The Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Patients With Aneurysmal Subarachnoid Hemorrhage: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
The neutrophil–to-lymphocyte ratio (NLR), as an essential systemic inflammation factor, has been widely used as a prognostic indicator in various diseases, such as malignant tumors, cardiovascular disease, and intracranial hemorrhage. An increasing number of studies have believed that NLR is a valuable predictor of prognosis for patients with aneurysmal subarachnoid hemorrhage (aSAH). However, these results remain controversial. In the current study, we planned to carry out a systematic review and meta-analysis to investigate the association between NLR and poor outcome, and the occurrence of delayed cerebral ischemia (DCI). We carried out a comprehensive search for published literatures on PubMed, EMBASE, Cochrane Library, and Web of Science databases from inception to April 1, 2021. We conducted an assessment of all included studies based on the principles proposed in the Newcastle-Ottawa Quality Assessment Scale (NOS). Poor outcome and the occurrence of DCI were considered as the main outcome measure. We calculated the pooled odds ratio (OR) and corresponding 95% confidence interval (CI) to examine the strength of the association of NLR with poor outcome or the occurrence of DCI. We strictly selected a total of 10 studies comprising 4,989 patients. Nine studies reported the association between NLR and poor outcome, and five studies reported the association between NLR and the occurrence of DCI. The pooled results indicated higher NLR was significantly associated with both poorer outcomes (OR = 1.32, 95%CI 1.11–1.57; P = 0.002, I2 = 87%), and the occurrence of DCI (OR = 1.72, 95%CI 1.22–2.41; P = 0.002, I2 = 82%) in aSAH patients. The NLR is a valuable indicator of inflammation to independently predict poor outcome and occurrence of DCI after aSAH, where a higher NLR is significantly associated with poor outcomes and occurrence of DCI. These findings suggest that the NLR can help clinicians evaluate the prognosis and identify potentially severe patients early, which may contribute to better management and improve poor prognosis of aSAH patients.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".