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A nomogram model for identifying cognitive frailty in elderly patients with chronic diseases: a multicenter
Shouqiang Huang1, Huan Liu2, Qingwei Liu3
1Department of Ophthalmology, Wuhu Second People's Hospital, Wuhu, Anhui, China.
Objectives:
In the context of population aging, the prevalence of cognitive frailty among elderly individuals with chronic diseases is steadily increasing. Cognitive frailty is a prevalent geriatric condition that significantly impacts the health of elderly patients with chronic diseases. This study aimed to explore the influencing factors of cognitive frailty in elderly patients with chronic diseases and to development and Internal Validation of a Nomogram for Screening Prevalent Cognitive Frailty.
Methods:
From March 2025 to August 2025, a total of 752 elderly patients with chronic diseases (age ≥ 60 years) were selected from the First Affiliated Hospital of Wannan Medical College, the Second People's Hospital of Wuhu, and the First Affiliated Hospital of Guangxi Medical University. The assessment utilized the Overall Cognition Ascertain Dementia-8, FRAIL Scale, Clinical Physiological Resilience, Oral Frailty Scale, SARC-F Questionnaire, and PHQ-2 Depression Scale. The logistic regression model was applied to explore the associated factors of cognitive frailty in elderly patients with chronic diseases. The model's performance was evaluated using various measures, including the area under the receiver operating characteristic curve, the calibration curve, the Hosmer-Lemeshow test, and decision curve analysis.
Results:
A total of 752 elderly patients with chronic diseases were included, among whom 171 (22.7%) had cognitive frailty. Gender, age, oral frailty, sarcopenia, depressive status, and clinical physiological resilience were identified as influencing factors of cognitive frailty in elderly patients with chronic diseases (p < 0.05). In the training set, the AUC was 0.856 (95% CI: 0.823-0.890), while in the validation set, the AUC was 0.886 (95% CI: 0.840-0.932). Additionally, the test results showed that the model exhibited good fit in both the training set (χ 2 = 10.154, p = 0.2054) and the validation set (χ 2 = 5.156, p = 0.741). The Brier scores for the training set and the validation set were 0.128 and 0.104, respectively.
Conclusion:
Elderly patients with chronic diseases have a higher incidence of cognitive frailty. The nomogram model in this multicenter cross-sectional study may help identify hospitalized older patients with chronic diseases who are likely to have cognitive frailty and may require further assessment.
