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Data‐driven tools are needed to inform individualized treatment decisions for people with type 2 diabetes (T2D). To show how treatment might be individualized, an interactive outline tool was developed to predict treatment outcomes. Individualized predictions were generated for change in HbA1c and body weight after initiation of newer antidiabetes drugs recommended by current guidelines. These predictions were based on data from randomized controlled trials of glucose‐lowering drugs. The data included patient demographics and clinical characteristics (sex, age, body mass index, weight, diabetes duration, HbA1c level, current diabetes treatment and renal function). Predicted outcomes were determined using prespecified statistical models from original trial protocols and estimated coefficients for selected baseline characteristics. This prototype illustrates how evidence‐based individualized treatment might be facilitated in the clinic for people with T2D. Further and ongoing development is required to improve the tool's prognostic value, including the addition of disease co‐morbidities and patient‐orientated outcomes. Patient engagement and data‐sharing by sponsors of clinical trials, as well as real‐world evidence, are needed to provide reliable predicted outcomes to inform shared patient–physician decision‐making. [ABSTRACT FROM AUTHOR]

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 machine‐learning 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. Results: Both the data‐driven model and the simple risk score discriminated between patients at higher and lower hypoglycaemia risk, and performed similarly well based on the time‐dependent area under the curve index (0.63 and 0.66, respectively) over a 2‐year time horizon. Conclusions: Both the data‐driven model and the simple hypoglycaemia risk score were able to discriminate between patients at higher and lower risk of severe hypoglycaemia, the latter doing so using easily accessible clinical data. The implementation of such a tool (http://www.hyporiskscore.com/) may facilitate improved recognition of, and education about, severe hypoglycaemia risk, potentially improving patient care. [ABSTRACT FROM AUTHOR]

Aims: To undertake a post‐hoc analysis, utilizing a hypoglycaemia risk score based on DEVOTE trial data, to investigate if a high risk of severe hypoglycaemia was associated with an increased risk of cardiovascular events, and whether reduced rates of severe hypoglycaemia in patients identified as having the highest risk affected the risk of cardiovascular outcomes. Materials and Methods: The DEVOTE population was divided into quartiles according to patients' individual hypoglycaemia risk scores. For each quartile, the observed incidence and rate of severe hypoglycaemia, major adverse cardiovascular event (MACE) and all‐cause mortality were determined to investigate whether those with the highest risk of hypoglycaemia were also at the greatest risk of MACE and all‐cause mortality. In addition, treatment differences within each risk quartile [insulin degludec (degludec) vs. insulin glargine 100 units/mL (glargine U100)] in terms of severe hypoglycaemia, MACE and all‐cause mortality were investigated. Results: Patients with the highest risk scores had the highest rates of severe hypoglycaemia, MACE and all‐cause mortality. Treatment ratios between degludec and glargine U100 in the highest risk quartile were 95% confidence interval (CI) 0.56 (0.39; 0.80) (severe hypoglycaemia), 95% CI 0.76 (0.58; 0.99) (MACE) and 95% CI 0.77 (0.55; 1.07) (all‐cause mortality). Conclusions: The risk score demonstrated that a high risk of severe hypoglycaemia was associated with a high incidence of MACE and all‐cause mortality and that, in this high‐risk group, those treated with degludec had a lower incidence of MACE. These observations support the hypothesis that hypoglycaemia is a risk factor for cardiovascular events. [ABSTRACT FROM AUTHOR]