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Record W4402289378 · doi:10.1016/s2213-8587(24)00219-5

Sodium–glucose co-transporter-2 inhibitors for hospitalised patients with COVID-19: a prospective meta-analysis of randomised trials

2024· review· en· W4402289378 on OpenAlexaff
Claire L. Vale, Peter J. Godolphin, David J. Fisher, Peter Horby, Mikhail Kosiborod, Judith S. Hochman, Katie E Webster, Julian P. T. Higgins, Andrew D. Althouse, Otávio Berwanger, Remo H.M. Furtado, Samvel B. Gasparyan, Richard Haynes, Gary G. Koch, Martin Landray, Eric Leifer, John M. Marshall, Srinivas Murthy, Matthew D. Neal, Natalie Staplin, Janet Dı́az, Jonathan A C Sterne, Manu Shankar‐Hari

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoSt. Michael's Hospital
FundersPublic Health ScotlandNational Heart, Lung, and Blood InstituteNIHR Nottingham Biomedical Research CentreMedical Research CouncilSwansea UniversityNational Institutes of HealthPublic Health EnglandNational Institute for Health and Care ResearchAstraZenecaUK Research and InnovationNational Heart and Lung InstituteNIHR Oxford Biomedical Research CentreWellcome TrustWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsMedicinePlaceboCoronavirus disease 2019 (COVID-19)Meta-analysisRandomized controlled trialMEDLINE2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Internal medicineIntensive care medicinePharmacologyAlternative medicineVirologyDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.032
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0320.054
Meta-epidemiology (narrow)0.0040.002
Meta-epidemiology (broad)0.0260.064
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.127
GPT teacher head0.393
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2024
Admission routes1
Has abstractyes

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