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BMJ Publishing Group, BMJ Open Diabetes Research and Care, 1(6), p. e000527, 2018

DOI: 10.1136/bmjdrc-2018-000527

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Development of a model to predict 5-year risk of severe hypoglycemia in patients with type 2 diabetes

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

ObjectiveWe constructed a predictive model of long-term risk for severe hypoglycemia (SH: hypoglycemia requiring assistance) in patients with type 2 diabetes (T2DM).Research design and methodsData from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study (original n=10 251, n=5135 used in the current analysis), a randomized, multicenter, double 2×2 factorial design study examining the effect of glycemic, blood pressure, and lipid control on cardiovascular outcomes in patients with diagnosed T2DM, were used. Over the follow-up (3.76±1.12 years), the ACCORD participants experienced 607 incident SH events. Cox regression was used to identify the SH risk prediction model.ResultsWe identified 17 predictors—glycemic management, age, race, education, waist circumference, medications (insulin, antihypertensive, HMG-CoA reductase inhibitors, sulfonylurea, biguanide and meglitinide), years since diabetes diagnosis, history of hypoglycemia in the last week, systolic blood pressure, diastolic blood pressure, serum creatinine, and urinary albumin creatinine ratio—to construct a prediction model for SH (c-statistic=0.782). Using this information, we derived point scores to estimate the 5-year risk for SH in individual patients with T2DM. After adjusting for other variables in the model, the three strongest predictors for SH over 5 years were intensive glycemic management (HR=2.37, 95% CI 1.99 to 2.83), insulin use (HR=2.14, 95% CI 1.77 to 2.59), and antihypertensive medication use (HR=1.90, 95% CI 1.26 to 2.86).ConclusionUsing the ACCORD data, we identified attributes to predict 5-year risk of SH in patients with T2DM, which warrant evaluation in broader populations to determine applicability.