Risk of amputation with canagliflozin across categories of age and cardiovascular risk in three US nationwide databases: cohort study
Bibliographic record
Abstract
OBJECTIVE: To estimate the rate of lower limb amputation among adults newly prescribed canagliflozin according to age and cardiovascular disease. DESIGN: Population based, new user, cohort study. DATA SOURCES: Two commercial and Medicare claims databases, 2013-17. PARTICIPANTS: Patients newly prescribed canagliflozin were propensity score matched 1:1 with patients newly prescribed a glucagon-like peptide-1 (GLP-1) receptor agonist. Hazard ratios and rate differences per 1000 person years were computed for the rate of lower limb amputation in the following four groups: group 1, patients aged less than 65 years without baseline cardiovascular disease; group 2, patients aged less than 65 with baseline cardiovascular disease; group 3, patients aged 65 or older without baseline cardiovascular disease; group 4, patients aged 65 or older with baseline cardiovascular disease. Within each group, pooled hazard ratio and rate difference per 1000 person years were calculated by meta-analysis. INTERVENTION: Canagliflozin versus a GLP-1 agonist. MAIN OUTCOME MEASURES: Lower limb amputation requiring surgery. RESULTS: Across the three databases, 310 840 propensity score matched adults who started canagliflozin or a GLP-1 agonist were identified. The hazard ratio and rate difference per 1000 person years for amputation in adults receiving canagliflozin compared with a GLP-1 agonist for each group was: group 1, hazard ratio 1.09 (95% confidence interval 0.83 to 1.43), rate difference 0.12 (-0.31 to 0.55); group 2, hazard ratio 1.18 (0.86 to 1.62), rate difference 1.06 (-1.77 to 3.89); group 3, hazard ratio 1.30 (0.52 to 3.26), rate difference 0.47 (-0.73 to 1.67); and group 4, hazard ratio 1.73 (1.30 to 2.29), rate difference 3.66 (1.74 to 5.59). CONCLUSIONS: The increase in rate of amputation with canagliflozin was small and most apparent on an absolute scale for adults aged 65 or older with baseline cardiovascular disease, resulting in a number needed to treat for an additional harmful outcome of 556 patients at six months (that is, 18 more amputations per 10 000 people who received canagliflozin). These results help to contextualize the risk of amputation with canagliflozin in routine care.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".