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

Autor: Heller S; Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK., Lingvay I; Department of Internal Medicine and Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA., Marso SP; HCA Midwest Health Heart and Vascular Institute, Overland Park, Kansas, USA., Philis-Tsimikas A; Scripps Whittier Diabetes Institute, San Diego, California, USA., Pieber TR; Department of Internal Medicine, Medical University of Graz, Graz, Austria., Poulter NR; Imperial Clinical Trials Unit, Imperial College London, London, UK., Pratley RE; AdventHealth Translational Research Institute, Orlando, Florida, USA., Hachmann-Nielsen E; Novo Nordisk A/S, Søborg, Denmark., Kvist K; Novo Nordisk A/S, Søborg, Denmark., Lange M; Novo Nordisk A/S, Søborg, Denmark., Moses AC; Novo Nordisk A/S, Søborg, Denmark.; Independent Consultant, Portsmouth, New Hampshire, USA., Trock Andresen M; Novo Nordisk A/S, Søborg, Denmark., Buse JB; University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
Jazyk: angličtina
Zdroj: Diabetes, obesity & metabolism [Diabetes Obes Metab] 2020 Dec; Vol. 22 (12), pp. 2248-2256.
DOI: 10.1111/dom.14208
Abstrakt: 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.
(© 2020 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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