Risk Stratification for Heart Failure and Death in an Acute Coronary Syndrome Population Using Inflammatory Cytokines and N-Terminal Pro-Brain Natriuretic Peptide
Bibliographic record
Abstract
BACKGROUND: Inflammation in acute coronary syndrome (ACS) can identify those at greater long-term risks for heart failure (HF) and death. The present study assessed the performance of interleukin (IL)-6, IL-8, and monocyte chemoattractant protein-1 (MCP-1) (cytokines involved in the activation and recruitment of leukocytes) in addition to known biomarkers [e.g., N-terminal pro-brain natriuretic peptide (NT-proBNP)] for predicting HF and death in an ACS population. METHODS: In a cohort of 216 ACS patients, NT-proBNP (Elecsys; Roche) and IL-6, IL-8, and MCP-1 (evidence investigator; Randox) were measured in serial specimens collected early after symptom onset (n = 723). We collected at least 2 specimens from each participant: an early specimen (median 2 h; interquartile range 2-4 h) and a later specimen (9 h; 9-9 h), and used the later specimens' biomarker concentrations for risk stratification. RESULTS: An increase in both IL-6 and NT-proBNP was observed but not for IL-8 or MCP-1 early after pain onset. Kaplan-Meier analysis demonstrated that individuals with increased NT-proBNP (>183 ng/L) or cytokines (IL-6 > 6.4 ng/L; above upper limit of normal for IL-8 or MCP-1) had a greater probability of death or HF in the following 8 years (P <0.05). In a Cox proportional hazard model adjusted for both CRP and troponin I, increased IL-6, MCP-1, and NT-proBNP remained significant risk factors. Combining all 3 biomarkers resulted in a higher likelihood ratio for death or HF than models restricted to any 2 of these biomarkers. CONCLUSION: IL-6, MCP-1, and NT-proBNP are independent predictors of long-term risk of death or HF, highlighting the importance of identifying leukocyte activation and recruitment in ACS patients.
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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) | 0.000 | 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".