Proposed Clinical Indicators for Efficient Screening and Testing for COVID-19 Infection from Classification and Regression Trees (CART) Analysis

Abstract

Background: The introduction and rapid transmission of SARS CoV2 in the United States resulted in implementation of methods to assess, mitigate and contain the resulting COVID-19 disease based on limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptom complexes may differ.

Methods: Classification and regression trees (CART) recursive partitioning created a decision tree classifying enrollees into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years who were enrolled from March 29-April 26, 2020 were included. Presence or absence of SARSCoV2 was the target variable.

Results: Of 736 tested, 55 were positive for SARS-CoV2. Cases significantly more often reported chills, loss of taste/smell, diarrhea, fever, nausea/vomiting and contact with a COVID-19 case, but less frequently reported shortness of breath and sore throat. A 7-terminal node tree with a sensitivity of 96% and specificity of 53%, and an AUC of 78% was developed. The positive predictive value for this tree was 14% while the negative predictive value was 99%. Almost half (44%) of the participants could be ruled out as likely non-cases without testing.

Discussion: Among those referred for testing, negative responses to three questions could classify about half of tested persons with low risk for SARS-CoV2 and would save limited testing resources. These questions are: was the patient in contact with a COVID-19 case? Has the patient experienced 1) a loss of taste or smell; or 2) nausea or vomiting? The outpatient symptoms of COVID-19 appear to be broader than the well-known inpatient syndrome.