Association of soluble ST2 with all-cause and cardiovascular mortality in renal transplant recipients: a single-centre cohort study
Bibliographic record
Abstract
BACKGROUND: Soluble ST2 is a novel biomarker of myocardial fibrosis with an established role in prognostication of patients with heart failure. Its role in cardiovascular risk prediction for renal transplant recipients has not been investigated despite promising results for ST2 in other populations with renal disease. METHODS: In this prospective cohort study, 367 renal transplant recipients were followed up for a median of 16.2 years to investigate the association of soluble ST2 concentration with all-cause mortality. Cardiovascular mortality and major adverse cardiovascular events were secondary outcomes. Cox regression models were used to calculate hazard ratios and 95% confidence intervals for ST2 before and after adjustments. ST2 concentration was analysed both as a continuous variable and following categorisation according to the recommended cut-point of 35 ng/ml. RESULTS: A twofold higher ST2 concentration was associated with a 36% increased risk of all-cause mortality after adjustment for conventional cardiovascular risk factors and high-sensitivity C-reactive protein (adjusted hazard ratio 1.36; 95% confidence interval 1.06-1.75; p = 0.016). Associations with ST2 concentration were similar for cardiovascular events (adjusted hazard ratio 1.31; 95% confidence interval 1.00-1.73; p = 0.054), but were stronger for cardiovascular mortality (adjusted hazard ratio 1.61; 95% confidence interval 1.07-2.41; p = 0.022). Addition of ST2 to risk prediction models for mortality and cardiovascular events failed to improve their predictive accuracy. CONCLUSIONS: ST2 is associated with, but does not improve prediction of, adverse outcomes in renal transplant recipients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".