COVID-19 ICU mortality prediction: a machine learning approach using SuperLearner algorithm
Authors: Lorenzoni Giulia , Sella Nicolò , Boscolo Annalisa , Azzolina Danila , Bartolotta Patrizia , Pasin Laura , Pettenuzzo Tommaso , De Cassai Alessandro , Baratto Fabio , Toffoletto Fabio , De Rosa Silvia , Fullin Giorgio , Peta Mario , Rosi Paolo , Polati Enrico , Zanella Alberto , Grasselli Giacomo , Pesenti Antonio , Navalesi Paolo , Gregori Dario ,   for the VENETO ICU Network, Tocco Martina , Pretto Chiara , Tamburini Enrico , Fregolent Davide , Pirelli Pier Francesco , Marchesin Davide , Perona Matteo , Franchetti Nicola , Paolera Michele Della , Simoni Caterina , Falcioni Tatiana , Tresin Alessandra , Schiavolin Chiara , Schiavi Aldo , Vathi Sonila , Sartori Daria , Sorgato Alice , Pistollato Elisa , Linassi Federico , Gianoli Sara , Gaspari Silvia , Gruppo Francesco , Maggiolo Alessandra , Giurisato Elena , Furlani Elisa , Calore Alvise , Serra Eugenio , Pittarello Demetrio , Tiberio Ivo , Bond Ottavia , Michieletto Elisa , Muraro Luisa , Peralta Arianna , Persona Paolo , Petranzan Enrico , Zarantonello Francesco , Graziano Alessandro , Piasentini Eleonora , Bernardi Lorenzo , Pianon Roberto , Mazzon Davide , Poole Daniele , Badii Flavio , Bosco Enrico , Agostini Moreno , Trevisiol Paride , Farnia Antonio , Altafini Lorella , Calò Mauro Antonio , Meggiolaro Marco , Lazzari Francesco , Martinello Ivan , Papaccio Francesco , di Gregorio Guido , Bonato Alfeo , Sgarabotto Camilla , Montacciani Francesco , Alessandra Parnigotto , Gagliardi Giuseppe , Ferraro Gioconda , Ongaro Luigi , Baiocchi Marco , Danzi Vinicio , Zanatta Paolo , Donadello Katia , Gottin Leonardo , Sinigaglia Ezio , da Ros Alessandra , Marchiotto Simonetta , Bassanini Silvia , Zamperini Massimo , Daroui Ivan , Mosaner Walter 
Published: September 2021

Background

Since the beginning of coronavirus disease 2019 (COVID-19), the development of predictive models has sparked relevant interest due to the initial lack of knowledge about diagnosis, treatment, and prognosis. The present study aimed at developing a model, through a machine learning approach, to predict intensive care unit (ICU) mortality in COVID-19 patients based on predefined clinical parameters.

Results

Observational multicenter cohort study. All COVID-19 adult patients admitted to 25 ICUs belonging to the VENETO ICU network (February 28th 2020-april 4th 2021) were enrolled. Patients admitted to the ICUs before 4th March 2021 were used for model training (“training set”), while patients admitted after the 5th of March 2021 were used for external validation (“test set 1”). A further group of patients (“test set 2”), admitted to the ICU of IRCCS Ca’ Granda Ospedale Maggiore Policlinico of Milan, was used for external validation. A SuperLearner machine learning algorithm was applied for model development, and both internal and external validation was performed. Clinical variables available for the model were (i) age, gender, sequential organ failure assessment score, Charlson Comorbidity Index score (not adjusted for age), Palliative Performance Score; (ii) need of invasive mechanical ventilation, non-invasive mechanical ventilation, O2 therapy, vasoactive agents, extracorporeal membrane oxygenation, continuous venous-venous hemofiltration, tracheostomy, re-intubation, prone position during ICU stay; and (iii) re-admission in ICU.

One thousand two hundred ninety-three (80%) patients were included in the “training set”, while 124 (8%) and 199 (12%) patients were included in the “test set 1” and “test set 2,” respectively. Three different predictive models were developed. Each model included different sets of clinical variables. The three models showed similar predictive performances, with a training balanced accuracy that ranged between 0.72 and 0.90, while the cross-validation performance ranged from 0.75 to 0.85. Age was the leading predictor for all the considered models.

Conclusions

Our study provides a useful and reliable tool, through a machine learning approach, for predicting ICU mortality in COVID-19 patients. In all the estimated models, age was the variable showing the most important impact on mortality.