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Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold JC, Wolff G, Kelm M, Beil M, Sviri S, van Heerden PV, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen FH, Moreno R, Cecconi M, Leaver S, De Lange DW, Guidet B, Flaatten H, Osmani V
Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation
Disease-course adapting machine learning prognostication models in critically ill elderly COVID-19 patients: a multi-centre cohort study with external validation
Christian Jung;
Behrooz Mamandipoor;
Jesper Fjølner;
Raphael Bruno;
Bernhard Wernly;
Antonio Artigas;
Bernardo Bollen Pinto;
Joerg C. Schefold;
Georg Wolff;
Malte Kelm;
Michael Beil;
Sigal Sviri;
Peter Vernon van Heerden;
Wojciech Szczeklik;
Miroslaw Czuczwar;
Muhammed Elhadi;
Michael Joannidis;
Sandra Oeyen;
Tilemachos Zafeiridis;
Brian Marsh;
Finn H. Andersen;
Rui Moreno;
Maurizio Cecconi;
Susannah Leaver;
Dylan W. De Lange;
Bertrand Guidet;
Hans Flaatten;
Venet Osmani
ABSTRACT
Background:
The SARS-CoV-2 coronavirus disease (COVID-19) pandemic is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk.
Objective:
This study aimed to evaluate machine-learning based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporate multifaceted clinical information on the evolution of the disease.
Methods:
Patient data was obtained from 151 ICUs from 26 countries (COVIP study). In total, 1,432 elderly (aged 70 years and above) COVID-19 positive patients admitted to an intensive care unit. Different models based on the Sequential Organ Failure Assessment (SOFA), Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were derived as baseline models that included admission variables only. Then, we included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with the baseline group. Furthermore, we derived baseline and final models on an EU patient cohort and externally validated them on a non-EU cohort that included Asian, African and Americas patients.
Results:
Final models that incorporated clinical events and time-to-event provided superior performance with AUC of 0.81 (95% CI 0.804-0.811), with respect to both, the baseline models that used admission variables only, and conventional ICU prediction model (SOFA-score, p<0.001).
Conclusions:
Integrating important clinical events and time-to-event information led to superior 30-day mortality prediction accuracy compared to models based on the admission information and conventional ICU prediction models. The present study shows that machine-learning models provide may support complex decision-making in critically ill elderly COVID-19 patients. Clinical Trial: NCT04321265
Citation
Please cite as:
Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold JC, Wolff G, Kelm M, Beil M, Sviri S, van Heerden PV, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen FH, Moreno R, Cecconi M, Leaver S, De Lange DW, Guidet B, Flaatten H, Osmani V
Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation