BACKGROUND: Frailty predicts mortality after acute coronary syndrome (ACS). The standard frailty scales, such as the Fried score, consist of a variety of questionnaires and physical tests. Our aim was to investigate easily available clinical data and blood markers to predict frailty at discharge, in elderly patients after ACS. METHODS: A total of 342 patients older than 65 years, survivors after ACS, were included. A high number of clinical variables were collected. In addition, blood markers potentially linked to frailty and related to the processes of inflammation, coagulation, hormonal dysregulation, nutrition, renal dysfunction, and heart dysfunction were determined. Frailty was evaluated using the Fried score at discharge. The main outcome was frailty defined by a Fried score ≥ 3 points. Secondary endpoints were mortality and myocardial infarction at 30-month median follow-up. RESULTS: A total of 116 patients were frail. Seven clinical variables or biomarkers predicted frailty: age ≥ 75 years, female, prior ischemic heart disease, admission heart failure, haemoglobin ≤ 12.5 g/dL, vitamin D ≤ 9 ng/mL, and cystatin-C ≥ 1.2 mg/L. This model based on clinical data and biomarkers showed an excellent discrimination accuracy for frailty (C-statistic = 0.818). During the follow-up, 105 patients died and 137 died or suffered myocardial infarction. The clinical data and biomarker model (C-statistics = 0.730 and 0.691) performed better than the Fried score (C-statistics = 0.676 and 0.650) for death and death or myocardial infarction, respectively. CONCLUSIONS: Easy available clinical data and biomarkers can identify frail patients at discharge after ACS and predict outcomes better than the standard Fried's frailty scale.
BACKGROUND: Frailty predicts mortality after acute coronary syndrome (ACS). The standard frailty scales, such as the Fried score, consist of a variety of questionnaires and physical tests. Our aim was to investigate easily available clinical data and blood markers to predict frailty at discharge, in elderly patients after ACS. METHODS: A total of 342 patients older than 65 years, survivors after ACS, were included. A high number of clinical variables were collected. In addition, blood markers potentially linked to frailty and related to the processes of inflammation, coagulation, hormonal dysregulation, nutrition, renal dysfunction, and heart dysfunction were determined. Frailty was evaluated using the Fried score at discharge. The main outcome was frailty defined by a Fried score ≥ 3 points. Secondary endpoints were mortality and myocardial infarction at 30-month median follow-up. RESULTS: A total of 116 patients were frail. Seven clinical variables or biomarkers predicted frailty: age ≥ 75 years, female, prior ischemic heart disease, admission heart failure, haemoglobin ≤ 12.5 g/dL, vitamin D ≤ 9 ng/mL, and cystatin-C ≥ 1.2 mg/L. This model based on clinical data and biomarkers showed an excellent discrimination accuracy for frailty (C-statistic = 0.818). During the follow-up, 105 patients died and 137 died or suffered myocardial infarction. The clinical data and biomarker model (C-statistics = 0.730 and 0.691) performed better than the Fried score (C-statistics = 0.676 and 0.650) for death and death or myocardial infarction, respectively. CONCLUSIONS: Easy available clinical data and biomarkers can identify frail patients at discharge after ACS and predict outcomes better than the standard Fried's frailty scale.
Authors: Pedro J Flores-Blanco; Ángel López-Cuenca; James L Januzzi; Francisco Marín; Marianela Sánchez-Martínez; Miriam Quintana-Giner; Ana I Romero-Aniorte; Mariano Valdés; Sergio Manzano-Fernández Journal: Clin Cardiol Date: 2016-06-01 Impact factor: 2.882
Authors: Olívia Maria Costa Figueredo; Mariana Barbosa Câmara-Souza; Talita Malini Carletti; Maria da Luz Rosário de Sousa; Renata Cunha Matheus Rodrigues Garcia Journal: Int Dent J Date: 2020-01-09 Impact factor: 2.607