| Contributors: |
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; Tocco, Martina; Pretto, Chiara; Tamburini, Enrico; Fregolent, Davide; Francesco Pirelli, Pier; Marchesin, Davide; Perona, Matteo; Franchetti, Nicola; Della Paolera, Michele; 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; Antonio Cal??, Mauro; 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 |
| Description: |
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 ... |