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Record W2808467887 · doi:10.1016/j.jacc.2018.03.009

Cardiovascular Events Associated With SGLT-2 Inhibitors Versus Other Glucose-Lowering Drugs

2018· article· en· W2808467887 on OpenAlexaffabout
Carolyn S.P. Lam, Shun Kohsaka, Avraham Karasik, Jonathan E. Shaw, Navdeep Tangri, Su‐Yen Goh, Marcus Thuresson, Hungta Chen, Filip Surmont, Niklas Hammar, Matthew A. Cavender, Alex Z. Fu, John Wilding, Kamlesh Khunti, Anna Norhammar, Kåre I. Birkeland, Marit E. Jørgensen, Reinhard W. Holl, Hanne Løvdal Gulseth, Bendix Carstensen, Esther Bollow, Josep Franch‐Nadal, Luis A. Garcı́a Rodrı́guez, Suzanne V. Arnold, Johan Bodegård, Kyle Nahrebne, Betina T. Blak, Eric Wittbrodt, Matthias Saathoff, Yusuke Noguchi, Donna Tan, Maro R. I. Williams, Hye Won Lee, Maya Greenbloom, Oksana Kaidanovich‐Beilin, Khung Keong Yeo, Yong Mong Bee, Joan Khoo, Agnes Koong, Yee How Lau, Fei Gao, Wee Boon Tan, Hanis Abdul Kadir, Kyoung Hwa Ha, Jinhee Lee, Gabriel Chodick, Cheli Melzer Cohen, Reid Whitlock, Lucía Cea Soriano, Oscar Fernández Cantero, Ellen Riehle, Jenni Ilomäki, Dianna J. Magliano

Bibliographic record

VenueJournal of the American College of Cardiology · 2018
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Manitoba
FundersSaint Luke's Health SystemAstraZeneca
KeywordsEmpagliflozinDapagliflozinMedicineCanagliflozinHazard ratioInternal medicineConfidence intervalHeart failureDiabetes mellitusPropensity score matchingType 2 diabetesRelative riskEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Randomized trials demonstrated a lower risk of cardiovascular (CV) events with sodium-glucose cotransporter-2 inhibitors (SGLT-2i) in patients with type 2 diabetes (T2D) at high CV risk. Prior real-world data suggested similar SGLT-2i effects in T2D patients with a broader risk profile, but these studies focused on heart failure and death and were limited to the United States and Europe. OBJECTIVES: The purpose of this study was to examine a broad range of CV outcomes in patients initiated on SGLT-2i versus other glucose-lowering drugs (oGLDs) across 6 countries in the Asia Pacific, the Middle East, and North American regions. METHODS: New users of SGLT-2i and oGLDs were identified via claims, medical records, and national registries in South Korea, Japan, Singapore, Israel, Australia, and Canada. Propensity scores for SGLT-2i initiation were developed in each country, with 1:1 matching. Hazard ratios (HRs) for death, hospitalization for heart failure (HHF), death or HHF, MI, and stroke were assessed by country and pooled using weighted meta-analysis. RESULTS: After propensity-matching, there were 235,064 episodes of treatment initiation in each group; ∼27% had established CV disease. Patient characteristics were well-balanced between groups. Dapagliflozin, empagliflozin, ipragliflozin, canagliflozin, tofogliflozin, and luseogliflozin accounted for 75%, 9%, 8%, 4%, 3%, and 1% of exposure time in the SGLT-2i group, respectively. Use of SGLT-2i versus oGLDs was associated with a lower risk of death (HR: 0.51; 95% confidence interval [CI]: 0.37 to 0.70; p < 0.001), HHF (HR: 0.64; 95% CI: 0.50 to 0.82; p = 0.001), death or HHF (HR: 0.60; 95% CI: 0.47 to 0.76; p < 0.001), MI (HR: 0.81; 95% CI: 0.74 to 0.88; p < 0.001), and stroke (HR: 0.68; 95% CI: 0.55 to 0.84; p < 0.001). Results were directionally consistent across both countries and patient subgroups, including those with and without CV disease. CONCLUSIONS: In this large, international study of patients with T2D from the Asia Pacific, the Middle East, and North America, initiation of SGLT-2i was associated with a lower risk of CV events across a broad range of outcomes and patient characteristics. (Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors [CVD-REAL]; NCT02993614).

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)0.007
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.008
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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 designObservational
Domainnot available
GenreEmpirical

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".

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Citations466
Published2018
Admission routes2
Has abstractyes

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