This article is a Preprint
Preprints are preliminary research reports that have not been certified by peer review. They should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Preprints posted online allow authors to receive rapid feedback and the entire scientific community can appraise the work for themselves and respond appropriately. Those comments are posted alongside the preprints for anyone to read them and serve as a post publication assessment.
Symptoms that predict positive COVID-19 testing and hospitalization: an analysis of 9,000 patients
Preprint
in English
| medRxiv
| ID: ppmedrxiv-21261729
ABSTRACT
PurposeTo develop a reliable tool that predicts which patients are most likely to be COVID-19 positive and which ones have an increased risk of hospitalization. MethodsFrom February 2020 to April 2021, trained nurses recorded age, gender, and symptoms in an outpatient COVID-19 testing center. All positive patients were followed up by phone for 14 days or until symptom-free. We calculated the symptoms odds ratio for positive results and hospitalization and proposed a "random forest" machine-learning model to predict positive testing. ResultsA total of 8,998 patients over 16 years old underwent COVID-19 RT-PCR, with 1,914 (21.3%) positives. Fifty patients needed hospitalization (2.6% of positives), and three died (0.15%). Most common symptoms were cough, headache, sore throat, coryza, fever, myalgia (57%, 51%, 44%, 36%, 35%, 27%, respectively). Cough, fever, and myalgia predicted positive COVID-19 test, while others behaved as protective factors. The best predictors of positivity were fever plus anosmia/ageusia (OR=6.31), and cough plus anosmia/ageusia (OR=5.82), both p<0.0001. Our random forest model had an ROC-AUC of 0.72 (specificity=0.70, sensitivity=0.61, PPV=0.38, NPV=0.86). Having steady fever during the first days of infection and persistent dyspnea increased the risk of hospitalization (OR=6.66, p<0.0001 and OR=3.13, p=0.003, respectively), while anosmia-ageusia (OR=0.36, p=0.009) and coryza (OR=0.31, p=0.014) were protective. ConclusionPresent study and algorithm may help identify patients at higher risk of having SARS-COV-2 (online calculator http//wdchealth.covid-map.com/shiny/calculator/), and also disease severity and hospitalization based on symptoms presence, pattern, and duration, which can help physicians and health care providers.
cc_by_nc_nd
Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Etiology study
/
Prognostic study
/
Rct
Language:
English
Year:
2021
Document type:
Preprint