142-LB: Cost-Effectiveness of Real-Time Continuous Glucose Monitoring (rt-CGM) vs. Intermittent-Scanning Continuous Glucose Monitoring (is-CGM) in Patients with Type 2 Diabetes on Multiple Daily Injections of Insulin (T2D MDI) in Canada
Bibliographic record
Abstract
Rt-CGM systems provide real-time glucose values, hypo- and hyperglycemia alerts, and predictive “urgent low soon” alerts that have been shown to improve glycemic outcomes and reduce severe hypoglycemic events (SHEs) in patients with T2D, without the need for periodic scanning. The economic value of rt-CGM vs is-CGM is unknown for patients with T2D MDI in Canada. We conducted a cost-effectiveness analysis comparing the two systems for this patient population using the INESSS Canadian payer perspective. A 30-year analysis was performed using the validated ECHO-T2DM. Patient characteristics were sourced from Karter (2021). Efficacy and safety were derived from the DIAMOND T2D and REPLACE RCTs. The HbA1c difference of -0.33 (favoring rt-CGM) was derived using Bucher's adjusted indirect comparison, SHE rates were 0.014 and 0 Per Person-Year (PPY) for is-CGM and rt-CGM, respectively, and severe hyperglycemia/DKA rates were proxied by the difference in time above range (0.0313 and 0 PPY, respectively). Costs and disutility weights were sourced from published Canadian literature and discounted at 1.5% annually. A reduction in fear of hypoglycemia (FOH) utility was sourced from the ALERTT1 RCT. Sensitivity analyses were performed. Rt-CGM was associated with quality-adjusted life year gains of 0.346 and incremental costs of CAD 5,737, with an incremental cost-effectiveness ratio (ICER) of CAD 16,598. No DKA events for is-CGM and a 50% reduction in FOH utility increased the ICERs to CAD 30,987 and CAD 29,116, respectively. ICER was CAD 2,278 when both CGM costs were reduced by 50% and rt-CGM became cost-saving (dominant) at a 65% reduction. These results suggest rt-CGM is cost-effective vs is-CGM in patients with T2D MDI in Canada. These findings can inform government and commercial healthcare decision-makers when considering reimbursement of CGM systems. Disclosure H. Alshannaq: Employee; Dexcom, Inc., Other Relationship; Vertex Pharmaceuticals Incorporated. G. J. Norman: Employee; Dexcom, Inc. M. Willis: None. A. Nilsson: None. Funding 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".