Development of a hypoglycaemia risk score to identify high‐risk individuals with advanced type 2 diabetes in DEVOTE

Abstract

Aims: The ability to differentiate patient populations with type 2 diabetes at high risk of severe hypoglycaemia could impact clinical decision making. The aim of this study was to develop a risk score, using patient characteristics, that could differentiate between populations with higher and lower 2-year risk of severe hypoglycaemia among individuals at increased risk of cardiovascular disease. Materials and methods: Two models were developed for the risk score based on data from the DEVOTE cardiovascular outcomes trials. The first, a data-driven machinelearning model, used stepwise regression with bidirectional elimination to identify risk factors for severe hypoglycaemia. The second, a risk score based on known clinical risk factors accessible in clinical practice identified from the data-driven model, included: insulin treatment regimen; diabetes duration; sex; age; and glycated haemoglobin, all at baseline. Both the data-driven model and simple risk score were evaluated for discrimination, calibration and generalizability using data from DEVOTE, and were validated against the external LEADER cardiovascular outcomes trial dataset.

The 6-Month Efficacy of an Intensive Lifestyle Modification Program on Type 2 Diabetes Risk Among Rural Women with Prior Gestational Diabetes Mellitus: a Cluster Randomized Controlled Trial

“…Total scores range from 0 to 88; a cutoff score of 30 or higher represents a greater 10-year risk of type 2 diabetes. Risk score has been widely used in tracking effectiveness of diabetes prevention programs over time, such as in Finland, Germany, and India (Alssema et al, 2011;Heller et al, 2020;Schmiedel et al, 2015). The sensitivity of this questionnaire is 73%, with a positive predictive value of 57% and a negative predictive value of 78%.…”

Prediction Models for Prognosis of Hypoglycemia in Patients with Diabetes: A Systematic Review and Meta-Analysis

“…After an initial screening, the remaining 56 published studies were screened by full text; 41 were excluded after the screening of the full text (reasons outlined in Figure 1). The remaining 15 studies met our eligible criteria (Chandran et al, 2019; Chow et al, 2018; Claydon-Platt et al, 2014; Elbaz et al, 2021; Ena et al, 2018; K. Han et al, 2018; Heller et al, 2020; Hu et al, 2020; Karter et al, 2017; Lagani et al, 2015; Misra-Hebert et al, 2020; Murata et al, 2004; Ruan et al, 2020; Schroeder et al, 2017; Shah et al, 2019). In addition, we found one eligible study by hand-search (Mathioudakis et al, 2021).…”

“…Although some preventive interventions for hypoglycemia (Meulstee et al, 2015;Pratley et al, 2020) can reduce the occurrence of hypoglycemia, there are no validated methods for targeting these interventions efficiently. The ADA reported in 2013 that greater attention must be focused on understanding which patients were at most risk for hypoglycemia (Seaquist et al, 2013).…”

Artificial Intelligence in Bariatric Surgery: Current Status and Future Perspectives

“…According to Aminian et al [35], this random forest model may support and accelerate the process of decision-making toward bariatric surgery. This is desirable as the duration of obesity itself and the presence of its related comorbidities have repeatedly been reported to lead to less postoperative weight loss and higher comorbidity-related mortality [40][41][42]. As weight loss after bariatric surgery is not always associated with health-related quality of life, predicting the increase in quality of life after bariatric surgery is a welcome algorithm in the process of expectation management and shared decision-making, preoperatively [43,44].…”