Shan Wang,1 Ying Sun,1 Wen Tang,1 Shangxin Lu,1 Feng Feng,1 Xiaopei Hou,1 Lihong Ma,2 Runzhi Li,3 Jieqiong Hu,1 Bing Liu,1 Yunli Xing1 

1Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou, Henan, People’s Republic of China

Correspondence: Yunli Xing, Department of Geriatrics, Beijing Friendship Hospital, Capital Medical University, No. 95, Yongan Road, Xicheng District, Beijing, 100050, People’s Republic of China, Tel +86 13121181469, Fax +86 10 6313 8040, Email xingyunli1976@126.com

Background: Cognitive frailty (CF) is strongly associated with major adverse cardiovascular events, yet its assessment requires specialized equipment, limiting clinical practicality. This study aimed to develop and validate a nomogram model for predicting CF in patients with acute coronary syndrome (ACS) to enhance early identification and intervention.
Methods: Patients with ACS (N=547) were enrolled and randomly split into a training set (70%) and a testing set (30%). The training set was used to construct the nomogram, while the testing set was used for validation. Model performance was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) to assess discrimination, accuracy, and clinical utility, respectively.
Results: The nomogram included six predictors: education level, age, systolic blood pressure (SBP), Charlson Comorbidity Index (CCI), Short Physical Performance Battery (SPPB), and nutritional status. The model demonstrated strong discriminatory power, with an area under the ROC curve of 0.854 (95% CI: 0.741– 0.861) in the training cohort and 0.733 (95% CI: 0.500– 0.898) in the testing cohort. Calibration analysis confirmed high accuracy, and DCA indicated significant net benefits across both cohorts, supporting its clinical applicability.
Conclusion: The nomogram effectively predicts CF in ACS patients by considering education, age, SBP, CCI, SPPB, and nutritional status, serving as a visual aid for healthcare providers to facilitate the early identification and intervention of CF. Future research is needed to validate the nomogram’s efficacy in diverse populations and explore standardized assessment methods that enhance its clinical applicability in mitigating CF in ACS patients.

Keywords: acute coronary syndrome, cognitive frailty, nomogram, predictive model, risk factors