High Neutrophil-to-Lymphocyte Ratio as a Predictor of All-Cause and Cardiovascular-Related Mortality in Hemodialysis Patients: A Systematic Review and Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
Background Chronic kidney disease (CKD) remains a major cause of mortality. Recent studies have demonstrated a correlation between the neutrophil-to-lymphocyte Ratio (NLR), which is an inflammatory biomarker, and various chronic diseases. This study aims to assess high NLR as a prognostic indicator for all-cause and cardiovascular (CV)-related mortality in patients with CKD undergoing hemodialysis (HD). Materials and Methods This systematic review (SR) and meta-analysis (MA) were done based on preferred reporting items for systematic reviews and meta-analyses statements 2020. The literature review identified 555 studies up to August 2023 from PubMed, EBSCOHost, ProQuest, Cochrane, and Google Scholar databases using predetermined keywords. Newcastle-Ottawa Scale (NOS) was used to assess the bias of these studies. Data were extracted and MA was done using RevMan. Results Nine and six relevant studies were included for SR and MA, respectively. According to NOS risk of bias, all studies showed overall good quality. HD patients with high NLR had a significantly increased risk of all-cause mortality (3.83 times higher) than those with low NLR (95% CI: 1.85-7.93; p=0.0003; I2=83%). Similarly, HD patients with high NLR had an increased risk of CV-related mortality (1.19 times) than those with low NLR, though not significant (95%CI: 0.82-1.72; p=0.37; I2=60%). Conclusion This study shows a correlation between high NLR values and increased risk of all-cause and CV-related mortality in CKD patients undergoing HD (higher ratio than low NLR values).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| 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".