In:
PLOS ONE, Public Library of Science (PLoS), Vol. 17, No. 9 ( 2022-9-22), p. e0274171-
Abstract:
The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers’ performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0274171
DOI:
10.1371/journal.pone.0274171.g001
DOI:
10.1371/journal.pone.0274171.g002
DOI:
10.1371/journal.pone.0274171.g003
DOI:
10.1371/journal.pone.0274171.g004
DOI:
10.1371/journal.pone.0274171.g005
DOI:
10.1371/journal.pone.0274171.g006
DOI:
10.1371/journal.pone.0274171.g007
DOI:
10.1371/journal.pone.0274171.g008
DOI:
10.1371/journal.pone.0274171.g009
DOI:
10.1371/journal.pone.0274171.g010
DOI:
10.1371/journal.pone.0274171.t001
DOI:
10.1371/journal.pone.0274171.t002
DOI:
10.1371/journal.pone.0274171.s001
DOI:
10.1371/journal.pone.0274171.s002
DOI:
10.1371/journal.pone.0274171.s003
DOI:
10.1371/journal.pone.0274171.s004
DOI:
10.1371/journal.pone.0274171.s005
DOI:
10.1371/journal.pone.0274171.s006
DOI:
10.1371/journal.pone.0274171.s007
DOI:
10.1371/journal.pone.0274171.s008
DOI:
10.1371/journal.pone.0274171.s009
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2267670-3
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