Author:
,Apra Caroline,Caucheteux Charlotte,Mensch Arthur,Mansour Jenny,Bernaux Mélodie,Dechartres Agnès,Debuc Erwan,Lescure Xavier,Dinh Aurélien,Yordanov Youri,Jourdain Patrick,Mensch Arthur,Caucheteux Charlotte,Apra Caroline,Mansour Jenny,Paris Nicolas,Gramfort Alexandre,Aime-Eusebi Amélie,Apra Caroline,Bleibtreu Alexandre,Debuc Erwan,Dechartres Agnès,Deconinck Laurène,Dinh Aurélien,Jourdain Patrick,Katlama Christine,Lebel Josselin,Lescure François-Xavier,Yordanov Youri,Artigou Yves,Banzet Amélie,Boucheron Elodie,Boudier Christiane,Buzenac Edouard,Chapron Marie-Claire,Chekaoui Dalhia,De Bastard Laurent,Debuc Erwan,Dinh Aurélien,Grenier Alexandre,Haas Pierre-Etienne,Hody Julien,Jarraya Michèle,Jourdain Patrick,Lacaille Louis,Le Guern Aurélie,Leclert Jeremy,Male Fanny,Marchand-Arvier Jerôme,Martin-Blondet Emmanuel,Nassour Apolinne,Ourahou Oussama,Penn Thomas,Ribardiere Ambre,Robin Nicolas,Rouge Camille,Schmidt Nicolas,Villie Pascaline, , , ,
Abstract
AbstractReverse 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.
Publisher
Springer Science and Business Media LLC