An improved multivariate model that distinguishes COVID-19 from seasonal flu and other respiratory diseases.
Aging (Albany NY)
; 12(20): 19938-19944, 2020 10 21.
Article
in English
| MEDLINE | ID: covidwho-884122
ABSTRACT
COVID-19 shared many symptoms with seasonal flu, and community-acquired pneumonia (CAP) Since the responses to COVID-19 are dramatically different, this multicenter study aimed to develop and validate a multivariate model to accurately discriminate COVID-19 from influenza and CAP. Three independent cohorts from two hospitals (50 in discovery and internal validation sets, and 55 in the external validation cohorts) were included, and 12 variables such as symptoms, blood tests, first reverse transcription-polymerase chain reaction (RT-PCR) results, and chest CT images were collected. An integrated multi-feature model (RT-PCR, CT features, and blood lymphocyte percentage) established with random forest algorism showed the diagnostic accuracy of 92.0% (95% CI 73.9 - 99.1) in the training set, and 96. 6% (95% CI 79.6 - 99.9) in the internal validation cohort. The model also performed well in the external validation cohort with an area under the receiver operating characteristic curve of 0.93 (95% CI 0.79 - 1.00), an F1 score of 0.80, and a Matthews correlation coefficient (MCC) of 0.76. In conclusion, the developed multivariate model based on machine learning techniques could be an efficient tool for COVID-19 screening in nonendemic regions with a high rate of influenza and CAP in the post-COVID-19 era.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia, Viral
/
Models, Statistical
/
Coronavirus Infections
Type of study:
Cohort study
/
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
/
Young adult
Language:
English
Journal:
Aging (Albany NY)
Journal subject:
Geriatrics
Year:
2020
Document Type:
Article
Affiliation country:
Aging.104132
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