Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves

Abstract

Increased 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.

Keywords: COVID-19; FIB-4; Fibrosis; Liver function test; SARS-COV2.

Fig. 1

A Daily hospital admissions and B daily deaths at Fondazione Policlinico Universitario A. Gemelli IRCCS across COVID waves

Fig. 2

Flowchart of the study

Fig. 3

Kaplan–Meier survival probability curves with FIB-4 as covariate

Fig. 4

Kaplan–Meier survival probability curves across COVID waves for patients A with Fib-4 score < 2.53, B with Fib-4 score ≥ 2.53. p-values of the Log-rank test

Fig. 5

Univariate logistic regression curves. A–D show the probability of death versus Fib4 components. E shows the probability of death versus FIB-4. As shown in B and E, mortality risk curves are steeper during the first and second waves, the same AST and FIB-4 values are associated with a higher probability of death than in the third and fourth waves. The same behaviour does not emerge with the other FIB-4 components