Long-Term Prognostic Value of Cognitive Impairment on Top of Frailty in Older Adults after Acute Coronary Syndrome.

Frailty is a marker of poor prognosis in older adults after acute coronary syndrome. We investigated whether cognitive impairment provides additional prognostic information. The study population consisted of a prospective cohort of 342 older (>65 years) adult survivors after acute coronary syndrome. Frailty (Fried score) and cognitive function (Pfeiffer's Short Portable Mental Status Questionnaire-SPMSQ) were assessed at discharge. The endpoints were mortality or acute myocardial infarction at 8.7-year median follow-up. Patient distribution according to SPMSQ results was: no cognitive impairment (SPMSQ = 0 errors; n = 248, 73%), mild impairment (SPMSQ = 1-2 errors; n = 52, 15%), and moderate to severe impairment (SPMSQ ≥3 errors; n = 42, 12%). A total of 245 (72%) patients died or had an acute myocardial infarction, and 216 (63%) patients died. After adjustment for clinical data, comorbidities, and Fried score, the SPMSQ added prognostic value for death or myocardial infarction (per number of errors; HR = 1.11, 95%, CI 1.04-1.19, p = 0.002) and death (HR = 1.11, 95% 1.03-1.20, p = 0.007). An SPMSQ with ≥3 errors identified the highest risk subgroup. Geriatric conditions (SPSMQ and Fried score) explained 19% and 43% of the overall chi-square of the models for predicting death or myocardial infarction and death, respectively. Geriatric assessment after acute coronary syndrome should include both frailty and cognitive function. This is particularly important given that cognitive impairment without dementia can be subclinical and thus remain undetected.

Frailty Tools for Assessment of Long-term Prognosis After Acute Coronary Syndrome.

OBJECTIVE: To evaluate the 5 components of the Fried frailty phenotype (self-reported unintentional weight loss, physical activity questionnaire, gait speed, grip strength, and self-reported exhaustion) for long-term outcomes in elderly survivors of acute coronary syndrome. METHODS: A total of 342 consecutive patients (from October 1, 2010, to February 1, 2012) were included. The 5 components of the Fried score and albumin concentration, as malnutrition index, were assessed before hospital discharge. Patients were followed up until April 2020 (median follow-up, 8.7 years). The end point was postdischarge all-cause mortality. RESULTS: Mean ± SD age was 77±7 years and mean ± SD Fried score was 2.0±1.1 points. A total of 216 (63%) patients died. After adjusting for clinical covariates, the Fried phenotype was associated with mortality (per points, hazard ratio [HR], 1.35; 95% CI, 1.17 to 1.57; P<.001). Among Fried components, physical activity (HR, 2.21; 95% CI, 1.34 to 3.65; P=.002) and gait speed (HR, 1.77; 95% CI, 1.29 to 2.43; P<.001) were the deficits independendtly associated with mortality. Albumin level provided further prognostic information (per increase in g/dL; HR, 0.63, 95% CI, 0.45 to 0.88; P=.007). The model adding the components of the Fried score and albumin level to the clinical model showed the highest risk reclassification (integrated discrimination improvement, 0.040; 95% CI, 0.018 to 0.075; P=.001; continuous net reclassification improvement, 0.291; 95% CI, 0.132 to 0.397; P=.001) in comparison with the model using clinical covariates alone. CONCLUSION: Frailty assessment using the Fried phenotype has prognostic value for long-term mortality in elderly survivors of acute coronary syndrome. Physical activity and gait speed are the predictive components of the Fried score. Albumin level provides incremental prognostic information.

Comorbidity assessment for mortality risk stratification in elderly patients with acute coronary syndrome.

BACKGROUND: The Charlson's is the most used comorbidity index. It comprises 19 comorbidities, some of which are infrequent in elderly patients with acute coronary syndrome (ACS), while some others are manifestations of cardiac disease rather than comorbidities. Our goal was to simplify comorbidity assessment in elderly non-ST-segment elevation ACS patients. METHODS: The study group consisted of 1 training (n = 920, 76 ±â€¯7 years) and 1 testing (n = 532; 84 ±â€¯4 years) cohorts. The end-point was all-cause mortality at 1-year follow-up. Comorbidities were assessed selecting those medical disorders other than cardiac disease that were independently associated with mortality by multivariable analysis. RESULTS: A total of 130 (14%) patients died in the training cohort. Six comorbidities were predictive: renal failure, anemia, diabetes, peripheral artery disease, cerebrovascular disease and chronic lung disease. The increase in the number of comorbidities yielded a gradient of risk on top of well-known clinical predictors: ≥3 comorbidities (27% mortality, HR = 1.90, 95% CI 1.20-3.03, p = .006); 2 comorbidities (16% mortality, HR = 1.29, 95% CI 0.81-2.04, p = .30); and 0-1 comorbidities (7.6% mortality, reference category). The discrimination accuracy (C-statistic = 0.80) and calibration (Hosmer-Lemeshow test, p = .20) of the predictive model using the 6 comorbidities was comparable to the predictive model using the Charlson index (C-statistic = 0.80; Hosmer-Lemeshow test, p = .70). Similar results were reproduced in the testing cohort (≥3 comorbidities: 24% mortality, HR = 2.37, 95% CI 1.25-4.49, p = .008; 2 comorbidities: 14% mortality, HR = 1.59, 95% CI 0.82-3.07, p = .20; 0-1 comorbidities: 7.5% reference category). CONCLUSION: A simplified comorbidity assessment comprising 6 comorbidities provides useful risk stratification in elderly patients with ACS.