Study on the construction of risk prediction model and efficacy validation of cognitive decline in elderly patients with type 2 diabetes

This study aims to construct a predictive model for assessing the risk of cognitive decline among elderly patients diagnosed with type 2 diabetes and validate its effectiveness. A total of 480 elderly patients with type 2 diabetes who signed a family doctor and underwent chronic disease management were selected and divided into a cognitive decline group (n = 62) and a non-cognitive decline group (n = 418) based on cognitive decline. Various clinical variables, lifestyle choices, medication regimens, complications, and medical histories were subjected to statistical analysis for both groups. Univariate and multivariate logistic regression analyses were performed to identify risk factors, based on which a risk prediction model was constructed. Hosmer and Lemeshow were used to investigate the goodness of fit of the risk prediction model, and Statistical Product and Service Solutions (SPSS) was used to draw Receiver Operating Characteristic (ROC) curves to assess the predictive value of the risk prediction model. Age, infrequent exercise, inadequate sleep, and the presence of depression emerged as significant risk factors for cognitive decline. The goodness-of-fit test using the Hosmer and Lemeshow statistic yielded χ2 = 0.041 and p = 0.855, confirming the model’s appropriateness. ROC curve analysis demonstrated an Area Under the Curve (AUC) of 0.912 (95% CI: 0.874 to 0.950), underscoring the model’s predictive capability. Age, infrequent exercise, inadequate sleep and depression could be risk factors for cognitive decline in elderly individuals with type 2 diabetes, and the proposed risk prediction model displays robust predictive accuracy for identifying those at risk of cognitive decline.

Elderly type 2 diabetes; Cognitive decline; Risk prediction; Model construction; Efficacy validation

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