What drives glycemic control in a person living with diabetes?

Diabetes reversal: Update on current knowledge and proposal of prediction score parameters for diabetes remission

BACKGROUND AND AIM

The field of diabetes reversal is continuously evolving. Strategies for implementing diabetes care towards diabetes reversal are still being worked out. We aim to analyse data from available literature to ascertain factors allowing patient centric dietary approach to achieve diabetes reversal in clinical practice.

METHODS

In this exploratory review, an update on current knowledge is presented to delineate factors driving diabetes remission in an individual based on major studies in the field. This knowledge is then applied to subtypes of type 2 diabetes to optimise dietary approach for reversal of diabetes.

RESULTS AND CONCLUSION

Shorter duration of diabetes, lesser number of medicines needed to achieve euglycemia and 15 kg weight loss are common factors favouring diabetes remission in all major studies. A patient centric approach to diabetes reversal taking into account the recently described diabetes subtypes is being proposed to improve the proportion of patients achieving remission. We also propose the parameters of a novel diabetes remission prediction score, based on patient motivation, interaction with the care-team, level of diabetes self-care and the intent of the care-team.

Developing Clinical Decision Support System using Machine Learning Methods for Type 2 Diabetes Drug Management

BACKGROUND AND OBJECTIVES

Application of artificial intelligence/machine learning (AI/ML) for automation of diabetes management can enhance equitable access to care and ensure delivery of minimum standards of care. Objective of the current study was to create a clinical decision support system using machine learning approach for diabetes drug management in people living with Type 2 diabetes.

METHODOLOGY

Study was conducted at an Endocrinology clinic and data collected from the electronic clinic management system. 15485 diabetes prescriptions of 4974 patients were accessed. A data subset of 1671 diabetes prescriptions of 940 patients with information on diabetes drugs, demographics (age, gender, body mass index), biochemical parameters (HbA1c, fasting blood glucose, creatinine) and patient clinical parameters (diabetes duration, compliance to diet/exercise/medications, hypoglycemia, contraindication to any drug, summary of patient self monitoring of blood glucose data, diabetes complications) was used in analysis. An input of patient variables were used to predict all diabetes drug classes to be prescribed. Random forest algorithms were used to create decision trees for all diabetes drugs.

RESULTS AND CONCLUSION

Accuracy for predicting use of each individual drug class varied from 85% to 99.4%. Multi-drug accuracy, indicating that all drug predictions in a prescription are correct, stands at 72%. Multi drug class accuracy in clinical application may be higher than this result, as in a lot of clinical scenarios, two or more diabetes drugs may be used interchangeably. This report presents a first positive step in developing a robust clinical decision support system to transform access and quality of diabetes care.