Analysis of risk factors for frailty syndrome in elderly patients with acute coronary syndrome and establishment of a nomogram prediction model.

Objective: To analyze the risk factors of frailty syndrome in elderly patients with acute coronary syndrome (ACS) and establish a nomogram prediction model.

Methods: Totally 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 assigned into a frailty group and a non frailty group based on the presence or absence of frailty syndrome. Clinical data of patients were collected. The logistic method was performed to analyze the influencing factors of elderly ACS patients with frailty syndrome. R software was performed to construct nomogram prediction models. ROC and calibration curves were performed to evaluate the discrimination and consistency of nomogram prediction models. DCA was used to evaluate 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, CCI index, living alone, anxiety, history of falling, 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 index, living alone, anxiety, history of falling, sarcopenia, and NT-proBNP are the influencing factors of frailty syndrome in elderly ACS patients. The predictive model constructed based on these factors has good predictive performance.