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INTRODUCTIONThis study aimed to explore the level of knowledge of Muslim patients with diabetes mellitus (DM) regarding DM and the self-management of DM when fasting during Ramadan. METHODSMuslim patients with DM attending the Diabetes Centre of Singapore General Hospital, Singapore, were surveyed on their knowledge of DM and self-management of DM when fasting during Ramadan. Data on patient demographics, DM history and management of DM during the previous Ramadan was also collected. RESULTS A total of 92 patients (34 male, 58 female) were surveyed. The mean age of the patients was 53.4 ± 13.3 years.The patients were either Malay (91.3%) or Indian (8.7%), and most (66.3%) had at least a secondary school education. Most (89.1%) had Type 2 DM. The mean duration of DM was 8.7 ± 5.1 years and mean pre-Ramadan haemoglobin A1c was 8.4% ± 1.8%. DM treatment consisted of insulin therapy (37.0%), oral glucose-lowering drugs (35.9%) and combined therapy (22.8%). The mean DM knowledge score was 58.8% for general knowledge and 75.9% for fasting knowledge. During the previous Ramadan, although 71.4% of the patients consulted their physicians, 37.3% did not monitor their blood glucose levels and 47.0% had hypoglycaemic episodes. Among those who had hypoglycaemia, 10.8% continued to fast.CONCLUSION Unsafe self-management practices were observed among DM patients who fasted during Ramadan. Efforts should be made to bridge the gap between knowledge of DM and self-management of DM.

OBJECTIVE With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns. RESEARCH DESIGN AND METHODS Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient’s age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models. RESULTS Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82–13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75–30.63), stroke (HR 19.37, 95% CI 16.92–22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90–15.36), and mortality (HR 3.47, 95% CI 3.17–3.80). CONCLUSIONS T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Muslims with diabetes were able to self-manage when fasting using tele-monitoring support and intervention, with decreased complications during Ramadan compared with pre-Ramadan.

Background: With the rising prevalence of diabetes, machine learning (ML) models have been increasingly used for prediction of diabetes and its complications, due to their ability to handle large complex data sets. This study aims to evaluate the quality and performance of ML models developed to predict microvascular and macrovascular diabetes complications in an adult Type 2 diabetes population. Methods: A systematic review was conducted in MEDLINE®, Embase®, the Cochrane® Library, Web of Science®, and DBLP Computer Science Bibliography databases according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist. Studies that developed or validated ML prediction models for microvascular or macrovascular complications in people with Type 2 diabetes were included. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC). An AUC >0.75 indicates clearly useful discrimination performance, while a positive mean relative AUC difference indicates better comparative model performance. Results: Of 13 606 articles screened, 32 studies comprising 87 ML models were included. Neural networks (n = 15) were the most frequently utilized. Age, duration of diabetes, and body mass index were common predictors in ML models. Across predicted outcomes, 36% of the models demonstrated clearly useful discrimination. Most ML models reported positive mean relative AUC compared with non-ML methods, with random forest showing the best overall performance for microvascular and macrovascular outcomes. Majority (n = 31) of studies had high risk of bias. Conclusions: Random forest was found to have the overall best prediction performance. Current ML prediction models remain largely exploratory, and external validation studies are required before their clinical implementation. Protocol Registration: Open Science Framework (registration number: 10.17605/OSF.IO/UP49X).