Predictive usefulness of RT-PCR testing in different patterns of Covid-19 symptomatology: analysis of a French cohort of 12,810 outpatients.

The AP-HP/Universities/Inserm COVID-19 Research Collaboration; Writing Committee; Apra, Caroline; Caucheteux, Charlotte; Mensch, Arthur; Mansour, Jenny; Bernaux, Mélodie; Dechartres, Agnès; Debuc, Erwan; Lescure, Xavier; Dinh, Aurélien; Yordanov, Youri; Jourdain, Patrick; Data Science Committee; Paris, Nicolas; Gramfort, Alexandre; Scientific Committee; Aime-Eusebi, Amélie; Bleibtreu, Alexandre; Deconinck, Laurène

Reverse transcriptase polymerase chain reaction (RT-PCR) is a key tool to diagnose Covid-19. Yet it may not be the most efficient test in all patients. In this paper, we develop a clinical strategy for prescribing RT-PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19, including 12,810 patients tested by RT-PCR. We use a machine-learning algorithm (decision tree) in order to predict RT-PCR results based on the clinical presentation. We show that symptoms alone are sufficient to predict RT-PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of RT-PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of RT-PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, RT-PCR provides little added diagnostic value. We propose a prescribing strategy based on clinical presentation that can improve the global efficiency of RT-PCR testing.

REVERSE transcriptase polymerase chain reaction; COVID-19; OUTPATIENTS

Scientific Reports, 2021, Vol 11, Issue 1, p1

2045-2322

Academic Journal

10.1038/s41598-021-99991-6