A Symptom-Based Rule for Diagnosis of COVID-19
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COVID-19
A Symptom-Based Rule for Diagnosis of COVID-19 David S. Smith 1
&
Elizabeth A. Richey 1 & Wendy L. Brunetto 1
Accepted: 15 October 2020 # Springer Nature Switzerland AG 2020
Abstract SARS-CoV-19 PCR testing has a turn-around time that makes it impractical for real-time decision-making, and current point-ofcare tests have limited sensitivity, with frequent false negatives. The study objective was to develop a clinical prediction rule to use with a point-of-care test to diagnose COVID-19 in symptomatic outpatients. A standardized clinical questionnaire was administered prior to SARS-CoV-2 PCR testing. Data was extracted by a physician blinded to the result status. Individual symptoms were combined into 326 unique clinical phenotypes. Multivariable logistic regression was used to identify independent predictors of COVID-19, from which a weighted clinical prediction rule was developed, to yield stratified likelihood ratios for varying scores. A retrospective cohort of 120 SARS-CoV-2-positive cases and 120 SARS-CoV-2-negative matched controls among symptomatic outpatients in a Connecticut HMO was used for rule development. A temporally distinct cohort of 40 cases was identified for validation of the rule. Clinical phenotypes independently associated with COVID-19 by multivariable logistic regression include loss of taste or smell (olfactory phenotype, 2 points) and fever and cough (febrile respiratory phenotype, 1 point). Wheeze or chest tightness (reactive airways phenotype, − 1 point) predicted non-COVID-19 respiratory viral infection. The AUC of the model was 0.736 (0.674–0.798). Application of a weighted C19 rule yielded likelihood ratios for COVID-19 diagnosis for varying scores ranging from LR 15.0 for 3 points to LR 0.1 for − 1 point. Using a Bayesian diagnostic approach, combining community prevalence with the evidence-based C19 rule to adjust pretest probability, clinicians can apply a point of care test with limited sensitivity across a range of clinical scenarios to differentiate COVID-19 infection from influenza and respiratory viral infection. Keywords COVID-19 . Clinical prediction rule . Phenotypes
Introduction While COVID-19 outbreaks continue to emerge, accurate diagnosis in real-time by office-based clinicians is limited by a number of factors. Patients with COVID-19 present along with cases of seasonal respiratory viruses including influenza, which have overlapping symptoms. SARS-CoV-19 PCR testing has a turn-around time that makes it impractical for immediate decision-making in the clinic. Finally, rapid antigen point-of-care (POC) tests, when available, have limited sensitivity, with frequent false negatives [1]. Accurate interpretation of a POC rapid test result in clinical practice relies upon accurate pretest probability estimation [2].
Clinical prediction rules have previously been used to aid in interpretation of POC diagnostic test results in clinical diagnosis, for example in acute pharyngitis [3]. We anticipated the need in outpatient clinics of an evidence-based clinical pre
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