Heart failure patients with implantable cardioverter-defibrillator: molecular predictors for risk assessment of adverse events
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: None. Objective. The objectives of the study were to evaluate a multimarker strategy including galectin-3, soluble ST2 (sST2) and NT-proBNP for risk stratification of adverse cardiovascular events (ACE) and death in heart failure (HF) patients with implantable cardioverter-defibrillator (ICD) during the 12-month follow-up period. Methods. A total of 57 patients (41 men, median age of 65 [59; 68] years) of New York Heart Association (NYHA) class II-IV and baseline LVEF of 34% [26; 44]% were enrolled in the study. 71% of patients had prior myocardial infarction and 86% received prior myocardial revascularization (coronary artery bypass graft/stent). Serum levels of sST2, NT-proBNP and galectin-3 were measured using an enzyme immunoassay at baseline. All patients received implantation of cardioverter-defibrillator. Results. After the 12-month follow-up period, all patients were divided into 2 groups: group A (n = 17) comprised patients with ASE, group B (n = 40) comprised those without it. During 12-month follow-up period 15.8% of patients (n = 9) died. The median values of sST2 in group A were higher (p = 0.001) by 51% than in group B (24.42 [22.46; 31.4] and 49.94 [37.4; 58.54] ng/mL, respectively). The median values of galectin-3 in group A (p = 0.002) were 14.7 [9.95; 24.3] ng/L and 9.2 [7.2; 14.7] ng/L in group B. The median values of NT-proBNP were also higher (p = 0.021) by 22% in patients with ASE. Odds ratio for hyperexpression of sST2 was 3.06 (95% CI 2.89-3.17; p <0.0001), 3.79 for galectin-3 (95% CI 2.67-4.11; p < 0.001), and 1.27 for NT-proBNP (95% CI 0.89-2.21; p = 0.036). Reclassification did not significantly benefit after NT-proBNP addition into the full model; some indices even worsened with all 3 biomarkers. Based on ROC-analysis, baseline sST2 concentration of 34.43 ng/mL (sensitivity of 86.7%, specificity of 71.9%, and AUC of 0.78; p < 0.0001) and baseline galectin-3 concentration of 11.6 ng/L (sensitivity of 67.5%, specificity of 70.6%, and AUC of 0.72; p = 0.0014) were identified as a cut-off values predicting adverse cardiac events. The combined evaluation of both biomarkers increased the predictive value of the analysis (sensitivity of 90.1%, specificity of 79.1%, and AUС of 0.84; p < 0.0001). Conclusion. Our data suggest that ST2 and galectin-3 may be used as diagnostic biomarkers in HF patients with ICD. The combined use of sST2 and galectin-3 demonstrated higher diagnostic sensitivity and specificity for prediction of adverse outcomes. However, NT-proBNP in addition to ST2 and galectin-3 biomarkers had a limited effect on risk stratification.
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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 | 0.001 |
| 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".