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A prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

Abstract：

:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in thousands of deaths in the world. Information about prediction model of prognosis of SARS-CoV-2 infection is scarce. We used machine learning for processing laboratory findings of 110 patients with SARS-CoV-2 pneumonia (including 51 non-survivors and 59 discharged patients). The maximum relevance minimum redundancy (mRMR) algorithm and the least absolute shrinkage and selection operator logistic regression model were used for selection of laboratory features. Seven laboratory features selected in the model were: prothrombin activity, urea, white blood cell, interleukin-2 receptor, indirect bilirubin, myoglobin, and fibrinogen degradation products. The signature constructed using the seven features had 98% [93%, 100%] sensitivity and 91% [84%, 99%] specificity in predicting outcome of SARS-CoV-2 pneumonia. Thus it is feasible to establish an accurate prediction model of outcome of SARS-CoV-2 pneumonia based on laboratory findings.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Wu G,Zhou S,Wang Y,Lv W,Wang S,Wang T,Li X

doi

10.1038/s41598-020-71114-7

subject

Has Abstract

pub_date

2020-08-20 00:00:00

pages

14042

issue

1

issn

2045-2322

pii

10.1038/s41598-020-71114-7

journal_volume

10

pub_type

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