Development and Validation of a Prognostic Nomogram Based on Clinical and CT Features for Adverse Outcome Prediction in Patients with COVID-19.
COVID-19
CT
Coronavirus
Nomogram
Prognosis
Journal
Korean journal of radiology
ISSN: 2005-8330
Titre abrégé: Korean J Radiol
Pays: Korea (South)
ID NLM: 100956096
Informations de publication
Date de publication:
08 2020
08 2020
Historique:
received:
20
04
2020
revised:
10
05
2020
accepted:
20
05
2020
entrez:
18
7
2020
pubmed:
18
7
2020
medline:
25
7
2020
Statut:
ppublish
Résumé
The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.
Identifiants
pubmed: 32677385
pii: 21.1007
doi: 10.3348/kjr.2020.0485
pmc: PMC7369204
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1007-1017Informations de copyright
Copyright © 2020 The Korean Society of Radiology.
Déclaration de conflit d'intérêts
The authors have no potential conflicts of interest to disclose.
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