A Biomarker-Based Score for Risk of Hospitalization for Heart Failure in Patients With Diabetes
Bibliographic record
Abstract
Objective: Heart failure (HF) is an impactful complication of type 2 diabetes mellitus (T2DM). We aimed to develop and validate a risk score for hospitalization for HF (HHF) incorporating biomarkers and clinical factor(s) in patients with T2DM. Research Design and Methods: We derived a risk score for HHF using clinical data, high-sensitivity troponin T (hsTnT), and N-terminal-pro-B-type natriuretic peptide (NT-proBNP) from 6,106 placebo-treated patients with T2DM in SAVOR-TIMI 53. Candidate variables were assessed using Cox regression. The strongest indicators of HHF risk were included in the score using integer weights. The score was externally validated in 7,251 placebo-treated patients in DECLARE-TIMI 58. The effect of dapagliflozin on HHF was assessed by risk category in DECLARE-TIMI 58. Results: The strongest indicators of HHF risk were NT-proBNP, prior HF, and hsTnT (each p<0.001). A risk score using these 3 variables identified a gradient of HHF risk (p-trend<0.001) in the derivation and validation cohorts, with c-indices of 0.87 (95%CI, 0.84-0.89) and 0.84 (0.81-0.86), respectively. Whereas there was no significant effect of dapagliflozin vs. placebo on HHF in the low-risk group (hazard ratio [HR] 0.98[0.50-1.92]), dapagliflozin significantly reduced HHF in the intermediate-, high-, and very high-risk groups (HR 0.64[0.43-0.95], 0.63[0.43-0.94], and 0.72[0.54-0.96], respectively). Correspondingly, absolute risk reductions increased across these latter 3 groups: 1.0%(0.0%-1.9%), 3.0%(0.7%-5.3%), and 4.4%(-0.2%-8.9%) (p-trend<0.001). Conclusions: We developed and validated a risk score for HHF in T2DM that incorporated NT-proBNP, prior HF, and hsTnT. The risk score identifies patients at higher risk of HHF who derive greater absolute benefit from dapagliflozin.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".