Analysis for Risk Factors for Frailty Syndrome in Elderly Patients with Acute Coronary Syndrome and Establishment of a Nomogram Prediction Model.

OBJECTIVE

The objective was to analyze the risk factors for frailty syndrome in elderly patients with acute coronary syndrome (ACS) and establish a nomogram prediction model.

METHODS

A total of 256 elderly ACS patients admitted to our hospital from September 2022 to March 2025 were retrospectively selected and randomly assigned into a modeling group and a validation group in a 7:3 ratio. The modeling group was further divided into a frailty group and a non-frailty group based on the presence or absence of frailty syndrome. Clinical data were collected, and logistic regression analysis was performed to identify influencing factors for frailty syndrome in elderly ACS patients. R software was performed to construct nomogram prediction models. The ROC curve and calibration curve were used to evaluate the discrimination and calibration of the model. Decision curve analysis (DCA) was employed to assess its clinical application value.

RESULTS

Out of 179 patients, 70 developed frailty syndrome, with an incidence rate of 39.11%. The logistic analysis results showed that age, Charlson Comorbidity Index (CCI), living alone, anxiety, history of falls, sarcopenia, and NT-proBNP were risk factors for frailty syndrome in elderly ACS patients (p < 0.05). The AUC of the modeling group was 0.877, and the H-L test showed χ2 = 8.567 (p = 0.785). The AUC of the validation group was 0.890, and the H-L test showed χ2 = 7.231 (p = 0.705). DCA curve showed that when the threshold probability was between 0.06 and 0.95, the nomogram prediction model for evaluating elderly ACS with frailty syndrome had high clinical application value.

CONCLUSION

Age, CCI, living alone, anxiety, history of falls, sarcopenia, and NT-proBNP are the influencing factors of frailty syndrome in elderly ACS patients. The predictive model constructed based on these factors demonstrates good predictive performance.