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

Am J Emerg Med. 2021 Jul;45:378-384. doi: 10.1016/j.ajem.2020.09.017. Epub 2020 Sep 9.

Abstract

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.

Keywords: Covid-19; Remote triage; Severe; Symptoms.

MeSH terms

  • Aged
  • COVID-19 / epidemiology*
  • Case-Control Studies
  • Comorbidity
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Male
  • Middle Aged
  • Pandemics*
  • Retrospective Studies
  • Risk Factors
  • SARS-CoV-2
  • Tertiary Care Centers
  • Triage / methods
  • Triage / statistics & numerical data*
  • United States / epidemiology