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Record W7127612487 · doi:10.1093/eurheartj/ehaf784.982

Impact of SGLT2-inhibitors on cardiac function and structural changes in patients with heart failure and reduced ejection fraction - Results from the REWIN-HFrEF Study

2025· article· en· W7127612487 on OpenAlexaff
M G De Angelis, R M Inciardi, M Correale, M Amarante, L Assoni, Antonio Maria Sammartino, Elisa Brangi, Miguel Ángel Mazzini, Michele Granatiero, L Battisti, C Tomasi, Savina Nodari

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsEjection fractionHeart failureAmbulatoryCardiac function curveCohortProspective cohort studyCohort study

Abstract

fetched live from OpenAlex

Abstract Background The use of SGLT2 inhibitors has been shown to improve survival and reduce adverse events in patients with heart failure with reduced ejection fraction (HFrEF). Less is known on the role of SGLT2i to promote cardiac structure and function changes in this population. Aim of the Study To evaluate the impact of SGLT2 inhibitors on cardiac function and structural changes using both conventional and advanced echocardiographic parameters in a real-world cohort of ambulatory HFrEF patients. Methods We conducted a prospective multicenter observational cohort study, enrolling 151 consecutive patients with stable chronic HFrEF from February 2022 to June 2022. Baseline demographic, clinical, and laboratory parameters were collected and subsequently re-evaluated at the six-month follow-up. Additionally, all participants underwent standard and advanced transthoracic echocardiography (including speckle-tracking analysis) both at the time of enrollment and after six months of treatment with SGLT-2i. Results A total of 151 patients were enrolled, mean age was 70 ± 13 years, 28 (19%) were female. Baseline mean ejection fraction was 32 ± 6.6%. After six months of SGLT2-i therapy, mean ejection fraction increased from 32% to 35% (P: < 0.001). A trend towards improvement without statistical significance was also observed in global longitudinal strain (GLS) (from -10.37 ± 3.92% to -10.81 ± 4.12%) as well as in RV functional parameters (TAPSE: from 19.43 ± 3.85 mm to 19.67 ± 3.06 mm; S’TDI: from 9.93 ± 2.56 cm/sec to 10.12 ± 2.62 cm/sec; FAC: from 48.90 ± 9.94% to 49.32 ± 11.72%). Statistically significant improvements were also found in LV end-diastolic and end-systolic volumes (LVEDV from 178.17 ± 62.69 mL to 161. 80 ± 57.16, P: < 0.001; LVESV from 122.33 ± 50.23 mL to 108.00 ± 46.23, P: < 0.001) and in right and left atrial volumes (respectively RAV from 54.63 ± 30.87 mL to 50.43 ± 27.68 mL P: 0.031; and LAV from 92.92 ± 41.92 mL to 86.19 ± 38.57, P: 0.009). None of these parameters showed significant differential changes in diabetic compared to non-diabetic patients, nor in ischemic versus non-ischemic patients. Conclusions Treatment with SGLT2 inhibitors showed a remarkable improvement in left ventricular structure and function after 6 months of therapy in a cohort of real-world ambulatory HFrEF patients.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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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Citations0
Published2025
Admission routes1
Has abstractyes

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