Association of NTproBNP and cTnI with outpatient sudden cardiac death in hemodialysis patients: the Choices for Healthy Outcomes in Caring for ESRD (CHOICE) study
Bibliographic record
Abstract
BACKGROUND: Sudden cardiac death (SCD) is the most common etiology of death in hemodialysis patients but not much is known about its risk factors. The goal of our study was to determine the association and risk prediction of SCD by serum N-terminal prohormone of brain natriuretic peptide (NTproBNP) troponin I (cTnI) in hemodialysis patients. METHODS: We measured NTproBNP and cTnI in 503 hemodialysis patients of a national prospective cohort study. We determined their association with SCD using Cox regression, adjusting for demographics, co-morbidities, and clinical factors and risk prediction using C-statistic and Net Reclassification Improvement (NRI). RESULTS: Patients' mean age was 58 years and 54 % were male. During follow-up (median 3.5 years), there were 75 outpatient SCD events. In unadjusted and fully-adjusted models, NTproBNP had a significant association with the risk of SCD. Analyzed as a continuous variable, the risk of SCD increased 27 % with each 2-fold increase in NTproBNP (HR, 1.27 per doubling; 95 % CI, 1.13-1.43; p < 0.001). In categorical models, the risk of SCD was 3-fold higher in the highest tertile of NTproBNP (>7,350 pg/mL) compared with the lowest tertile (<1,710 pg/mL; HR for the highest tertile, 3.03; 95 % CI, 1.56-5.89; p = 0.001). Higher cTnI showed a trend towards increased risk of SCD in fully adjusted models, but was not statistically significant (HR, 1.17 per doubling; 95 % CI, 0.98-1.40; p = 0.08). Sensitivity analyses using competing risk models showed similar results. Improvement in risk prediction by adding cardiac biomarkers to conventional risk factors was greater with NTproBNP (C-statistic for 3-year risk: 0.810; 95 % CI, 0.757 to 0.864; and continuous NRI: 0.270; 95 % CI, 0.046 to 0.495) than with cTnI. CONCLUSIONS: NTproBNP is associated with the risk of SCD in hemodialysis patients. Further research is needed to determine if biomarkers measurement can guide SCD risk prevention strategies in dialysis patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".