Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes

Purpose: The objective of this study was to evaluate the efficacy of machine learning algorithms in predicting risks of complications and poor glycemic control in nonadherent type 2 diabetes (T2D).Materials and Methods: This study was a real-world study of the complications and blood glucose prognosis of nonadherent T2D patients. Data of inpatients in Sichuan Provincial People’s Hospital from January 2010 to December 2015 were collected. The T2D patients who had neither been monitored for glycosylated hemoglobin A nor had changed their hyperglycemia treatment regimens within the last 12 months were the object of this study. Seven types of machine learning algorithms were used to develop 18 prediction models. The predictive performance was mainly assessed using the area under the curve of the testing set.Results: Of 800 T2D patients, 165 (20.6%) met the inclusion criteria, of which 129 (78.2%) had poor glycemic control (defined as glycosylated hemoglobin A ≥7%). The highest area under the curves of the testing set for diabetic nephropathy, diabetic peripheral neuropathy, diabetic angiopathy, diabetic eye disease, and glycosylated hemoglobin A were 0.902 ± 0.040, 0.859 ± 0.050, 0.889 ± 0.059, 0.832 ± 0.086, and 0.825 ± 0.092, respectively.Conclusion: Both univariate analysis and machine learning methods reached the same conclusion. The duration of T2D and the duration of unadjusted hypoglycemic treatment were the key risk factors of diabetic complications, and the number of hypoglycemic drugs was the key risk factor of glycemic control of nonadherent T2D. This was the first study to use machine learning algorithms to explore the potential adverse outcomes of nonadherent T2D. The performances of the final prediction models we developed were acceptable; our prediction performances outperformed most other previous studies in most evaluation measures. Those models have potential clinical applicability in improving T2D care.

Factors associated with glycemic control in people with diabetes at the Family Health Strategy in Pernambuco

OBJECTIVE Identifying factors associated with glycemic control in people with type 2 Diabetes Mellitus (DM) registered in the Family Health Strategy (FHS) in Pernambuco, Brazil. METHOD Associations between glycemic control (glycosylated hemoglobin A lower or equal to 7%) presented by people with DM and variables related to sociodemographic conditions, lifestyle, characteristics of diabetes, treatment and follow-up of patients by health services were investigated by multiple regression. RESULTS More than 65% of the participants presented inadequate glycemic control, especially those with lower age, longer illness duration, more annual contacts with FHS and complex therapeutic regimen. People with DM without referrals to specialists presented greater glycemic control. Associations with education level and obesity did not remain significant in the multivariate model. CONCLUSION The evolution of diabetes hinders adequate control, however, attention to younger people with DM and referrals to specialists are factors that can improve glycemic control.