Establishment and health management application of a prediction model for high-risk complication combination of type 2 diabetes mellitus based on data mining

Abstract

In recent years, the prevalence of T2DM has been increasing annually, in particular, the personal and socioeconomic burden caused by multiple complications has become increasingly serious. This study aimed to screen out the high-risk complication combination of T2DM through various data mining methods, establish and evaluate a risk prediction model of the complication combination in patients with T2DM. Questionnaire surveys, physical examinations, and biochemical tests were conducted on 4,937 patients with T2DM, and 810 cases of sample data with complications were retained. The high-risk complication combination was screened by association rules based on the Apriori algorithm. Risk factors were screened using the LASSO regression model, random forest model, and support vector machine. A risk prediction model was established using logistic regression analysis, and a dynamic nomogram was constructed. Receiver operating characteristic (ROC) curves, harrell’s concordance index (C-Index), calibration curves, decision curve analysis (DCA), and internal validation were used to evaluate the differentiation, calibration, and clinical applicability of the models. This study found that patients with T2DM had a high-risk combination of lower extremity vasculopathy, diabetic foot, and diabetic retinopathy. Based on this, body mass index, diastolic blood pressure, total cholesterol, triglyceride, 2-hour postprandial blood glucose and blood urea nitrogen levels were screened and used for the modeling analysis. The area under the ROC curves of the internal and external validations were 0.768 (95% CI, 0.744−0.792) and 0.745 (95% CI, 0.669−0.820), respectively, and the C-index and AUC value were consistent. The calibration plots showed good calibration, and the risk threshold for DCA was 30–54%. In this study, we developed and evaluated a predictive model for the development of a high-risk complication combination while uncovering the pattern of complications in patients with T2DM. This model has a practical guiding effect on the health management of patients with T2DM in community settings.

Epidemiological Situation of High-Prevalence Non-Communicable Diseases in Spain: A Systematic Review

“…There are another two studies in our review that reveal that diabetic patients, in addition to suffering from the disease, also experience certain comorbidities and complications such as hypertension, hyperlipidaemia, obesity, chronic kidney disease, chronic renal failure, cardiovascular disease, diabetic retinopathy, diabetic neuropathy, heart failure and peripheral vascular disease [57,64]. This coincides with the analyses of Dimore et al (2023) [87] and Luo et al (2023) [88], which highlight complications in patients with DM2 including retinopathy, foot ulcers, nephropathy and neuropathy. Similarly, Berhe et al (2023) [89] identified within their analysis microvascular complications such as eye disease, renal disease and peripheral neuropathy, as well as macrovascular complications, with hypertension being the most common and cerebrovascular disease the least common.…”