Clinical Research
Establishment and validation of a nomogram prediction model for cognitive frailty in community-dwelling elderly patients with type 2 diabetes mellitus
Du Jin, Zhang Di, Chen Yurong, Zhang Weihong
Published 2024-04-20
Cite as Chin J Behav Med & Brain Sci, 2024, 33(4): 310-316. DOI: 10.3760/cma.j.cn371468-20230901-00079
Abstract
ObjectiveTo explore the influencing factors of cognitive frailty in community-dwelling elderly patients with type 2 diabetes mellitus, and to establish and verify a nomogram prediction model.
MethodsA total of 527 elderly patients with type 2 diabetes mellitus in the community of East Hanghai Road, Zhengzhou City were selected by convenience sampling method from August 2021 to May 2022, and they were investigated by general information questionnaire, frailty phenotype, Montreal cognitive assessment scale, clinical dementia rating scale, geriatric depression scale, short-form mini-nutritional assessment and Athens insomnia scale.Multifactorial Logistic regression analysis was performed by SPSS 21.0 software to determine the influencing factors of cognitive frailty in community-dwelling elderly patients with type 2 diabetes mellitus.The nomogram prediction model was constructed based on the screened influencing factors, and the predictive accuracy and discrimination of the model were verified.
ResultsThe results showed that 64 (12.1%) of 527 community-dwelling elderly patients with type 2 diabetes mellitus developed cognitive frailty.Age ≥70 years, living alone(B=1.645, P<0.001, OR=5.182, 95%CI=2.623-10.237), depression(B=1.135, P=0.001, OR=3.112, 95%CI=1.572-6.161), malnutrition(B=0.987, P=0.005, OR=2.683, 95%CI=1.355-5.310), insomnia(B=0.761, P=0.047, OR=2.141, 95%CI=1.012-4.530), and glycosylated hemoglobin (HbA1c) ≥8.5%(B=1.083, P=0.014, OR=2.954, 95%CI=1.242-7.027)were risk factors for cognitive frailty in type 2 diabetes patients, while regular exercise was a protective factor(B=-0.982, P=0.002, OR=0.375, 95%CI=0.203-0.690). Based on the above seven independent influencing factors, the nomogram prediction model established accordingly had good accuracy (H-L test: χ2=5.389, P=0.715) and discrimination (AUC=0.790, 95%CI=0.728-0.852).
ConclusionThe nomogram prediction model which is constructed by integrating the seven influencing factors of cognitive frailty has good predictive value, which can intuitively and easily identify community-dwelling elderly type 2 diabetes mellitus patients with a high risk of cognitive frailty, and provide a reference for early screening and timely intervention.
Key words:
Type 2 diabetes mellitus; Cognitive frailty; Community; Elderly; Nomogram; Prediction model
Contributor Information
Du Jin
The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
Zhang Di
The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
Chen Yurong
Community Health Service Center of East Hanghai Road, Zhengzhou City, Zhengzhou 450009, China
Zhang Weihong
School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China
