24 SGLT-2I therapy in heart failure : challenges and opportunities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".