Machine learning based study of longitudinal HbA1c trends and their association with all-cause mortality: Analyses from a National Diabetes Registry

Intensified glycemic control by HbA1c for patients with coronary heart disease and Type 2 diabetes: a review of findings and conclusions

The occurrence and development of coronary heart disease (CHD) are closely linked to fluctuations in blood glucose levels. While the efficacy of intensified treatment guided by HbA1c levels remains uncertain for individuals with diabetes and CHD, this review summarizes the findings and conclusions regarding HbA1c in the context of CHD. Our review showed a curvilinear correlation between regulated level of HbA1c and therapeutic effectiveness of intensified glycemic control among patients with type 2 diabetes and coronary heart disease. It is necessary to optimize the dynamic monitoring indicators of HbA1c, combine genetic profiles, haptoglobin phenotypes for example and select more suitable hypoglycemic drugs to establish more appropriate glucose-controlling guideline for patients with CHD at different stage of diabetes.

Mortality prediction in patients with hyperglycaemic crisis using explainable machine learning: a prospective, multicentre study based on tertiary hospitals

BACKGROUND

Experiencing a hyperglycaemic crisis is associated with a short- and long-term increased risk of mortality. We aimed to develop an explainable machine learning model for predicting 3-year mortality and providing individualized risk factor assessment of patients with hyperglycaemic crisis after admission.

METHODS

Based on five representative machine learning algorithms, we trained prediction models on data from patients with hyperglycaemic crisis admitted to two tertiary hospitals between 2016 and 2020. The models were internally validated by tenfold cross-validation and externally validated using previously unseen data from two other tertiary hospitals. A SHapley Additive exPlanations algorithm was used to interpret the predictions of the best performing model, and the relative importance of the features in the model was compared with the traditional statistical test results.

RESULTS

A total of 337 patients with hyperglycaemic crisis were enrolled in the study, 3-year mortality was 13.6% (46 patients). 257 patients were used to train the models, and 80 patients were used for model validation. The Light Gradient Boosting Machine model performed best across testing cohorts (area under the ROC curve 0.89 [95% CI 0.77-0.97]). Advanced age, higher blood glucose and blood urea nitrogen were the three most important predictors for increased mortality.

CONCLUSION

The developed explainable model can provide estimates of the mortality and visual contribution of the features to the prediction for an individual patient with hyperglycaemic crisis. Advanced age, metabolic disorders, and impaired renal and cardiac function were important factors that predicted non-survival.

TRIAL REGISTRATION NUMBER

ChiCTR1800015981, 2018/05/04.

6. Glycemic Targets: Standards of Care in Diabetes-2023

The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.

Association of hemoglobin A1c time in range with risk for diabetes complications

INTRODUCTION

We assessed the association between hemoglobin A1c time in range (A1c TIR), based on unique patient-level A1c target ranges, with risks of developing microvascular and macrovascular complications in older adults with diabetes.

RESEARCH DESIGN AND METHODS

We used a retrospective observational study design and identified patients with diabetes from the Department of Veterans Affairs (n=397 634). Patients were 65 years and older and enrolled in Medicare during the period 2004-2016. Patients were assigned to individualized A1c target ranges based on estimated life expectancy and the presence or absence of diabetes complications. We computed A1c TIR for patients with at least four A1c tests during a 3-year baseline period. The association between A1c TIR and time to incident microvascular and macrovascular complications was studied in models that included A1c mean and A1c SD.

RESULTS

We identified 74 016 patients to assess for incident microvascular complications and 89 625 patients to assess for macrovascular complications during an average follow-up of 5.5 years. Cox proportional hazards models showed lower A1c TIR was associated with higher risk of microvascular (A1c TIR 0% to <20%; HR=1.04; 95%) and macrovascular complications (A1c TIR 0% to <20%; HR=1.07; 95%). A1c mean was associated with increased risk of microvascular and macrovascular complications but A1c SD was not. The association of A1c TIR with incidence and progression of individual diabetes complications within the microvascular and macrovascular composites showed similar trends.

CONCLUSIONS

Maintaining stability of A1c levels in unique target ranges was associated with lower likelihood of developing microvascular and macrovascular complications in older adults with diabetes.

“Big Data” Approaches for Prevention of the Metabolic Syndrome

Metabolic syndrome (MetS) is characterized by the concurrence of multiple metabolic disorders resulting in the increased risk of a variety of diseases related to disrupted metabolism homeostasis. The prevalence of MetS has reached a pandemic level worldwide. In recent years, extensive amount of data have been generated throughout the research targeted or related to the condition with techniques including high-throughput screening and artificial intelligence, and with these “big data”, the prevention of MetS could be pushed to an earlier stage with different data source, data mining tools and analytic tools at different levels. In this review we briefly summarize the recent advances in the study of “big data” applications in the three-level disease prevention for MetS, and illustrate how these technologies could contribute tobetter preventive strategies.