Sodium-Glucose Cotransporter 2 Inhibition and Hospitalizations in Patients with CKD
Bibliographic record
Abstract
Key Points In this post hoc analysis of Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation, canagliflozin reduced the risk of first and subsequent all-cause hospitalization by 14%. Sodium-glucose cotransporter 2 inhibitor reduced the risk of hospitalization by 15%, with consistent effects irrespective of kidney function, albuminuria, and diabetes status. Absolute reductions in hospitalizations with sodium-glucose cotransporter 2 inhibitor in patients with CKD are substantial, with major implications for individuals and health systems. Background Unplanned hospitalization, irrespective of cause, is a meaningful outcome for patients, caregivers, clinicians, and health systems. The effects of sodium-glucose cotransporter 2 (SGLT2) inhibitors on all-cause hospitalization in patients with CKD have not been systematically evaluated. Methods We conducted a post hoc analysis of the Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation trial to evaluate the effect of canagliflozin on nonelective all-cause hospitalization using Cox proportional hazards models, with recurrent events analysis to assess effects on first and subsequent hospitalizations. We performed inverse variance weighted meta-analysis of three placebo-controlled SGLT2 inhibitor CKD-focused trials to assess the relative and absolute effects of SGLT2 inhibitors on first and subsequent all-cause hospitalizations overall and across clinically relevant subgroups. For analyses of cause-specific hospitalization, adverse events that were reported by investigators but not adjudicated were used. Results Over a median follow-up of 2.6 years, 3015 hospitalizations occurred among 1543 of 4401 (35%) participants in the Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation trial. Compared with placebo, canagliflozin reduced the risk of first all-cause hospitalization (hazard ratio [HR], 0.88; 95% confidence interval [CI], 0.80 to 0.98; P = 0.02) and first and subsequent hospitalizations (HR, 0.86; 95% CI, 0.76 to 0.96; P = 0.007). In a meta-analysis of three placebo-controlled SGLT2 inhibitor CKD-focused trials, SGLT2 inhibitors reduced the risk of first and subsequent hospitalizations from any cause by 15% (HR, 0.85; 95% CI, 0.78 to 0.95; P < 0.001), with consistent effects irrespective of diabetes, baseline kidney function, and albuminuria (all P interactions > 0.50). Reductions were driven by hospitalizations due to infection, cardiac, renal or urinary, and metabolism or nutritional disorders. We estimated that SGLT2 inhibition in CKD would prevent 36 (95% CI, 13 to 56) unplanned hospitalizations per 1000 patient-years of treatment, across a broad range of patients. Conclusions SGLT2 inhibitors reduce the risk of hospitalizations from any cause in patients with CKD, irrespective of diabetes status, kidney function, and degree of albuminuria. Clinical Trial registry name and registration number: NCT02065791. Podcast This article contains a podcast at https:// dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2025_09_30_CJASNSeptember.20.9.mp3
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.021 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".