Efficacy analysis of neuroprotective drugs in patients with acute ischemic stroke based on network meta-analysis
Notice bibliographique
Résumé
Objective: This network meta-analysis aims to explore the efficacy and safety of neuroprotective agents in patients with ischemic stroke and attempts to identify which drug is the most effective in improving outcomes for patients with acute ischemic stroke (AIS) through a ranking method. Methods: We comprehensively searched the PubMed, Medline, Embase, Web of Science, and Cochrane library databases from their establishment to 30 June 2024. Data were extracted from the studies identified, and their quality was assessed using the Cochrane risk-of-bias tool or the Newcastle-Ottawa Scale (NOS). The outcome measures were for a favorable prognosis, based on the modified Rankin Scale score (mRS) or National Institutes of Health Stroker Scale (NIHSS) score, mortality, and adverse effect with different drug regimens. We utilized Stata version 16.0 and Review Manager (RevMan) version 5.3.0 for statistical analysis. Results: A total of 35 studies were included: 25 randomized control trials, eight retrospective studies, and two prospective studies. The total sample size was 18,423 cases and included nine interventions: citicoline, edaravone (EDV), edaravone dexborneol, cinepazide maleate, cerebrolysin, minocycline, ginkgolide, ginkgo diterpene lactone meglumine (GDLM), and conventional (CON) treatment. Our analysis revealed that, except for edaravone dexborneol, the ginkgolide, EDV, cinepazide maleate, citicoline, cerebrolysin, minocycline, and GDLM treatment schemes reduced the mortality of patients with AIS compared with CON. Each drug regimen significantly improved the neural function of these patients compared with CON, which from highest to lowest was citicoline + vinpocetine, GDLM, citicoline, edaravone dexborneol, cinepazide maleate, ginkgolide, EDV, and CON. Moreover, we also found that, except for citicoline, the ginkgolide, EDV, edaravone dexborneol, GDLM, and cinepazide maleate treatment schemes had a high total treatment effective rate in these patients, the order from highest to lowest being ginkgolide, EDV, edaravone dexborneol, GDLM, cinepazide maleate, CON, and citicoline. In terms of the ineffective rate, we found that, compared with CON, the edaravone dexborneol, EDV, citicoline, GDLM, ginkgolide, and cinepazide maleate treatment schemes all had a lower ineffective rate. Finally, our analysis revealed that, except for cinepazide maleate and ginkgolide, the EDV, minocycline, edaravone dexborneol, GDLM, citicoline, and cerebrolysin schemes all had a higher rate of adverse effect on patients compared to CON. Based on the impact of the adverse effect with different surgical interventions, we further analyzed the effect of these drug treatments by the total treatment effective rate combined with adverse effect, revealing that EDV, ginkgolide, and edaravone dexborneol were the safest and most effective treatments. Conclusion: In patients with AIS, ginkgolide, EDV, cinepazide maleate, citicoline, cerebrolysin, minocycline, and GDLM were associated with a reduction in mortality rate. Moreover, ginkgolide, EDV, edaravone dexborneol, and GDLM treatment schemes revealed not only a high total treatment effective rate but also a low rate of treatment inefficacy. When considering the combination of the total treatment effective rate with adverse effect, EDV, ginkgolide, and edaravone dexborneol were revealed as the safest and most effective.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,006 | 0,003 |
| Bibliométrie | 0,003 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».