Abstract 16789: Modeling the Optimal Strategy of Natriuretic Peptide Testing for Heart Failure Prevention in Diabetes: A Pooled Cohort Analysis
Bibliographic record
Abstract
Introduction: N-terminal pro-B-type natriuretic peptide (NT-proBNP) is associated with heart failure (HF) risk and measurement is recommended in diabetes. Incorporation of NT-proBNP with WATCH-DM, a HF risk score validated in diabetes, improved HF risk prediction in low-risk individuals (score <13). Whether WATCH-DM can improve the yield of NT-proBNP testing and inform allocation of SGLT2 inhibitors is unknown. Methods: Adults with diabetes and free of HF at baseline from ARIC, CHS, CRIC, DHS, FOS, and MESA cohorts were included. Participants without cardiovascular disease (CVD) were stratified into high- vs. low-risk categories based on 4 approaches: 1) WATCH-DM (score ≥ vs. <13); 2) NT-proBNP (≥ vs. <125 pg/mL); 3) WATCH-DM plus NT-proBNP (both high vs. not); 4) WATCH-DM-based NT-proBNP (if score <13, measure NT-proBNP) (either high vs. not). Five-year cumulative incidence rates of HF, number needed to treat (NNT 5 ) and number needed to screen (NNS 5 ) were estimated based on pooled treatment effects of SGLT2 inhibitors. Results: The study included 6,410 participants (47.5% women, 35.1% Black, 20.5% with CVD) with 428 (6.7%) incident HF events over 5-year follow-up. The 5-year risk of HF was 12.3% among participants with CVD and comparable to those without CVD identified as high-risk based on all 4 approaches ( Figure ). Among participants without CVD, WATCH-DM-based NT-proBNP identified 46% of participants as high-risk and accounted for 80% of HF events. The NNT 5 with an SGLT2 inhibitor to prevent 1 HF event in participants with CVD was 25 and similar to the primary prevention population identified as high-risk ( Figure ). The NNS 5 was lowest in the WATCH-DM-based NT-proBNP strategy ( Figure ). Conclusions: Selective NT-proBNP assessment based on a validated clinical risk score for HF improved the yield of testing and may represent an efficient approach to risk stratification and allocation of therapies among individuals with diabetes.
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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 | 0.043 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".