Characterizing COVID-19: A chief complaint based approach

Am J Emerg Med. 2021 Jul:45:398-403. doi: 10.1016/j.ajem.2020.09.019. Epub 2020 Sep 14.

Abstract

Background: The COVID-19 pandemic has inundated emergency departments with patients exhibiting a wide array of symptomatology and clinical manifestations. We aim to evaluate the chief complaints of patients presenting to our ED with either suspected or confirmed COVID-19 to better understand the clinical presentation of this pandemic.

Methods: This study was a retrospective computational analysis that investigated the chief complaints of all confirmed and suspected COVID-19 cases presenting to our adult ED (patients aged 22 and older) using a variety of data mining methods. Our study employed descriptive statistics to analyze the set of complaints that are most common, hierarchical clustering analysis to provide a nuanced way of identifying complaints that co-occur, and hypothesis testing identify complaint differences among age differences.

Results: A quantitative analysis of 5015 ED visits of COVID-suspected patients (1483 confirmed COVID-positive patients) identified 209 unique chief complaints. Of the 209 chief complaints, fever and shortness of breath were the most prevalent initial presenting symptoms. In the subset of COVID-19 confirmed positive cases, we discovered seven distinct clusters of presenting complaints. Patients over 65 years of age were more likely to present with weakness and altered mental status.

Conclusions: Our research highlights an important aspect of the evaluation and management of COVID-19 patients in the emergency department. Our study identified most common chief complaints, chief complaints differences across age groups, and 7 distinct groups of COVID-19 symptoms. This large-scale effort to classify the most commonly reported symptoms in ED patients provides public health officials and providers with data for identifying COVID-19 cases.

Keywords: COVID-19; Chief complaint; Coronavirus; Informatics; Pandemic.

MeSH terms

  • COVID-19 / epidemiology*
  • Comorbidity
  • Emergency Service, Hospital / statistics & numerical data*
  • Humans
  • Mental Disorders / epidemiology*
  • Pandemics*
  • Retrospective Studies
  • SARS-CoV-2
  • United States / epidemiology