ArticleFrontiers in cellular and infection microbiology2021
A Model Predicting Mortality of Hospitalized Covid-19 Patients Four Days After Admission: Development, Internal and Temporal-External Validation.
Article in Frontiers in cellular and infection microbiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04351724 (A Multicenter, Randomized, Active Controlled, Open Label, Platform Trial on the Efficacy and Safety of Experimental Therapeutics for Patients With COVID-19), which is not on this map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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.
A Multicenter, Randomized, Active Controlled, Open Label, Platform Trial on the Efficacy and Safety of Experimental Therapeutics for Patients With COVID-19 (Caused by Infection With Severe Acute Respiratory Syndrome Coronavirus-2)
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic models in COVID-19 infection that predict severity: a systematic review.European journal of epidemiology · 2023Pooled it
- Visualizing a marker's degrees of necessity and of sufficiency in the predictiveness curve.BMC medical research methodology · 2025Trial
- Admission biomarkers and COVID-19 mortality: A retrospective study during Vietnam's pandemic peak.Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures) · 2026Article
- Machine Learning-Based Prediction for Axial Pain Following Expansive Unilateral Open-Door Laminoplasty: A Retrospective Cohort Study.Pain and therapy · 2026Article
- Construction and temporal external validation of interpretable machine-learning models for predicting tigecycline-associated hypofibrinogenemia.European journal of clinical pharmacology · 2026Article
- Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges.BMC medical informatics and decision making · 2024Article
- Machine learning models for predicting severe COVID-19 outcomes in hospitals.Informatics in medicine unlocked · 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
- External Validation of Mortality Scores among High-Risk COVID-19 Patients: A Romanian Retrospective Study in the First Pandemic Year.Journal of clinical medicine · 2022Article
- Exploratory COVID-19 death risk score based on basic laboratory tests and physiological clinical measurements.World journal of emergency medicine · 2022Article
- COVID-19 hospitalizations and patients' age at admission: The neglected importance of data variability for containment policies.Frontiers in public health · 2022Article
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Authors and funding
30 authors.
Funding
Abstract
Objective: To develop and validate a prognostic model for in-hospital mortality after four days based on age, fever at admission and five haematological parameters routinely measured in hospitalized Covid-19 patients during the first four days after admission. Methods: Haematological parameters measured during the first 4 days after admission were subjected to a linear mixed model to obtain patient-specific intercepts and slopes for each parameter. A prediction model was built using logistic regression with variable selection and shrinkage factor estimation supported by bootstrapping. Model development was based on 481 survivors and 97 non-survivors, hospitalized before the occurrence of mutations. Internal validation was done by 10-fold cross-validation. The model was temporally-externally validated in 299 survivors and 42 non-survivors hospitalized when the Alpha variant (B.1.1.7) was prevalent. Results: The final model included age, fever on admission as well as the slope or intercept of lactate dehydrogenase, platelet count, C-reactive protein, and creatinine. Tenfold cross validation resulted in a mean area under the receiver operating characteristic curve (AUROC) of 0.92, a mean calibration slope of 1.0023 and a Brier score of 0.076. At temporal-external validation, application of the previously developed model showed an AUROC of 0.88, a calibration slope of 0.95 and a Brier score of 0.073. Regarding the relative importance of the variables, the (apparent) variation in mortality explained by the six variables deduced from the haematological parameters measured during the first four days is higher (explained variation 0.295) than that of age (0.210). Conclusions: The presented model requires only variables routinely acquired in hospitals, which allows immediate and wide-spread use as a decision support for earlier discharge of low-risk patients to reduce the burden on the health care system. Clinical Trial Registration: Austrian Coronavirus Adaptive Clinical Trial (ACOVACT); ClinicalTrials.gov, identifier NCT04351724.
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