The Role of Symptom Clusters in Triage of COVID-19 Patients

• Published in: Quality management in health care Vol. 32; no. Supplement 1; pp. S21 - S28
• Main Authors: Wojtusiak, Janusz, Bagais, Wejdan, Vang, Jee, Guralnik, Elina, Roess, Amira, Alemi, Farrokh
• Format: Journal Article
• Language: English
• Published: United States Wolters Kluwer Health, Inc 01-01-2023; Lippincott Williams & Wilkins Ovid Technologies
• Subjects: Coronaviruses; COVID-19; COVID-19 - epidemiology; Female; Humans; Independent variables; Male; Medical diagnosis; Patients; Quality of care; ROC Curve; Syndrome; Triage; Variables
• ISSN: 1063-8628; 1550-5154
• DOI: 10.1097/QMH.0000000000000399

COVID-19 manifests with a broad range of symptoms. This study investigates whether clusters of respiratory, gastrointestinal, or neurological symptoms can be used to diagnose COVID-19.
We surveyed symptoms of 483 subjects who had completed COVID-19 laboratory tests in the last 30 days. The survey collected data on demographic characteristics, self-reported symptoms for different types of infections within 14 days of onset of illness, and self-reported COVID-19 test results. Robust LASSO regression was used to create 3 nested models. In all 3 models, the response variable was the COVID-19 test result. In the first model, referred to as the "main effect model," the independent variables were demographic characteristics, history of chronic symptoms, and current symptoms. The second model, referred to as the "hierarchical clustering model," added clusters of variables to the list of independent variables. These clusters were established through hierarchical clustering. The third model, referred to as the "interaction-terms model," also added clusters of variables to the list of independent variables; this time clusters were established through pairwise and triple-way interaction terms. Models were constructed on a randomly selected 80% of the data and accuracy was cross-validated on the remaining 20% of the data. The process was bootstrapped 30 times. Accuracy of the 3 models was measured using the average of the cross-validated area under the receiver operating characteristic curves (AUROCs).
In 30 bootstrap samples, the main effect model had an AUROC of 0.78. The hierarchical clustering model had an AUROC of 0.80. The interaction-terms model had an AUROC of 0.81. Both the hierarchical cluster model and the interaction model were significantly different from the main effect model (α = .04). Patients with different races/ethnicities, genders, and ages presented with different symptom clusters.
Using clusters of symptoms, it is possible to more accurately diagnose COVID-19 among symptomatic patients.