rico School of Medicine, San Juan, PR, USA., Dixit A; Indian Institute of Information Technology Guwahati, India., Ng BK; Baptist Health South Florida, Miami, FL, USA., Schmitzberger F; Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA., Syed-Abdul S; Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan., Fung C; Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA. Electronic address: chfung@med.umich.edu.
Jazyk: angličtina
Zdroj: The American journal of emergency medicine [Am J Emerg Med] 2021 Jul; Vol. 45, pp. 378-384. Date of Electronic Publication: 2020 Sep 09.
DOI: 10.1016/j.ajem.2020.09.017
Abstrakt: Objective: Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data.
Materials and Methods: We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 RESULTS: Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.06), dyspnea (OR, 2.56; 95% CI: 1.51-4.33), male sex (OR, 1.70; 95% CI: 1.10-2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17-4.16) and CKD (OR, 1.76; 95% CI: 1.01-3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33-0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76).
Conclusions: Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure.
Competing Interests: Declaration of Competing Interest CR, AM, JC, AB, AD, BN, FS, SS, CF report no conflicts of interest.
(Published by Elsevier Inc.)
Databáze: MEDLINE
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