Sodium–glucose co-transporter-2 inhibitors for hospitalised patients with COVID-19: a prospective meta-analysis of randomised trials
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose co-transporter-2 (SGLT2) inhibitors have been proposed as a potential treatment for adults hospitalised with COVID-19, due to their potential anti-inflammatory and endothelial protective effects. Published evidence from randomised control trials (RCTs) does not provide evidence of benefit. We aimed to estimate the effect of oral administration of SGLT2 inhibitors compared with usual care or placebo in adults hospitalised with COVID-19. METHODS: Eligible RCTs that estimated the effect of oral administration of SGLT2 inhibitors compared with usual care or placebo on 28-day all-cause mortality (primary outcome) were included in this prospective meta-analysis. The primary safety outcome was ketoacidosis by 28 days. Trials were identified through systematic searches of ClinicalTrials.gov, EudraCT, and the WHO ISRCTN registry between Nov 1, 2022 and Jan 31, 2023. The search terms were "random*" AND "COVID" AND each SGLT2i, not restricted by trial status or language. Individual searches were then combined. Prespecified summary outcome data, overall and within subgroups of interest, were provided by each trial. The primary analyses were inverse variance weighted meta-analysis of odds ratios (ORs). Risk of bias was assessed using the Cochrane Risk of Bias tool. This study was registered with PROSPERO, CRD42023406442. FINDINGS: for inconsistency across trials 0%). The risk of bias was assessed as being low. Ketoacidosis was observed in seven participants in the SGLT2 inhibitor group and two patients in the usual care or placebo group. INTERPRETATION: Although administration of SGLT2 inhibitor was safe, we found no clear evidence that adding SGLT2 inhibitor therapy improved outcomes in patients hospitalised with COVID-19 compared with usual care or placebo. These data do not support the use of SGLT2 inhibitors as standard treatment in adults hospitalised for COVID-19. FUNDING: None.
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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 | 0.032 | 0.054 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.064 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".