Development and Validation of a Risk Prediction Model for Cognitive Frailty in Elderly Patients With Type 2 Diabetes Mellitus.

Aims: This study aimed to develop and validate a risk prediction model for cognitive frailty in elderly patients with Type 2 diabetes mellitus (T2DM).

Design: A cross-sectional design.

Methods: From February to November 2023, a convenience sample of 430 older adults with T2DM was enrolled at a tertiary hospital in Jinzhou. The study analysed 22 indicators, including sociodemographic characteristics, behavioural factors, information related to T2DM, nutritional status, instrumental activities of daily living (IADL) and depression. Independent risk factors related to cognitive frailty were identified using LASSO and multivariate logistic regression analysis. A prediction model was created using a nomogram. The calibration curve, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve were used to evaluate model performance. This study was reported using the STARD checklist (Data S1).

Results: The study found that cognitive frailty was prevalent in 30.7% of elderly patients with T2DM. Age, physical activity, glycosylated haemoglobin (HBA1c), duration of diabetes, nutritional status, IADL and depression were predictors of cognitive frailty. The ROC curve shows that the nomogram has good discriminative power. The calibration plots demonstrated a good fit between the observed and ideal curves. Additionally, DCA highlighted the clinical application of the nomogram.

Conclusions: This study provided an effective and convenient approach to evaluating the risk of cognitive frailty among elderly T2DM patients, which can help in the clinical screening of high-risk individuals.

Impact: Nurses should emphasise the care of comorbid cognitive frailty in elderly patients with T2DM. The intuitive and noninvasive nomogram can help clinical nurses assess the risk probability of cognitive frailty in this population. Tailored prevention strategies for high-risk populations can be rapidly developed with this tool, significantly improving patients' quality of life.

Patient Or Public Contribution: Some patients were involved in data interpretation. No public contribution.