Abstract

ObjectivesNo previous studies have assessed the role of the FRAIL scale in predicting long-term outcomes in older patients with acute coronary syndromes (ACS). Design, Setting and ParticipantsThe multicenter observational LONGEVO-SCA registry included unselected patients ≥80 years of age with ACS from 44 centers. A comprehensive geriatric assessment was performed during hospitalization. MeasuresFrailty was measured by the FRAIL scale. For the purpose of this study, main outcome measured was mortality or readmission at 24 months. ResultsA total of 498 patients were included. Mean age was 84.3 years. A total of 198 patients (33.1%) were prefrail and 135 (27.1%) frail. Patients who were prefrail and frail had a higher degree of comorbidities, and higher prevalence of disability, cognitive impairment, and nutritional risk. A total of 165 out of 498 patients (33.1%) died, and 331 patients (66.7%) died or were readmitted at 24 months. Both prefrailty and frailty were associated with a higher mortality compared with robust patients (P < .001). The incidence of mortality or readmission was also higher in patients who were prefrail or frail (P < .001). After adjusting for potential confounders, the association between frailty and mortality or readmission remained significant (hazard ratio 1.28 for prefrailty and hazard ratio 1.96 for frailty, P < .001). The FRAIL scale showed an optimal ability for predicting mortality or readmission (area under the receiver operating characteristics curve 0.86, 95% confidence interval 0.83‒0.89). The area under the receiver operating characteristics curve from the Global Registry of Acute Coronary Events risk score was 0.89. No significant differences were observed between both AUC values (P = .163). Conclusions and ImplicationsThe FRAIL scale independently predicted long-term outcomes in older patients with ACS. The predictive ability of this scale was comparable to the strongly recommended Global Registry of Acute Coronary Events risk score. Frailty assessment is mandatory for improving risk prediction in these complex patients.

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