TY - JOUR
T1 - Predicting COVID-19 prognosis in the ICU remained challenging
T2 - external validation in a multinational regional cohort
AU - Meijs, Daniek A M
AU - van Kuijk, Sander M J
AU - Wynants, Laure
AU - Stessel, Björn
AU - Mehagnoul-Schipper, Jannet
AU - Hana, Anisa
AU - Scheeren, Clarissa I E
AU - Bergmans, Dennis C J J
AU - Bickenbach, Johannes
AU - Vander Laenen, Margot
AU - Smits, Luc J M
AU - van der Horst, Iwan C C
AU - Marx, Gernot
AU - Mesotten, Dieter
AU - van Bussel, Bas C T
AU - CoDaP Investigators
AU - Mulder, Mark
AU - Bels, Julia
AU - Wilmes, Nick
AU - Janssen, Emma
AU - Florack, Micheline
AU - Ghossein, Chahinda
N1 - Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
PY - 2022/12
Y1 - 2022/12
N2 - OBJECTIVES: Many prediction models for coronavirus disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the intensive care unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort.STUDY DESIGN AND SETTING: In this multinational cohort study, routine data from COVID-19 patients admitted to ICUs within the Euregio Meuse-Rhine were collected from March to August 2020. COVID-19 models were selected based on model type, predictors, outcomes, and reporting. Furthermore, general ICU scores were assessed. Discrimination was assessed by area under the receiver operating characteristic curves (AUCs) and calibration by calibration-in-the-large and calibration plots. A random-effects meta-analysis was used to pool results.RESULTS: 551 patients were admitted. Mean age was 65.4 ± 11.2 years, 29% were female, and ICU mortality was 36%. Nine out of 238 published models were externally validated. Pooled AUCs were between 0.53 and 0.70 and calibration-in-the-large between -9% and 6%. Calibration plots showed generally poor but, for the 4C Mortality score and Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) score, moderate calibration.CONCLUSION: Of the nine prognostic models that were externally validated in the EICC cohort, only two showed reasonable discrimination and moderate calibration. For future pandemics, better models based on routine data are needed to support admission decision-making.
AB - OBJECTIVES: Many prediction models for coronavirus disease 2019 (COVID-19) have been developed. External validation is mandatory before implementation in the intensive care unit (ICU). We selected and validated prognostic models in the Euregio Intensive Care COVID (EICC) cohort.STUDY DESIGN AND SETTING: In this multinational cohort study, routine data from COVID-19 patients admitted to ICUs within the Euregio Meuse-Rhine were collected from March to August 2020. COVID-19 models were selected based on model type, predictors, outcomes, and reporting. Furthermore, general ICU scores were assessed. Discrimination was assessed by area under the receiver operating characteristic curves (AUCs) and calibration by calibration-in-the-large and calibration plots. A random-effects meta-analysis was used to pool results.RESULTS: 551 patients were admitted. Mean age was 65.4 ± 11.2 years, 29% were female, and ICU mortality was 36%. Nine out of 238 published models were externally validated. Pooled AUCs were between 0.53 and 0.70 and calibration-in-the-large between -9% and 6%. Calibration plots showed generally poor but, for the 4C Mortality score and Spanish Society of Infectious Diseases and Clinical Microbiology (SEIMC) score, moderate calibration.CONCLUSION: Of the nine prognostic models that were externally validated in the EICC cohort, only two showed reasonable discrimination and moderate calibration. For future pandemics, better models based on routine data are needed to support admission decision-making.
U2 - 10.1016/j.jclinepi.2022.10.015
DO - 10.1016/j.jclinepi.2022.10.015
M3 - Article
C2 - 36309146
SN - 0895-4356
VL - 152
SP - 257
EP - 268
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -