• Luca Miele, Marianxhela Dajko, Maria Chiara Savino, Nicola D Capocchiano, Valentino Calvez, Antonio Liguori, Carlotta Masciocchi, Lorenzo Vetrone, Irene Mignini, Tommaso Schepis, Giuseppe Marrone, Marco Biolato, Alfredo Cesario, Stefano Patarnello, Andrea Damiani, Antonio Grieco, Vincenzo Valentini, Antonio Gasbarrini, and Gemelli against COVID Group.
• Dipartimento di Scienze Mediche e Chirurgiche (DiSMeC), Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, 8, Largo Gemelli, 00168, Rome, Italy. luca.miele@policlinicogemelli.it.
• Intern Emerg Med. 2023 Aug 1; 18 (5): 141514271415-1427.

AbstractIncreased values of the FIB-4 index appear to be associated with poor clinical outcomes in COVID-19 patients. This study aimed to develop and validate predictive mortality models, using data upon admission of hospitalized patients in four COVID-19 waves between March 2020 and January 2022. A single-center cohort study was performed on consecutive adult patients with Covid-19 admitted at the Fondazione Policlinico Gemelli IRCCS (Rome, Italy). Artificial intelligence and big data processing were used to retrieve data. Patients and clinical characteristics of patients with available FIB-4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive mortality models during the four waves of the COVID-19 pandemic. A logistic regression model was applied to the training and test set (75%:25%). The model's performance was assessed by receiver operating characteristic (ROC) curves. A total of 4936 patients were included. Hypertension (38.4%), cancer (12.15%) and diabetes (16.3%) were the most common comorbidities. 23.9% of patients were admitted to ICU, and 12.6% had mechanical ventilation. During the study period, 762 patients (15.4%) died. We developed a multivariable logistic regression model on patient data from all waves, which showed that the FIB-4 score > 2.53 was associated with increased mortality risk (OR = 4.53, 95% CI 2.83-7.25; p ≤ 0.001). These data may be useful in the risk stratification at the admission of hospitalized patients with COVID-19.© 2023. The Author(s).

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