Impact of age on the predictive value of NT-proBNP in patients with diabetes mellitus stabilised after an acute coronary syndrome
Bibliographic record
Abstract
Aims To assess the impact of age on the prognostic value of NT-proBNP concentration in patients with type-2 diabetes mellitus (T2DM) stabilised after an Acute Coronary Syndrome (ACS). Methods The AleCardio study compared aleglitazar with placebo in 7226 patients with T2DM and recent ACS. Patients with heart failure were excluded. Median follow-up was 104 weeks. Baseline NT-proBNP plasma concentration was measured centrally. Multivariable Cox regression was used to determine the mortality predictive information provided by NT-proBNP across age groups. Results Median age was 61y (IQR 54, 67). NT-proBNP concentration increased by quartile (Q) of age (median 264, 318, 391, and 588 pg/ml). Compared to Q1, patients in Q4 of NT-proBNP had higher (p < 0.001) adjusted HR for all-cause (aHR 6.9; 95 % CI 4.0–12) and cardiovascular (11; 5.4–23) death. Within each age Q, baseline NT-proBNP in patients who died was 3 times higher than in survivors (all p < 0.001). When age and NT-proBNP levels were modeled as continuous variables, their interaction term was nonsignificant. The relative prognostic information provided by NT-proBNP (percent of total X 2 ) increased from 38 % in age Q1 to 75 % in age Q4 for mortality, and from 50 % to 88 % for CV death. Conclusions Among patients with T2DM stabilised after an ACS, NT-proBNP level predicts death irrespective of age.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".