TY - JOUR
T1 - Boosting the accuracy of existing models by updating and extending
T2 - using a multicenter COVID-19 ICU cohort as a proxy
AU - Meijs, Daniek A.M.
AU - Wynants, Laure
AU - van Kuijk, Sander M.J.
AU - Scheeren, Clarissa I.E.
AU - Hana, Anisa
AU - Mehagnoul-Schipper, Jannet
AU - Stessel, Björn
AU - Vander Laenen, Margot
AU - Cox, Eline G.M.
AU - Sels, Jan Willem E.M.
AU - Smits, Luc J.M.
AU - Bickenbach, Johannes
AU - Mesotten, Dieter
AU - van der Horst, Iwan C.C.
AU - Marx, Gernot
AU - van Bussel, Bas C.T.
AU - CoDaP Investigators
AU - Heijnen, Nanon
AU - Mulder, Mark
AU - Bels, Julia
AU - Wilmes, Nick
AU - Hendriks, Charlotte
AU - Janssen, Emma
AU - Florack, Micheline
AU - Ghossein, Chahinda
N1 - Funding Information:
This work was supported by the \"Interreg Euregio Meuse-Rhine\" (Covid Data Platform (CoDaP) grant: Interreg-EMR 187). The funding source was not involved in the study design, data collection, data analysis, data interpretation, writing process, and decision to submit for publication.
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11/1
Y1 - 2024/11/1
N2 - 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.
AB - 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.
U2 - 10.1038/s41598-024-70333-6
DO - 10.1038/s41598-024-70333-6
M3 - Article
SN - 2045-2322
VL - 14
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 26344
ER -