Bevacizumab and conventional chemotherapy for the treatment of epithelial ovarian cancer: a systematic review of the literature, best evidence synthesis and metanalysis
Notice bibliographique
Résumé
Ovarian Cancer (OC) is the eighth most commonly diagnosed cancer amongst Canadian women and has the highest mortality rates, to date. The standard of care for OC is mainly comprised of cytoreduction followed by platinum-based chemotherapy. Although initial response to standard of care is met with favorable outcomes, long term clinical outcomes such as overall and progression free survival show modest improvements. There is an unmet need to further investigate other treatment modalities for OC patients.Bevacizumab (BEV) is an anti-angiogenesis medication approved by the FDA in 2018 as a first-line therapy in OC patients. Bevacizumab may be beneficial alongside other standards of care however, the magnitude of the benefit of Bevacizumab in OC has not been well documented.The objective of this study was to conduct a systematic literature review, best evidence synthesis, and a meta-analysis of randomized controlled trials evaluating progression free survival (PFS), overall survival (OS), objective response rate (ORR), as well as safety and tolerability in OC patients treated with BEV compared to an active control (AC; platinum-based chemotherapy regimen).We anticipated that the addition of BEV compared to conventional chemotherapy regimens alone may improve clinical outcome measures such as PFS, OS, ORR as well safety and tolerability in OC patients.This review was conducted according to PRISMA guidelines. Pubmed, MEDLINE, and EMBASE were searched (via OVID) for studies published after 2018 that evaluated the addition of BEV for the treatment of OC. Articles were selected based on the following: clinical trial, original research, full publication, while abstract, case reports, and posters were excluded. Data available for clinical outcomes, incidence of adverse events, patient characteristics and disease parameters were retained. A total of 7 meta-analyses were performed comparing the PFS (months), OS (months), ORR, Complete Response (CR), Partial Response (PR), incidence of serious adverse events and grade ≥3 AEs between BEV and AC groups. The quality of the evidence was evaluated using the Cochrane Risk of Bias tool for randomized trials (ROB 2). The inverse variance of mean differences, odds ratios (ORs) and their 95% confidence intervals (CI) were calculated using random-effects models.Of the 2869 database results screened, 106 full-text articles were assessed for eligibility and 13 were considered for the qualitative analysis. Out of 2316 patients, 1159 received BEV and 1157 received an AC. Between 5 and 8 articles were included in the meta-analyses evaluating PFS, OS, ORR, CR, PR, SAEs, and grade ≥3 AEs, respectively. Significantly longer mean PFS was observed in the BEV group (n = 1125; 10.7 months) compared to patients treated with an AC (n = 1123; 7.9; mean difference (MD): 2.91; 95% CI: 2.14-3.68, p < 0.00001; I2 = 87%). Significantly longer mean OS was observed in the BEV group (n = 1076; 21.6 months) compared to patients treated with an AC (n = 1075; 17.4; MD: 3.92; 95% CI: 2.11-5.73, p < 0.0001; I2 = 90%). An objective response was reported for 64.2% (274/427) of patients in the BEV group and 39.3% (172/438) of patients in the AC group (OR: 3.29, 95%CI: 2.42-4.45, p < 0.00001, I2 = 0%). Significantly more patients experienced a SAE (59.8% [370/944]) in the BEV group compared to the AC group (31.7% [299/942]; OR: 1.41; 95%CI: 1.16-1.71, p = 0.0005, I2 = 0%). The proportion of patients experiencing grade ≥3 AEs following BEV administration was 49.3% (201/408) compared to the AC group (39.7% [160/403]; OR: 1.68; 95%CI: 0.83-3.37; p = 0.15; I2 = 76%).The results of the study demonstrated that BEV administration resulted in improved clinical outcomes such as longer PFS, OS and greater objective response rates. Safety with respect to incidence of serious adverse events and grade ≥3 adverse events were more common among patients in the BEV group compared to the AC group
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 | 0,013 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,015 | 0,022 |
| 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) | 0,005 | 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 ».