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Record W4306166068 · doi:10.1111/dom.14893

Sodium‐glucose co‐transporter‐2 inhibitors in type 2 diabetes: Are clinical trial benefits for heart failure reflected in real‐world clinical practice? A systematic review and meta‐analysis of observational studies

2022· review· en· W4306166068 on OpenAlexaboutno aff
William Hinton, Abdus Samad Ansari, Martin Whyte, Andrew McGovern, Michael Feher, Neil Munro, Simon de Lusignan

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
FundersUniversity of OxfordNational Institute for Health and Care Research
KeywordsObservational studyMeta-analysisMedicineType 2 diabetesClinical trialSystematic reviewRandomized controlled trialInternal medicineDiabetes mellitusHeart failureClinical PracticeMEDLINEIntensive care medicinePhysical therapyEndocrinology

Abstract

fetched live from OpenAlex

AIM: To determine the absolute risk reduction (ARR) of heart failure events in people treated with sodium-glucose co-transporter-2 (SGLT2) inhibitors. MATERIALS AND METHODS: We searched PubMed, EMBASE, CINAHL and ISI Web of Science for observational studies published to 9 May 2022 that explored the association between SGLT2 inhibitors and any indication for heart failure (including new diagnosis or hospitalization for heart failure) in type 2 diabetes. Identified studies were independently screened by two reviewers and assessed for bias using the Newcastle-Ottawa scale. Eligible studies with comparable outcome data were pooled for meta-analysis using random-effects models, reporting hazard ratios (HRs) with 95% confidence intervals (CIs). The ARR per 100 person-years was determined overall, and in subgroups with and without baseline cardiovascular disease (CVD). RESULTS: From 43 eligible studies, with a total of 4 818 242 participants from 17 countries, 21 were included for meta-analysis. SGLT2 inhibitors were associated with a reduced risk of hospitalization for heart failure (HR 0.65, 95% CI 0.59-0.72) overall and both in those with CVD (HR 0.78, 95% CI 0.68-0.89) and without CVD (HR 0.53, 95% CI 0.39-0.71). Risk reduction for hospitalization for heart failure in people with a history of CVD (ARR 1.17, 95% CI 0.78-1.55) was significantly greater than for those without CVD (ARR 0.39, 95% CI 0.32-0.47). The number-needed-to-treat to prevent one event of hospitalization for heart failure was 86 (95% CI 65-128) person-years of treatment for the CVD group and 256 (95% CI 215-316) person-years for those without CVD. CONCLUSIONS: Real-world SGLT2 inhibitor use supports randomized trial data for the size effect of reduced hospitalization for heart failure in type 2 diabetes, although with a much lower ARR in people without CVD.

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)0.029
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.064
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0250.040
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.267
GPT teacher head0.470
Teacher spread0.203 · 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.

Study designMeta-analysis
DomainMethods
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

Citations15
Published2022
Admission routes1
Has abstractyes

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