Impacto da pandemia da COVID-19 no diagnóstico, manejo e desfechos no câncer de ovário: uma revisão sistemática e meta-análise
Notice bibliographique
Résumé
The COVID-19 pandemic has imposed unprecedented challenges on the global healthcare system, particularly impacting the diagnosis and treatment of ovarian cancer, known as the most lethal gynecological malignancy neoplasm in the world.With approximately 75% of patients diagnosed at advanced stages, understanding the repercussions of the pandemic on the care continuum and outcomes of malignant ovarian tumors is essential.This systematic review and meta-analysis aimed to synthesize the scientific evidence on the effects of the COVID-19 pandemic on diagnosis, management, and outcomes of this disease.This study followed the PRISMA-2020 statement and the COSMOS-E guidance.We searched PubMed, EMBASE, Web of Science, and CINAHL databases up to December 31, 2023.The Cochrane Risk of Bias Assessment Tool and the Newcastle-Ottawa Scale (NOS) were used to assess each study.We conducted qualitative and quantitative syntheses, using the R software (version 4.3.2) for meta-analyses.These relative risks (RRs) for oncological staging and therapeutic modification rate by fixed and random effects models.This study protocol is registered in the International Prospective Register of Systematic Reviews (PROSPERO), number CRD42021289875.We included 25 studies encompassing 9,699 cases during the pandemic (January 1, 2020 to December 31, 2021) compared to 14,847 pre-pandemic cases in 22 studies (January 1, 2013 to March 17, 2020).Nine studies, comprising 65% of the total sample of ovarian cancer cases, reported a decrease in diagnoses (-9.7%; range: -56.2% to -1.9%), while ten studies observed an increase (9.5%; range: 0.8% to 245%).The staging meta-analysis identified a significant increase of 23% in the probability of FIGO IV stages at diagnosis in the pandemic (RR=1.23;95% CI 1.02 to 1.48).Furthermore, 16 studies highlighted substantial variations in therapeutic approaches and modifications, ranging from -48% to 400% in surgeries, -18% to 104% in neoadjuvant chemotherapy, and 0% to 104% in cancellations, postponements, and therapeutic plan changes, demonstrating the comprehensive repercussion of the COVID-19 pandemic on the complexity of care for these tumors.The meta-analysis of six studies with low risk of bias (NOS 8-9) revealed a significant change in the therapeutic plan from primary cytoreductive surgery to neoadjuvant chemotherapy (N=991) compared to the pre-pandemic period (N=1,550).The results correspond to a significant increase of 10% in the probability of a change in the therapeutic plan in favor of neoadjuvant chemotherapy due to the pandemic (RR=1.10;95% CI 1.03 to 1.18) in the fixed-effects model, with no heterogeneity (I 2 =0%; <0.0001;p=0.51) between studies.The combined data are consistent with the recommendations of specialized societies and highlight the adaptation of clinical practices to the limitations imposed by the global health crisis.Despite these alterations, no significant negative impact on time to primary treatment and short-term outcomes was observed, although one study reported a reduction in the efficacy of surgical debulking in more complex and advanced cases compared to the prepandemic period.The COVID-19 pandemic has significantly impacted the diagnosis and management of ovarian cancer, resulting in disparities in case proportions and treatment preferences.Notably, the trend towards the diagnosis of metastatic cases and the adoption of neoadjuvant chemotherapy.These findings underscore the urgent necessity for adaptive treatment strategies in response to crisis scenarios.Prospective studies are essential to assess the impact of the COVID-19 pandemic on long-term outcomes.
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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 | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,102 | 0,008 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».