Troponin and BNP are markers for subsequent non-ischaemic congestive heart failure: the Caerphilly Prospective Study (CaPS)
Bibliographic record
Abstract
Objective To examine the long-term predictive value of 28 biomarkers for subsequent non-ischaemic congestive heart failure (CHF) and separately for other cardiovascular outcomes (myocardial infarction (MI) and stroke). Methods The Caerphilly Prospective Study recruited 2171 men aged 55–69 years from the general population in 1989–1993; men were screened for evidence of cardiovascular disease (CVD) and followed for clinical cardiovascular events. Fasting blood samples were stored at −70°C until assayed for novel biomarkers in 2010–2013. A competing risks proportional hazards regression analysis was used to estimate subhazard ratios (SHRs) for each biomarker for each cardiovascular outcome. Results During follow-up (average 13 years), only new, initial events were evaluated in the whole cohort: 584 MIs, 313 strokes and 261 episodes of CHF (not associated with acute MI). In a subcohort of men who had no clinical history or evidence of CVD at baseline examination (n=1279) those in the top third of the distributions of troponin and B-type natriuretic peptide (BNP) showed a threefold increase in risk for subsequent CHF as a first event after adjustment for all conventional risk factors (SHRs 3.37, 95% CI 1.39 to 8.14 and 3.23, 95% CI 1.45 to 7.23), respectively, in contrast to moderate elevations in risk for acute MI (troponin SHR 1.63, 95% CI 1.10 to 2.41) and for stroke (BNP SHR 1.75 95% CI 1.06 to 2.88). Conclusion Troponin and BNP could be considered as potentially useful screening tools to detect subjects without prior CVD at increased risk of developing CHF in subsequent years in addition to having lesser roles for predicting subsequent MI (troponin) or stroke (BNP).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".