Systemic inflammation response index as a clinical outcome evaluating tool and prognostic indicator for hospitalized stroke patients: a systematic review and meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Stroke, which is the main element of cerebrovascular disease (CVD), has become the foremost reason for death and disability on a global scale. The systemic inflammation response index (SIRI), a newly developed and comprehensive indicator, has demonstrated promise in forecasting clinical results for diverse ailments. Nevertheless, the uncertainty surrounding the assessment and prediction of clinical outcomes for stroke patients by SIRI persists, and the conflicting findings from the limited studies conducted on this matter further complicate the situation. Consequently, we performed a thorough systematic review and meta-analysis to explore the correlation between SIRI and the clinical results in individuals suffering from stroke. METHODS: This research was registered in PROSPERO and carried out following the PRISMA guidelines. A thorough investigation was carried out on PubMed, Embase, the Cochrane Library, Web of Science, and Scopus databases. Furthermore, we conducted a manual search in Chinese databases, such as China national Knowledge Infrastructure (CNKI), WanFang, VIP, and China Biology Medicine (CBM). We assessed the potential for bias in the studies included by utilizing the Newcastle-Ottawa Scale (NOS) tool. Adverse clinical outcomes were the main focus of the study, with secondary endpoints including mortality, the predictive value of SIRI, SIRI values across various endpoints, and clinical parameters associated with subarachnoid hemorrhage (SAH) in relation to low and high SIRI group. RESULTS: Following rigorous evaluation, a grand total of 22 investigations, encompassing a populace of 12,737 individuals, were considered suitable for incorporation in the final analysis. The findings from our meta-analysis indicate a strong and consistent correlation between elevated SIRI levels and adverse functional outcomes, irrespective of the method used to evaluate unfavorable outcomes. Furthermore, increased SIRI values have a strong correlation with mortality rates in both the short and long term. Besides, SIRI is a useful indicator of the severity of SAH. SIRI demonstrates strong predictive ability in identifying unfavorable outcomes and stroke-related pneumonia (SAP), as higher SIRI values are typically linked to negative endpoints. Nevertheless, the meta-analysis indicated that there was no significant increase in the risk of early neurological deterioration (END) and acute hydrocephalus (AHC) in high SIRI group when comparing to low SIRI. CONCLUSION: This study could potentially pave the way for groundbreaking insights into the relationship between SIRI and stroke patient outcomes, as it appears to be the first meta-analysis to explore this association. Given the critical role of the inflammatory response in stroke recovery, closely monitoring patients with high SIRI levels could represent a promising strategy for mitigating brain damage post-stroke. Thus, further investigation into SIRI and its impact on clinical outcomes is essential. While our initial findings offer valuable insights into this area, continued research is necessary to fully elucidate the potential of SIRI, ideally through dynamic monitoring and large-scale, multi-center studies. Ultimately, this research has the potential to inform clinical decision-making and improve patient outcomes following stroke. SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/ ; Identifier CRD42023405221.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,021 | 0,037 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».