Sodium-Glucose Cotransporter 2 Inhibitors and the Risk of Pneumonia and Septic Shock
Bibliographic record
Abstract
CONTEXT: Individuals with type 2 diabetes mellitus (DM) have an increased risk of pneumonia and septic shock. Traditional glucose-lowering drugs have recently been found to be associated with a higher risk of infections. It remains unclear whether sodium-glucose cotransporter 2 inhibitors (SGLT2is), which have pleiotropic/anti-inflammatory effects, may reduce the risk of pneumonia and septic shock in DM. METHODS: MEDLINE, Embase, and ClinicalTrials.gov were searched from inception up to May 19, 2022, for randomized, placebo-controlled trials of SGLT2i that included patients with DM and reported outcomes of interest (pneumonia and/or septic shock). Study selection, data extraction, and quality assessment (using the Cochrane Risk of Bias Assessment Tool) were conducted by independent authors. A fixed-effects model was used to pool the relative risk (RRs) and 95% CI across trials. RESULTS: Out of 4568 citations, 26 trials with a total of 59 264 patients (1.9% developed pneumonia and 0.2% developed septic shock) were included. Compared with placebo, SGLT2is significantly reduced the risk of pneumonia (pooled RR 0.87, 95% CI 0.78-0.98) and septic shock (pooled RR 0.65, 95% CI 0.44-0.95). There was no significant heterogeneity of effect size among trials. Subgroup analyses according to the type of SGLT2i used, baseline comorbidities, glycemic control, duration of DM, and trial follow-up showed consistent results without evidence of significant treatment-by-subgroup heterogeneity (all Pheterogeneity > .10). CONCLUSION: Among DM patients, SGLT2is reduced the risk of pneumonia and septic shock compared with placebo. Our findings should be viewed as hypothesis generating, with concepts requiring validation in future studies.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".