[Development of a Diabetic Foot Ulceration Prediction Model and Nomogram]

Prognostic factors in diabetes: Comparison of Chi-square automatic interaction detector (CHAID) decision tree technology and logistic regression

This study aimed to develop a diabetes prediction model. The model performance was compared with logistic regression, and the decision tree Chi-square automatic interaction detection (CHAID) was used to predict diabetes. In total, 3233 patients were included in the analysis. Of these, 589 patients with diabetes and 2644 patients without diabetes were included after analyzing the study sample from the Korean Genome and Epidemiology Study (KoGES)-8 data. In this study, Diabetes Mellitus (DM) diagnosis prediction was compared with logistic regression and prediction through machine learning (ML) using the CHAID decision classification tree. We performed statistical analysis using the CHAID method with International Business Machine (IBM) statistical program SPSS®. We performed logistic regression analysis to predict the classification of diabetes accurately, and the total classification accuracy of the analysis was 81.7%, and the CHAID decision tree classification accuracy was 81.8%. A diabetes diagnosis decision tree was created, which included seven terminal nodes and three depth levels. This analysis showed that a blood pressure problem and hospital visits were the most decisive variables at the time of classification, and two risk levels were created for diabetes diagnosis. The suggested method is a valuable tool for predicting diabetes. Patients who visit the hospital because of blood pressure problems are more likely to develop diabetes than under-treating hyperlipidemia. The diabetes prediction model can help doctors make decisions by detecting the possibility of diabetes early; however, it is impossible to diagnose diabetes using only the model without the doctor's opinion.

Establishment of a Nomogram Model for Predicting Cardiovascular and Cerebrovascular Events in Diabetic Nephropathy Patients Receiving Maintenance Hemodialysis

Objective. The objective of this study is to explore the risk factors of cardiovascular and cerebrovascular events (CCE) in patients with diabetic nephropathy (DN) receiving maintenance hemodialysis, and to establish a nomogram model on this basis. Method. 144 patients with DN receiving maintenance hemodialysis from February 2020 to February 2021 were selected and followed up for 12 months. They were divided into the occurrence and nonoccurrence groups according to whether CCE occurred. The multivariate logistic regression analysis was used to analyze the influencing factors of CCE, and a predictive nomogram model was established. The receiver operating characteristic (ROC) curve was drawn to evaluate the predictive effect of the nomogram model. The Hosmer-Lemeshow method was used to test the calibration degree. Results. Among the patients, 63 patients (43.75%) encountered CCE. Multivariate logistic regression analysis showed that age >60 years old, history of CCE, dialysis age >12 months, systolic blood pressure >140 mmHg, blood phosphorus level >1.5 mmol/L, triglyceride (TG) level >2.30 mmol/l, adiponectin (ADPN) level <5 mg/L, high-sensitivity-C-reactive protein (hs-CRP) level >10 mg/L, hemoglobin (Hb) level <120 g/L, serum creatinine (SCr) level >720 μmol/L, and albumin (ALB) level <40 g/L were independent risk factors for CCE. Based on the above independent risk factors, a nomogram model of CCE was created. ROC curve analysis showed that the area under curve for predicting CCE was 0.881 (95% CI: 0.833~0.919), indicating that the nomogram model had great predictive effect. The Hosmer-Lemeshow method showed that the calibration curve was in good agreement with the standard curve. Conclusion. Age, history of CCE, dialysis age, systolic blood pressure and serum phosphorus, and TG, ADPN, hs-CRP, Hb, SCr, and ALB levels are all influencing factors for the occurrence of CCE in patients with DN receiving maintenance hemodialysis, and the nomogram model has a great predictive effect on CCE.