In:
PLOS ONE, Public Library of Science (PLoS), Vol. 15, No. 12 ( 2020-12-28), p. e0244629-
Abstract:
Our objective is to compare the predictive accuracy of four recently established outcome models of patients hospitalized with coronavirus disease 2019 (COVID-19) published between January 1 st and May 1 st 2020. Methods We used data obtained from the Veterans Affairs Corporate Data Warehouse (CDW) between January 1 st , 2020, and May 1 st 2020 as an external validation cohort. The outcome measure was hospital mortality. Areas under the ROC (AUC) curves were used to evaluate discrimination of the four predictive models. The Hosmer–Lemeshow (HL) goodness-of-fit test and calibration curves assessed applicability of the models to individual cases. Results During the study period, 1634 unique patients were identified. The mean age of the study cohort was 68.8±13.4 years. Hypertension, hyperlipidemia, and heart disease were the most common comorbidities. The crude hospital mortality was 29% (95% confidence interval [CI] 0.27–0.31). Evaluation of the predictive models showed an AUC range from 0.63 (95% CI 0.60–0.66) to 0.72 (95% CI 0.69–0.74) indicating fair to poor discrimination across all models. There were no significant differences among the AUC values of the four prognostic systems. All models calibrated poorly by either overestimated or underestimated hospital mortality. Conclusions All the four prognostic models examined in this study portend high-risk bias. The performance of these scores needs to be interpreted with caution in hospitalized patients with COVID-19.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0244629
DOI:
10.1371/journal.pone.0244629.g001
DOI:
10.1371/journal.pone.0244629.g002
DOI:
10.1371/journal.pone.0244629.t001
DOI:
10.1371/journal.pone.0244629.t002
DOI:
10.1371/journal.pone.0244629.t003
DOI:
10.1371/journal.pone.0244629.t004
DOI:
10.1371/journal.pone.0244629.s001
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2020
detail.hit.zdb_id:
2267670-3
Permalink