Cardiovascular biomarkers as predictors of adverse outcomes in chronic Chagas cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Chronic Chagas Cardiomyopathy (CCM) is a unique form of cardiomyopathy compared to other etiologies of heart failure. In CCM, risk prediction based on biomarkers has not been well-studied. We assessed the prognostic value of a biomarker panel to predict a composite outcome (CO), including the need for heart transplantation, use of left ventricular assist devices, and mortality. METHODS: Prospective cohort study of 100 adults with different stages of CCM. Serum concentrations of amino-terminal pro-B type natriuretic peptide (NT-proBNP), galectin-3 (Gal-3), neutrophil gelatinase-associated lipocalin (NGAL), high sensitivity troponin T (hs-cTnT), soluble (sST2), and cystatin-C (Cys-c) were measured. Survival analyses were performed using Cox proportional hazard models. RESULTS: During a median follow-up of 52 months, the mortality rate was 20%, while the CO was observed in 25% of the patients. Four biomarkers (NT-proBNP, hs-cTnT, sST2, and Cys-C) were associated with the CO; concentrations of NT-proBNP and hs-cTnT were associated with the highest AUC (85.1 and 85.8, respectively). Combining these two biomarkers above their selected cut-off values significantly increased risk for the CO (HR 3.18; 95%CI 1.31-7.79). No events were reported in the patients in whom the two biomarkers were under the cut-off values, and when both levels were above cut-off values, the CO was observed in 60.71%. CONCLUSION: The combination of NT-proBNP and hs-TnT above their selected cut-off values is associated with a 3-fold increase in the risk of the composite outcome among CCM patients. The use of cardiac biomarkers may improve prognostic evaluation of patients with CCM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".