Exploring the Platelet-to-Lymphocyte Ratio for Risk Stratification in Heart Failure: A Systematic Review
Bibliographic record
Abstract
Heart failure (HF) remains a global health challenge with high morbidity and mortality, necessitating reliable biomarkers for risk stratification. The platelet-to-lymphocyte ratio (PLR), an emerging inflammatory marker, has shown prognostic potential in cardiovascular diseases, but its utility in HF remains inconsistently reported. This systematic review synthesizes evidence on PLR's prognostic value in HF, focusing on mortality, hospitalization, and its role in multimarker models. We searched four databases -PubMed, Scopus, Web of Science, and Cochrane Library - for English-language observational studies published between January 2020 and June 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Fourteen studies (n=14) were included after screening 172 records. Inclusion criteria comprised adult HF patients with PLR assessed as a prognostic factor; exclusions included reviews, editorials, abstracts without full data, animal studies, and non-English publications. Data on study characteristics, PLR cut-offs, outcomes, effect estimates, and adjustment covariates were extracted. Risk of bias was assessed using the Newcastle-Ottawa Scale. A meta-analysis was not performed due to high heterogeneity in study design, PLR measurement methods, and outcome definitions. Heterogeneity was further evaluated narratively based on methodological inconsistencies, differences in population characteristics, and statistical adjustments. Elevated PLR was significantly associated with increased mortality in ICU and acute HF settings, particularly when combined with the neutrophil-to-lymphocyte ratio (NLR), suggesting additive prognostic value in multimarker models. In contrast, PLR showed limited predictive utility in stable or community-dwelling HF cohorts. Risk of bias findings influenced interpretation, with stronger associations observed in studies with low bias scores. PLR cut-off thresholds varied substantially across studies, affecting comparability. While PLR adds incremental value in acute settings, especially when integrated with other inflammatory markers, its standalone use in chronic HF remains uncertain. Standardization of PLR measurement and further prospective research are essential to clarify its pathophysiological role and clinical applicability.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.016 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".