Clinical prediction model of main adverse cardiovascular and cerebrovascular events for elderly patients with acute coronary syndrome

Abstract Background Age is an independent factor for adverse outcomes in acute coronary syndrome (ACS). With the accelerated aging process, there is an increasing incidence of ACS in the elderly; however, research on this population is underrepresented. Due to the unique presence of multiple diseases and organ changes in the elderly, specialized risk assessment tools are required. Purpose To explore the risk factors for poor prognosis of elderly patients with ACS and establishing a risk stratification tool. Methods The derivation cohort consisted of 1674 elderly ACS patients consecutively enrolled in a single center from January 2013 to December 2013. The validation cohort included 2333 elderly ACS patients consecutively enrolled in nine medical centers from January 2015 to May 2019. All patients were followed up for 2 years, and the study endpoint was defined as major adverse cardiovascular and cerebrovascular events (MACCE). Model selection was performed using Cox regression and the Akaike Information Criterion (AIC) to construct the final risk assessment model. The performance of the predictive model was evaluated in the derivation and validation cohorts using the c-index, receiver operating characteristic (ROC) curves, and calibration curves. Results A total of 1,674 elderly ACS patients (70.97±4.68 years, 23.24% female) were included in the derivation cohort, and 210 cases (12.54%) of MACCE were recorded during the 2-year follow-up period. In the validation cohort, there were 2,333 elderly ACS patients (72.13±5.82 years, 35.83% female), with 364 cases (15.60%) of MACCE documented during the 2-year follow-up. Initially, 25 variables were preliminarily selected based on the results of univariate Cox regression and the clinical importance of these variables. Subsequently, these variables were included in a multivariable Cox model, and model selection was performed based on the AIC, resulting in the final selection of 10 variables (including age, cardiac insufficiency, current smoker, DAPT use, history of PCI or CABG, IABP use, number of lesions, residual SYNTAX score, preoperative HGB, and preoperative PLT). Then we developed and validated a nomogram to predict the 2-year MACCE in elderly ACS patients based on the assessment of 10 clinically easily obtainable variables during hospitalization. In both the derivation and validation cohorts, the nomogram demonstrated good discriminative ability (AUC of 0.723 and 0.699, c-index of 0.727 and 0.661, respectively) and calibration (calibration curves closely approximating the 45-degree diagonal line). The Kaplan-Meier analysis reveals significant differences in MACCE incidence rates between groups stratified by the median predicted probability (p<0.001). Conclusion Our study developed and validated a practical nomogram for predicting the 2-year risk of MACCE in elderly patients with ACS.