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Prognostic Value of a Laboratory Index of Frailty in Older Patients Hospitalized for COVID-19: The COMEPA Study

  • Autori: Veronese, N.; Briganò, V.; Ciriminna, S.; Ganci, A.; Bifara, F.; Pollicino, F.; Garlisi, M.C.; Tantillo, F.; Amodeo, S.; Rizzo, G.; Vernuccio, L.; Mansueto, P.; Licata, A.; Giannitrapani, L.; Dominguez, L.J.; Barbagallo, M.
  • Anno di pubblicazione: 2024
  • Tipologia: Articolo in rivista
  • OA Link: http://hdl.handle.net/10447/638178

Abstract

Background: Several indexes based on clinical and laboratory tests to identify frailty and to predict mortality have been produced. Only two studies, mixing clinical and laboratory parameters were made about a frailty index made of laboratory tests (FI-Lab) and mortality in older patients hospitalized for COVID-19. The aim of this study was to explore the accuracy and precision of an FI-Lab constructed with some common bio-humoral tests and mortality in a cohort of patients hospitalized for COVID-19. Methods: The FI-Lab was constructed using 40 different biohumoral tests during the first four days of hospitalization, with a score from 0 to 1. The association between FI-Lab and mortality was assessed using a multivariate Cox's regression analysis, reported as hazard ratios (HRs) and 95% confidence intervals (CIs). The accuracy of the FI-Lab was reported as area under the curve (AUC) and the precision with the C-Index. Results: 376 patients (mean age: 65 years; 53.7% males) were initially included. During the follow-up period, 41 deceased. After adjusting for five different factors, an FI-Lab value >0.54, the median value of our cohort, was associated with a relative risk about five times greater than lower values. Modeling FI-LAB as a continous variable, each increase in 0.01 points was associated with an increased risk in mortality of 8.4% (HR=1.084; 95%CI: 1.039-2.044). The FI-Lab was highly accurate (AUC=0.91; 95%CI: 0.87-0.95) and precise (C-Index=0.81) in predicting death. Conclusions: A simple index based on common laboratory tests can be used to predict mortality among older people hospitalized for COVID-19.