Role of Frailty Index-Laboratory to predict COVID-19 mortality: a prospective study - Elda De Vita; Nicola Veronese; Giacomo Guido; Luisa Frallonardo; Sergio Cotugno; Giorgia Manco Cesari; Marinella Cibelli; Alessandra Vigna; Davide Capruzzi; Monica Fiorella; Carmen Rita Santoro; Gaetano Brindicci; et al. (Frontiers in Public Health, 2025)
IntroductionThe COVID-19 pandemic has disproportionately impacted frail individuals, highlighting the urgent need for effective prognostic tools to improve patient outcomes. Early identification of at-risk individuals can optimize management and resource allocation, reducing mortality and morbidity. This study evaluates the Frailty Index-Laboratory (FI-LAB) as a predictor of mortality in COVID-19 patients.MethodsWe included all COVID-19 patients admitted to the Clinic of Infectious Diseases of the “Azienda Ospedaliera Policlinico di Bari” from March 2020 to February 2024. FI-LAB scores were calculated using 37 laboratory parameters obtained within the first 4 days of hospitalization. Mortality data were collected up to 90 days post-admission. Cox regression analysis, adjusting for demographics, comorbidities, COVID-19 symptoms, and vaccination status, was employed to examine the relationship between FI-LAB scores and mortality.ResultsOne thousand, four hundred ninety-two patients were included in the study population, the mean age was 57.2 years (SD = 15.9), with 56.6% being male. Patients in the highest FI-LAB tertile (>0.432) exhibited a 17.10-fold higher risk of death compared to those in the lowest tertile (<0.135), same result has been shown in the intermediate FI-LAB scores (0.135–0.432) when compared to the lowest tertile. Additionally, each 0.10-point increase in FI-LAB was linked to a nearly twofold increase in mortality hazard (HR = 1.99, 95% CI 1.69–2.37, p < 0.0001).ConclusionFrailty Index-Laboratory is a robust and practical tool for predicting mortality in hospitalized COVID-19 patients, aiding early identification of high-risk individuals. Implementing FI-LAB enhances patient management and resource allocation. Further studies are needed to confirm its effectiveness across diverse populations and healthcare settings.