Neutrophil-lymphocyte ratio as a predictor of outcome following traumatic brain injury: Systematic review and meta-analysis
Notice bibliographique
Résumé
Objectives: The neutrophil-to-lymphocyte ratio (NLR) is a simple and routinely performed hematological parameter; however, studies on NLR as a prognostic tool in traumatic brain injury (TBI) have yielded contradictory results. Materials and Methods: This systematic review and meta-analysis was conducted according to the Preferred Reporting Items in the Systematic Review and Meta-Analysis guidelines 2020. Electronic databases of PubMed, Cochrane Library, Web of Science, and Scopus were searched. The population consisted of TBI patients in the absence of moderate and severe extracranial injury. Day 1 NLR was taken for the analysis. The outcomes evaluated were mortality and the Glasgow Outcome Scale (GOS). No restrictions were placed on the language, year and country of publication, and duration of follow-up. Animal studies were excluded from the study. Studies, where inadequate data were reported for the outcomes, were included in the qualitative synthesis but excluded from the quantitative synthesis. Study quality was evaluated using the Newcastle-Ottawa scale (NOS). The risk of bias was estimated using the Cochrane RoBANS risk of bias tool. Results: We retrieved 7213 citations using the search strategy and 2097 citations were excluded based on the screening of the title and abstract. Full text was retrieved for 40 articles and subjected to the eligibility criteria, of which 28 were excluded from the study. Twelve studies were eligible for the synthesis of the systematic review while seven studies qualified for the meta-analysis. The median score of the articles was 8/9 as per NOS. The risk of selection bias was low in all the studies while the risk of detection bias was high in all except one study. Ten studies were conducted on adult patients, while two studies reported pediatric TBI. A meta-analysis for GOS showed that high NLR predicted unfavorable outcomes at ≥6 months with a mean difference of −5.18 (95% confidence interval: −10.04, −0.32); P = 0.04; heterogeneity (I 2 ), being 98%. The effect estimates for NLR and mortality were a mean difference of −3.22 (95% confidence interval: −7.12, 0.68), P = 0.11, and an I 2 of 85%. Meta-analysis for Area under the curve (AUC) receiver operating characteristic of the included studies showed good predictive power of NLR in predicting outcomes following TBI with AUC 0.706 (95% CI: 0.582–0.829). Conclusion: A higher admission NLR predicts an increased mortality risk and unfavorable outcomes following TBI. However, future research will likely address the existing gaps.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,011 | 0,005 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».