Systemic Inflammatory Biomarkers (Interleukin-6, High-Sensitivity C-Reactive Protein, and Neutrophil-to-Lymphocyte Ratio) and Prognosis in Heart Failure: A Meta-Analysis of Prospective Cohort Studies
Bibliographic record
Abstract
Background: Systemic inflammation plays a pivotal role in heart failure (HF) progression, yet no meta-analysis has synthesized prospective cohort data on interleukin-6 (IL-6), high-sensitivity C-reactive protein (hs-CRP), and neutrophil-to-lymphocyte ratio (NLR) as prognostic biomarkers. Objectives: To quantify the independent prognostic value of IL-6, hs-CRP, and NLR for mortality and HF-related outcomes across HF phenotypes. Methods: Following PRISMA and MOOSE guidelines, we searched PubMed, Embase, Scopus, Web of Science, and CENTRAL from January 2014 to October 2025 for prospective cohorts reporting adjusted hazard ratios (HRs). Random-effects meta-analysis pooled HRs; heterogeneity was assessed via I2 statistic, with subgroup and sensitivity analyses for robustness. Quality was evaluated using Newcastle–Ottawa Scale (NOS) and GRADE. Results: Thirteen cohorts (n ≈ 19,000) were included. Elevated IL-6 (five studies) was associated with increased all-cause mortality and composite outcomes (low-moderate heterogeneity, I2 < 35%). hs-CRP (five studies) showed similar prognostic strength, with trajectories amplifying risk. NLR (three studies) independently predicted adverse events with negligible heterogeneity. Associations persisted across HFrEF and HFpEF, acute/chronic settings, and geographic regions, independent of natriuretic peptides and comorbidities (NOS median 8/9; GRADE moderate-to-high). Conclusions: IL-6, hs-CRP, and NLR are robust, independent prognostic biomarkers in HF, supporting their integration into clinical risk stratification and inflammation-targeted therapies. PROSPERO: CRD420251207035.
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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 | 0.031 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.063 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".