ArticlePloS one2020
Comparison of in-hospital mortality risk prediction models from COVID-19.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review With Cumulative Meta-Analysis.Frontiers in medicine · 2021Pooled it
- Tracking the evolution of biomarker efficacy in SARS-CoV-2: a global meta-analyses series.Respiratory research · 2026Article
- Fast and interpretable mortality risk scores for critical care patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.Scientific reports · 2024Article
- Deep learning in public health: Comparative predictive models for COVID-19 case forecasting.PloS one · 2024Article
- Predicting the mortality of patients with Covid-19: A machine learning approach.Health science reports · 2023Article
- Assess and validate predictive performance of models for in-hospital mortality in COVID-19 patients: A retrospective cohort study in the Netherlands comparing the value of registry data with high-granular electronic health records.International journal of medical informatics · 2022Observational
- Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.Journal of King Saud University. Computer and information sciences · 2022Article
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- Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries.PloS one · 2022Article
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- Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse.Intensive care medicine experimental · 2021Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveOur 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 1st and May 1st 2020.
methodsWe used data obtained from the Veterans Affairs Corporate Data Warehouse (CDW) between January 1st, 2020, and May 1st 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.
resultsDuring 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.
conclusionsAll 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.
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