134-OR: Cost-Effectiveness of a Real-Time Continuous Glucose Monitoring System vs. Self-Monitoring of Blood Glucose in Type 2 Diabetes Patients on Insulin in Canada
Bibliographic record
Abstract
A health economic analysis was conducted to determine the cost-effectiveness of a real-time continuous glucose monitoring (rt-CGM) system versus self-monitoring of blood glucose (SMBG) in Type 2 diabetes (T2D) patients treated with insulin. The IQVIA CORE diabetes model was utilized for the analysis. Clinical data were sourced from a US retrospective cohort study of adult T2D patients on insulin and adapted to Canada. The baseline mean age (SD) of the cohort was 61 years (13.2) and proportion of male 49%. Mean baseline HbA1c for the cohort was 8.3% (67 mmol/mol) . Patients using rt-CGM were assumed to have a reduction in HbA1c of -0.56% based on the mean difference between groups after 12-months follow-up. A quality-of-life (QoL) benefit associated with reduced finger-stick testing was applied. The analysis was conducted from the Canadian Agency for Drugs and Technologies in Health perspective over a lifetime horizon. The rt-CGM system was associated with an incremental gain of 0.97 quality-adjusted life years (QALYs) compared with SMBG (mean [SD] 10.35 [3.08] versus 9.375 [2.85] QALYs) . Total mean [SD] lifetime costs were CAN$ 17,223 higher with rt-CGM (CAN$ 206,284 [121,493] versus 189,061 [129.680]) , resulting in an incremental cost-effectiveness ratio of CAN$ 17,652 per QALY gained. Sensitivity analyses demonstrated findings were sensitive to changes in QoL, HbA1c, younger patient cohorts, and rt-CGM cost. Varying rt-CGM cost to -25% of base case yielded an ICER of CAN$ 3,512, and varying rt-CGM cost to -50% of base case the ICER becomes dominant over SMBG. For T2D patients on insulin, rt-CGM was associated with significant clinical outcomes and is a cost-effective management option relative to SMBG based on a willingness-to-pay threshold of CAN$ 50,000 per QALY gained. Disclosure J.J.Isitt: Consultant; Dexcom, Inc. S.Roze: None. G.Cogswell: None. P.M.Lynch: Employee; Dexcom, Inc., Stock/Shareholder; Dexcom, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".