Association of systemic inflammatory markers with clinical adverse prognosis and outcomes in HFpEF: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
Objective To evaluate the association between systemic inflammatory markers and clinical outcomes (all-cause mortality, cardiovascular mortality, and rehospitalization) in patients with heart failure with preserved ejection fraction (HFpEF). Methods We conducted a comprehensive literature search in PubMed, Embase, and Ovid Medline databases from inception to June 27, 2024. Studies were included if they were observational clinical studies involving HFpEF patients over 18 years old, with exposure to systemic inflammatory markers and reporting on adverse prognosis outcomes. The Newcastle-Ottawa Scale (NOS) was used to assess study quality. Results Eight studies ultimately included in the meta-analysis which involved 9,744 participants from six countries. The meta-analysis showed that systemic inflammatory markers were significantly associated with all-cause mortality (HR 1.43, 95% CI 1.19–1.72, p < 0.05), cardiovascular mortality (HR 2.04, 95% CI 1.33–3.12, p < 0.05), and cardiovascular rehospitalization (HR 2.83, 95% CI 0.92–8.67, p < 0.05) in HFpEF patients. Low heterogeneity was observed across studies (I 2 = 0.00%). Sensitivity and publication bias analyses indicated that the results were robust. Conclusion Systemic inflammatory markers demonstrate significant predictive value for adverse clinical outcomes in HFpEF patients. The findings suggest that monitoring systemic inflammation may provide valuable prognostic information for clinicians managing HFpEF patients. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=562698 , identifier (CRD42024562698).
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".