Reporting of hypoglycaemia in clinical trials of basal insulins: A need for consensus

Abstract

Hypoglycaemia is a common side‐effect of diabetes therapies, particularly insulin, and imposes a substantial burden on individuals and healthcare systems. Consequently, regulatory approval of newer basal insulin (BI) therapies has relied on demonstration of a balance between achievement of good glycaemic control and less hypoglycaemia. Randomized controlled trials (RCTs) are the gold standard for assessing efficacy and safety, including hypoglycaemia risk, of BIs and are invaluable for obtaining regulatory approval. However, their highly selected patient populations and their conditions lead to results that may not be representative of real‐life situations. Real‐world evidence (RWE) studies are more representative of clinical practice, but they also have limitations. As such, data both from RCTs and RWE studies provide a fuller picture of the hypoglycaemia risk with BI therapies. However, substantial differences exist in the way hypoglycaemia is reported across these studies, which confounds comparisons of hypoglycaemia frequency among different BIs. This problem is ongoing and persists in recent trials of second‐generation BI analogues. Although they provide a lower risk of hypoglycaemia when compared with earlier BIs, they do not eliminate it. This review describes differences in the way hypoglycaemia is reported across RCTs and RWE studies of second‐generation BI analogues and examines potential reasons for these differences. For studies of BIs, there is a need to standardize aspects of design, analysis and methods of reporting to better enable interpretation of the efficacy and safety of such insulins among studies; such aspects include length of follow‐up, glycaemic targets, hypoglycaemia definitions and time intervals for determining nocturnal events.

Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study)

“…The longitudinal, prospective nature of our study contrasts the typically short, retrospective follow-ups of other prediction models (mode duration 24 hours-3 months) [ 12 , 93 - 96 ]. Buttressed by a sufficiently large sample size and completion rate >70%, iNPHORM will facilitate assessments of time-varying predictors, lagged dependent variables, and low-salience events (eg, nonsevere hypoglycemia) with minimal false negatives, extrapolation bias, and statistical power loss [ 97 ].…”

iGlarLixi versus basal plus Rapid‐Acting insulin in adults with type 2 diabetes advancing from basal insulin therapy: The SoliSimplify Real‐World study

“…In the present analysis, few hypoglycaemia events were recorded (iGlarLixi: 2.7%; BI + RAI: 2.5%), and the severity of these events is unknown. Because of factors such as impaired awareness, recall bias, incorrect coding, or reticence of participants to report episodes to their physicians, hypoglycaemia tends to be underreported in retrospective RWE studies 29,30 . Hypoglycaemic events recorded in EMRs are also more likely to be severe, that is, events requiring external assistance for recovery 31 .…”

Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study) (Preprint)

“…The longitudinal, prospective nature of our study contrasts the typically short, retrospective follow-ups of other prediction models (mode duration 24 hours-3 months) [12,[93][94][95][96]. Buttressed by a sufficiently large sample size and completion rate >70%, iNPHORM will facilitate assessments of time-varying predictors, lagged dependent variables, and low-salience events (eg, nonsevere hypoglycemia) with minimal false negatives, extrapolation bias, and statistical power loss [97].…”