Mapping Global Prevalence, Treatment Capacity, and Expertise for Retinoblastoma
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Notice bibliographique
Résumé
Abstract 68 Purpose Retinoblastoma (childhood eye cancer) is curable, but outcomes remain poor in low- and-middle-income countries (LMICs). Optimal resources and expertise for the management of retinoblastoma have been outlined in published clinical guidelines and serve as a guide to inform health policy at national, regional, and institutional levels. We conducted a situational analysis of resources and expertise that are available at key retinoblastoma treatment centers worldwide in an attempt to inform systems of patient referral and educational capacity initiatives, as well as enhance patient care. Methods We conducted an online survey of global retinoblastoma treatment centers to identify and document expertise and resources that are available for the care of children with retinoblastoma worldwide. Prevalence of retinoblastoma was calculated for each country on the basis of a published incidence of 1:16,000 live births and was compared with patient numbers reported from each treatment center. An online platform was developed using ESRI ArcGIS software (Redlands, CA) to disseminate this information in an interactive and data-rich format ( www.1rbw.org ). Results We documented information from 178 retinoblastoma treatment centers in 76 countries: 14 treatment centers (8%) from low-income (LIC), 97 treatment centers (54%) from middle-income (MIC), and 67 treatment centers (38%) from high-income countries (HICs). Six genetic testing laboratories provided information on their retinoblastoma test sensitivity and specificity. An estimated 1,421, 6,545, and 784 new patients are expected each year in LICs, MICs, and HICs, respectively. On the basis of the numbers of new patients that are reportedly managed by each treatment center, the map documents 41% of the global retinoblastoma patient burden (3,573 of 8,750 patients). This represents 10% (139 of 1,421) of patients in LICs, 42% (2,743 of 6,545) of patients in MICs, and 88% (691 of 784) of patients in HICs. Capacity for enucleation was available at 100% of centers in HICs and in 99% of centers in LMICs. Pathology was reported to be available in 100% of HIC centers and in 97% of LMIC centers. Imaging by two-dimensional ultrasound was widely available (98% in HICs v 93% in LMICs), but RetCam (98% in HICs v 68% in LMICs) and ultrasound biomicroscopy (75% in HICs v 35% in LMICs) was largely centered in HICs. Focal therapy modalities were available in 98% of HIC centers and in 83% of LMIC centers. Systemic chemotherapy was available in 98% of HIC centers and in 80% of LMIC centers. Intra-arterial chemotherapy was available in 73% of HIC centers and in 31% of LMIC centers, and intravitreal chemotherapy in 84% of HIC centers and 42% of LMIC centers. Radiotherapy was available in 93% of HIC centers and in 62% of LMIC centers. Ophthalmologists were part of the team in 100% of HIC centers and in 97% of LMIC centers. Oncologists were part of the managing team in 100% of HIC centers and in 79% of LMIC centers. Genetic counselors were available in 95% of HIC centers and in 39% of LMIC centers. Conclusion The One Retinoblastoma World map connects stakeholders and strengthens the capacity to care for the global retinoblastoma population. Knowledge of where and how children with retinoblastoma are managed worldwide provides an efficient and rapid path for parents to access urgent care. Estimated incidence versus the location and capabilities of treatment centers reveals opportunities to increase capacity, collaboration, and coverage in various regions. This first-of its-kind collaboration promotes global standards of care, setting the stage for multicenter clinical trials and other research, thereby accelerating the translation of results from the laboratory to the clinic. AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST No COIs from the authors.
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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) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 découle