Approximately 1/3 of patients with non–ST-segment elevation (NSTE) acute coronary syndromes (ACS) are ≥75 years of age. Risk stratification in these patients is generally difficult because supporting evidence is scarce. The investigators developed and validated a simple risk prediction score for 1-year mortality in patients ≥75 years of age presenting with NSTE ACS. The derivation cohort was the Italian Elderly ACS trial, which included 313 patients with NSTE ACS aged ≥75 years. A logistic regression model was developed to predict 1-year mortality. The validation cohort was a registry cohort of 332 patients with NSTE ACS meeting the same inclusion criteria as for the Italian Elderly ACS trial but excluded from the trial for any reason. The risk score included 5 statistically significant covariates: previous vascular event, hemoglobin level, estimated glomerular filtration rate, ischemic electrocardiographic changes, and elevated troponin level. The model allowed a maximum score of 6. The score demonstrated a good discriminating power (C statistic = 0.739) and calibration, even among subgroups defined by gender and age. When validated in the registry cohort, the scoring system confirmed a strong association with the risk for all-cause death. Moreover, a score ≥3 (the highest baseline risk group) identified a subset of patients with NSTE ACS most likely to benefit from an invasive approach. In conclusion, the risk for 1-year mortality in patients ≥75 years of age with NSTE ACS is substantial and can be predicted through a score that can be easily derived at the bedside at hospital presentation. The score may help in guiding treatment strategy.
Population aging and improvements in survival have contributed to a worldwide increase in the number of patients aged ≥75 years with acute coronary syndromes (ACS). Despite the vast amount of published information regarding the treatment of ACS, there is a paucity of data to guide the evaluation and management of ACS in older adults, as only a few published clinical trials included elderly patients. Patients ≥75 years of age represent 1/3 of patients with non–ST-segment elevation (NSTE) ACS and account for about 60% of overall mortality due to NSTE ACS. Nevertheless, they constitute only 9% of clinical trial populations.
In this context, risk assessment is useful in assessing prognosis and guiding the management of older adults with NSTE ACS. Although the most common prognostic models for NSTE ACS incorporate age as determinant of prognosis, none of the previous risk assessment models has been specifically validated in a clinical trial data set of older adults with NSTE ACS.
At this regard, the Italian Elderly ACS study ( ClinicalTrials.gov identifier NCT00510185 ) offers a unique opportunity to gain further information on these issues, because it specifically enrolled a prospective population of patients ≥75 years of age with NSTE ACS. The objectives of this study were to (1) describe overall 1-year mortality rates in a contemporary cohort of patients ≥75 years of age with NSTE ACS, (2) develop and validate a mortality risk prediction model, and (3) incorporate these findings into a simplified risk score to support clinical decision making. We also evaluated the ability of such a tool to identify subsets of patients with NSTE ACS who would benefit most from an early invasive approach during hospitalization.
Methods
The Italian Elderly ACS study enrolled patients ≥75 years of age with NSTE ACS admitted <48 hours after the most recent ischemic symptoms and showing ischemic changes on electrocardiography or elevated cardiac markers or both.
Details of the study design, setting, and population included in this trial cohort have been published previously. Briefly, the study involved 23 centers and was approved by the ethics committees of the participating hospitals. Overall, 313 patients were randomly assigned to either an early invasive strategy with coronary angiography and revascularization <72 hours after admission or an initially conservative strategy with angiography only in the case of recurrent ischemic symptoms.
Patients meeting the same inclusion criteria for the trial cohort, but excluded from the clinical trial for any reason, were enrolled in a registry (n = 332). Patients included in the registry were treated according to hospital routine and physician judgment in specific cases. Signature of an informed consent form was a prerequisite for enrollment either in the trial or in the registry.
For the purpose of the present analysis, all-cause death was considered the study outcome. It included in-hospital and 1-year mortality. Vital status was assessed through 30 days and every 6 months (follow-up visits) until study completion.
Analyses were performed using Stata version 12 (StataCorp LP, College Station, Texas) and R version 2.9.2 (R Foundation for Statistical Computing, Vienna, Austria). Results are presented as mean ± SD for continuous variables and as percentages for categorical data. Student’s t test and analysis of variance were used to evaluate differences in continuous variables between groups as appropriate. Comparisons between categorical variables were performed with chi-square tests. Univariate relations between baseline characteristics and mortality were assessed by logistic regression analysis, and variables were ranked by z score. Predictors of all-cause death were derived from baseline variables available at the time of initial hospital presentation. These variables included demographics (age, gender, weight, and body mass index), cardiac risk factors (diabetes mellitus, hypertension, hyperlipidemia, and current or recent smoking), medical history (history of ACS, heart failure, stroke, and previous revascularization), clinical presentation features (electrocardiographic findings, and the left ventricular ejection fraction as detected by echocardiography), initial laboratory values (serum creatinine, estimated glomerular filtration rate [eGFR] according to the Cockcroft-Gault equation, serum glucose, hemoglobin, and troponin level), and procedural variables (coronary angiography with or without revascularization). Elevated troponin levels were defined by each investigator according to the upper normal limits at the individual participating centers. Thus, we modeled a multivariate analysis that included covariates that yielded significance in the univariate analysis. To include predictors in the multivariate model, we used the Akaike information criterion and the Bayesian information criterion to compare different multivariate models on the basis of their fit to the data.
The risk scoring system was developed on the basis of the final logistic regression model. Specifically, variables that were independently and significantly associated with mortality in the final multivariate model were assigned weighted integer coefficient values on the basis of their regression coefficients (rounded to the nearest integer to get their point values). A patient’s total risk score was calculated by adding up the points for all existing risk factors. For each risk score, the predicted risk for mortality was calculated according to established methods. The comparison of observed and predicted risk for mortality for each risk score unit was used to evaluate the accuracy of the risk scoring system. The discriminatory capacity of the risk score was also assessed using the area under the receiver-operating characteristics curve (C statistic) as an index of model performance. The risk scoring system was derived from the trial cohort (derivation cohort) and validated using the registry data (validation cohort).
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