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Most published prediction models for Coronavirus Disease 2019 (COVID-19) were poorly reported, at high risk of bias, and heterogeneous in model performance. To tackle methodological challenges faced in previous prediction studies, we investigated whether model updating and extending improves mortality prediction, using the Intensive Care Unit (ICU) as a proxy. All COVID-19 patients admitted to seven ICUs in the Euregio-Meuse Rhine during the first pandemic wave were included. The 4C Mortality and SEIMC scores were selected as promising prognostic models from an external validation study. Five predictors could be estimated based on cohort size. TRIPOD guidelines were followed and logistic regression analyses with the linear predictor, APACHE II score, and country were performed. Bootstrapping with backward selection was applied to select variables for the final model. Additionally, shrinkage was performed. Model discrimination was displayed as optimism-corrected areas under the ROC curve and calibration by calibration slopes and plots. The mortality rate of the 551 included patients was 36%. Discrimination of the 4C Mortality and SEIMC scores increased from 0.70 to 0.74 and 0.70 to 0.73 and calibration plots improved compared to the original models after updating and extending. Mortality prediction can be improved after updating and extending of promising models., (© 2024. The Author(s).)

Objectives: To investigate healthcare system-driven variation in general characteristics, interventions, and outcomes in coronavirus disease 2019 (COVID-19) patients admitted to the ICU within one Western European region across three countries., Design: Multicenter observational cohort study., Setting: Seven ICUs in the Euregio Meuse-Rhine, one region across Belgium, The Netherlands, and Germany., Patients: Consecutive COVID-19 patients supported in the ICU during the first pandemic wave., Interventions: None., Measurements and Main Results: Baseline demographic and clinical characteristics, laboratory values, and outcome data were retrieved after ethical approval and data-sharing agreements. Descriptive statistics were performed to investigate country-related practice variation. From March 2, 2020, to August 12, 2020, 551 patients were admitted. Mean age was 65.4 ± 11.2 years, and 29% were female. At admission, Acute Physiology and Chronic Health Evaluation II scores were 15.0 ± 5.5, 16.8 ± 5.5, and 15.8 ± 5.3 (p = 0.002), and Sequential Organ Failure Assessment scores were 4.4 ± 2.7, 7.4 ± 2.2, and 7.7 ± 3.2 (p < 0.001) in the Belgian, Dutch, and German parts of Euregio, respectively. The ICU mortality rate was 22%, 42%, and 44%, respectively (p < 0.001). Large differences were observed in the frequency of organ support, antimicrobial/inflammatory therapy application, and ICU capacity. Mixed-multivariable logistic regression analyses showed that differences in ICU mortality were independent of age, sex, disease severity, comorbidities, support strategies, therapies, and complications., Conclusions: COVID-19 patients admitted to ICUs within one region, the Euregio Meuse-Rhine, differed significantly in general characteristics, applied interventions, and outcomes despite presumed genetic and socioeconomic background, admission diagnosis, access to international literature, and data collection are similar. Variances in healthcare systems' organization, particularly ICU capacity and admission criteria, combined with a rapidly spreading pandemic might be important drivers for the observed differences. Heterogeneity between patient groups but also healthcare systems should be presumed to interfere with outcomes in coronavirus disease 2019., Competing Interests: The authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)