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Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan–Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention. [ABSTRACT FROM AUTHOR]

Gestational diabetes mellitus (GDM) increases the risk of early-onset type 2 diabetes, which further exacerbates the risk of developing diabetic complications such as kidney, circulatory, and neurological complications. Yet, existing models have solely focused on the prediction of type 2 diabetes, and not of its complications, which are arguably the most clinically relevant outcomes. Our aim was to develop a prediction model for type 2 diabetic complications in patients with GDM. Using provincial administrative data from Quebec, Canada, we developed a model to predict type 2 diabetic complications within 10 years among 90,143 women with GDM. The model was internally validated and assessed for discrimination, calibration, and risk stratification accuracy. The incidence of diabetic complications was 3.8 (95% confidence interval (CI) 3.4–4.3) per 10,000 person-years. The final prediction model included maternal age, socioeconomic deprivation, substance use disorder, gestational age at delivery, severe maternal morbidity, previous pregnancy complications, and hypertensive disorders of pregnancy. The model had good discrimination [area under the curve (AUROC) 0.72 (95% CI 0.69–0.74)] and calibration (slope ≥ 0.9) to predict diabetic complications. In the highest category of the risk stratification table, the positive likelihood ratio was 8.68 (95% CI 4.14–18.23), thereby showing a moderate ability to identify women at highest risk of developing type 2 diabetic complications. Our model predicts the risk of type 2 diabetic complications with moderate accuracy and, once externally validated, may prove to be a useful tool in the management of women after GDM. [ABSTRACT FROM AUTHOR]