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A Symptom-Based Algorithm for Rapid Clinical Diagnosis of COVID-19 in Adults With High-Risk Exposure
Shinji Inaba1,2,3, Shuntaro Ikeda1, Naoki Tsuda4
1Department of Cardiology, Pulmonology, Hypertension, and Nephrology, Ehime University Graduate School of Medicine, Toon, JPN.
Insights
A new symptom-based algorithm aids in diagnosing coronavirus disease 2019 (COVID-19) in high-risk individuals without test kits. Stratified by vaccination status, it offers a reliable, accessible clinical diagnosis tool.
Area of Science:
- Infectious Diseases
- Clinical Diagnostics
- Public Health
Background:
- Clinical diagnosis of COVID-19 in Japan relies on symptoms and close contact history, lacking objectivity.
- Current symptom-based diagnosis for coronavirus disease 2019 (COVID-19) needs enhancement for reliability, especially in high-risk populations.
Purpose of the Study:
- To develop and validate a symptom-based diagnostic algorithm for COVID-19.
- Stratify the algorithm by vaccination status to improve diagnostic accuracy for individuals with high-risk exposure.
Main Methods:
- Retrospective, single-center study in Japan (April 2021-May 2022).
- Developed a predictive algorithm comparing symptoms of COVID-19 positive and negative individuals with high-risk exposure.
- Stratified analysis based on vaccination status.
Main Results:
- The combination of fever, sore throat, and cough showed 100% specificity but low sensitivity for COVID-19 diagnosis.
- Among vaccinated individuals, sore throat and cough were key indicators; fever was more predictive in unvaccinated individuals.
- The developed algorithm achieved 65.3% sensitivity and 88.5% specificity, comparable to rapid antigen tests.
Conclusions:
- The symptom-based algorithm provides a reliable, objective method for clinical COVID-19 diagnosis.
- This tool can facilitate rapid and accessible diagnosis in resource-limited or high-demand settings.
- Vaccination status is a crucial factor influencing symptom presentation and diagnostic indicators for COVID-19.
Abstract:
Objectives In Japan, clinical diagnosis based solely on symptoms, without the use of test kits, has been adopted to enable the rapid identification of individuals infected with coronavirus disease 2019 (COVID-19). A history of close contact with COVID-19 patients is a prerequisite for such symptom-based diagnosis. However, the current diagnostic criteria lack objectivity. This study aimed to develop a symptom-based algorithm stratified by vaccination status to support more reliable clinical diagnosis of COVID-19 among individuals with high-risk exposure. Methods This retrospective, single-center study was conducted in Japan between April 2021 and May 2022. An algorithm for predicting COVID-19 infection was developed by comparing symptoms in COVID-19-positive and COVID-19-negative individuals with high-risk exposure. Analyses were stratified by vaccination status, given its potential influence on symptom presentation. Patients A total of 179 individuals with high-risk exposure to COVID-19 patients were included in the analysis. Results The most common setting of close contact was within households or among roommates (55.3%, 99/179), followed by workplace or school settings (26.3%, 47/179). The combination of all three symptoms-fever, sore throat, and cough-demonstrated 100% specificity but low sensitivity, irrespective of vaccination status. Among vaccinated individuals, the combination of sore throat and cough was a more reliable diagnostic indicator, whereas fever was more predictive among unvaccinated individuals. Conclusion The symptom-based diagnostic algorithm developed in this study demonstrated a sensitivity of 65.3% and a specificity of 88.5%, approaching the diagnostic performance of rapid antigen testing. This algorithm may facilitate simple, rapid, and accessible clinical diagnosis of COVID-19 in resource-limited or high-demand settings.
