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Record W3136650528 · doi:10.1136/heartjnl-2020-ics.24

24 SGLT-2I therapy in heart failure : challenges and opportunities

2020· article· en· W3136650528 on OpenAlexaboutno aff
A Radhakrishna, Rebecca Cusack, James C. Barton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDapagliflozinHeart failureCohortInternal medicineEjection fractionAuditRetrospective cohort studyPopulationIntensive care medicineDiabetes mellitusEmergency medicineType 2 diabetes

Abstract

fetched live from OpenAlex

Introduction Heart failure (HF) is a complex disease which is growing to be a significant cause of morbidity and mortality leading to increased cost of chronic care and hospitalization. In the DAPA-HF study, the sodium-glucose co-transporter 2 inhibitor (SGLT-2i) dapagliflozin was shown to reduce the risk of worsening HF and death in patients with HF with reduced ejection fraction (HFrEF). Our goal was to conduct an audit in a tertiary referral centre at University Hospital Galway (UHG) to identify patients with HFrEF who fulfil the eligibility criteria for SGLT-2i therapy, as seen in the DAPA-HF study. We also sought to identify patients with Type 2 Diabetes Mellitus (T2DM) in our HFrEF cohort who are potential candidates for improvement of glycaemic control with SGLT-2i therapy according to the ADA-EASD Guidelines. Methodology A retrospective audit was conducted on 129 patients with HFrEF attending the specialist-led heart failure clinic at UHG between January and March 2020. Demographic, clinical, biochemical and medication data were collected from medical charts and our local digital database:EVOLVE® and CVWeb®. Patients had to meet the DAPA-HF inclusion criteria to be deemed eligible for dapagliflozin therapy. Results Table 1 summarises the baseline clinical data and table 2 summarises the list of medical therapy at our centre. Of note, the 129 patients in our study represented a more elderly cohort compared to the DAPA-HF study population. Only 49/129 (38%) of our HFrEF patients were eligible for SGLT-2i therapy based on the DAPA-HF inclusion criteria. This is primarily due to the higher than expected percentage of patients in our cohort who were asymptomatic (34.9%) and who had low NT-proBNP levels (29.6%). 16/129 (12.4%) had severe CKD with an eGFR <30 ml/min/1.73 m2. There were only 26/129 (20.2%) patients with T2DM of which 6 patients were already on SGLT-2i. The majority had ischemic cardiomyopathy (69%) with concomitant risk factors and (30.8%) had poor glycaemic control. Conclusion This study shows a lower than expected number of patients in our centre who would have been included in the DAPA-HF trial. This could be because many patients in this cohort were already on optimal HF treatment, many being asymptomatic and had low NT-proBNP levels. Some patients were also ineligible for SGLT-2i because of Stage 4 CKD. One-third of the diabetic patients in this HFrEF cohort were not at target HbA1C range and according to the ADA-EASD Guidelines, all these patients should have SGLT-2i added to intensify glycaemic control. Lately, the Canadian Heart Society have updated their guidelines with a strong recommendation to introduce SGLT-2i in diabetics with ischemic cardiomyopathy despite adequate glycaemic control for cardiovascular benefits. SGLT-2i represents an important, but underutilized therapeutic option by cardiologists, likely due to the lack of familiarity on its use. This study reveals that SGLT-2i prescription could potentially increase in HFrEF patients with or without T2DM as guidelines will soon be updated based on robust evidence from large-scale clinical trials and when prescribers become aware of the indication for primary prevention of heart failure hospitalization and death.

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)0.014
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.117
GPT teacher head0.270
Teacher spread0.152 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2020
Admission routes1
Has abstractyes

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