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Sodium-Glucose Cotransporter-2 Inhibitors and Major Adverse Cardiovascular Outcomes: A SMART-C Collaborative Meta-Analysis

2024· review· en· W4394061861 on OpenAlexaff
Siddharth M. Patel, Yu Mi Kang, KyungAh Im, Brendon L. Neuen, Stefan D. Anker, Deepak L. Bhatt, Javed Butler, David Z.I. Cherney, Brian Claggett, Robert A. Fletcher, William G. Herrington, Silvio E. Inzucchi, Meg Jardine, Kenneth W. Mahaffey, Darren K. McGuire, John J.V. McMurray, Bruce Neal, Milton Packer, Vlado Perkovic, Scott D. Solomon, Natalie Staplin, Muthiah Vaduganathan, Christoph Wanner, David C. Wheeler, Faı̈ez Zannad, Yujie Zhao, Hiddo J.L. Heerspink, Marc S. Sabatine, Stephen D. Wiviott

Bibliographic record

VenueCirculation · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsToronto General Hospital
FundersBaylor University Medical CenterUniversitair Medisch Centrum GroningenUniversity of GlasgowInstitut National de la Santé et de la Recherche MédicaleNational Institutes of HealthRijksuniversiteit GroningenBritish Heart FoundationAuris HealthUniversity College LondonImperial College LondonNational Heart, Lung, and Blood InstituteUniversité de LorraineNational Institute of Diabetes and Digestive and Kidney DiseasesBaylor UniversityAstraZeneca United States
KeywordsMedicineMeta-analysisAdverse effectTransporterPharmacologyInternal medicineIntensive care medicineBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Sodium-glucose cotransporter-2 inhibitors (SGLT2i) consistently improve heart failure and kidney-related outcomes; however, effects on major adverse cardiovascular events (MACE) across different patient populations are less clear. METHODS: This was a collaborative trial-level meta-analysis from the SGLT2i Meta-analysis Cardio-Renal Trialists Consortium, which includes all phase 3, placebo-controlled, outcomes trials of SGLT2i across 3 patient populations (patients with diabetes at high risk for atherosclerotic cardiovascular disease, heart failure [HF], or chronic kidney disease). The outcomes of interest were MACE (composite of cardiovascular death, myocardial infarction , or stroke), individual components of MACE (inclusive of fatal and nonfatal events), all-cause mortality, and death subtypes. Effect estimates for SGLT2i versus placebo were meta-analyzed across trials and examined across key subgroups (established atherosclerotic cardiovascular disease, previous myocardial infarction, diabetes, previous HF, albuminuria, chronic kidney disease stages, and risk groups). RESULTS: A total of 78 607 patients across 11 trials were included: 42 568 (54.2%), 20 725 (26.4%), and 15 314 (19.5%) were included from trials of patients with diabetes at high risk for atherosclerotic cardiovascular disease, HF, or chronic kidney disease, respectively. SGLT2i reduced the rate of MACE by 9% (hazard ration [HR], 0.91 [95% CI, 0.87–0.96], P <0.0001) with a consistent effect across all 3 patient populations ( I 2 =0%) and across all key subgroups. This effect was primarily driven by a reduction in cardiovascular death (HR, 0.86 [95% CI, 0.81–0.92], P <0.0001), with no significant effect for myocardial infarction in the overall population (HR, 0.95 [95% CI, 0.87–1.04], P =0.29), and no effect on stroke (HR, 0.99 [95% CI, 0.91–1.07], P =0.77). The benefit for cardiovascular death was driven primarily by reductions in HF death and sudden cardiac death (HR, 0.68 [95% CI, 0.46–1.02] and HR, 0.86 [95% CI, 0.78–0.95], respectively) and was generally consistent across subgroups, with the possible exception of being more apparent in those with albuminuria ( P interaction =0.02). CONCLUSIONS: SGLT2i reduce the risk of MACE across a broad range of patients irrespective of atherosclerotic cardiovascular disease, diabetes, kidney function, or other major clinical characteristics at baseline. This effect is driven primarily by a reduction of cardiovascular death, particularly HF death and sudden cardiac death, without a significant effect on myocardial infarction in the overall population, and no effect on stroke. These data may help inform selection for SGLT2i therapies across the spectrum of cardiovascular-kidney-metabolic disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.314
Teacher spread0.256 · 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 teacher head, not a consensus.

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

Citations165
Published2024
Admission routes1
Has abstractyes

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