410Heart failure risk stratification and efficacy of dapagliflozin in patients with type 2 diabetes mellitus
Bibliographic record
Abstract
Abstract Background Patients with type 2 diabetes mellitus (T2DM) are at increased risk of developing heart failure (HF). Treatment with sodium-glucose cotransporter-2 (SGLT2) inhibitors reduces the risk of hospitalization for HF (HHF) in patients with T2DM. Purpose To develop and validate a practical, multivariable clinical risk score for HHF in patients with T2DM and assess whether this score can identify high-risk patients with T2DM who have the greatest reduction in risk for HHF with an SGLT2 inhibitor. Methods We developed a clinical risk score for centrally-adjudicated HHF using independent clinical risk indicators of HHF in 8212 patients with T2DM in the placebo arm of SAVOR-TIMI 53. Candidate variables were assessed using multivariable Cox regression and independent clinical risk indicators achieving statistical significance of p<0.001 were included in the risk score and given weights proportional to the regression coefficients. We externally validated the score in 8578 patients with T2DM in the placebo arm of DECLARE-TIMI 58. Discrimination was assessed using Harrell's c-index. The relative and absolute risk reductions in HHF with the SGLT2 inhibitor dapagliflozin were assessed by baseline HHF risk. Results The 5 independent clinical risk indicators were prior heart failure, atrial fibrillation, coronary artery disease, estimated glomerular filtration rate (eGFR), and urine albumin/creatinine ratio (UACR) (Figure, left). A simple integer-based scheme using these predictors identified a strong >16-fold gradient of HHF risk (p-trend <0.001) in both the derivation and validation cohorts, with c-indices of 0.81 and 0.78, respectively. Whereas relative risk reductions were similar across the risk score (25–34%), absolute risk reductions were greater in those at higher baseline risk (interaction p-value for absolute risk reduction <0.01), with high-risk (2 points) and very high-risk patients (≥3 points) having 1.5% and 2.7% absolute risk reductions in HHF at 4 years with dapagliflozin, translating into NNTs of only 65 and 36, respectively (Figure, right). Conclusion(s) Risk stratification using a novel clinical risk score for HHF in patients with T2DM identifies patients at higher risk for HHF who derive greater benefit from treatment with the SGLT2 inhibitor dapagliflozin. Acknowledgement/Funding SAVOR-TIMI 53 and DECLARE-TIMI 58 were sponsored by AstraZeneca.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".