Gallagher Emily R, Chow Penny, Mills Maria R, Perry Hazel, Tam Allison C, Rosenbluth Glenn, Gutierrez Yvonne R, Shamshoni Jessica Kianmahd, Matthews Marisa, Schweitzer Daniela N, Hing Anne
Seattle Children's Hospital, Seattle, WA, USA.
University of California San Francisco, San Francisco, CA, USA.
Cleft Palate Craniofac J. 2025 Oct;62(10):1764-1773. doi: 10.1177/10556656241276857. Epub 2024 Aug 18.
ObjectiveTo develop consensus-based algorithms for genetic testing in patients with common craniofacial conditions.DesignAn online collaborative consisting of online meetings, independent work, and feedback across groups. A collaborative of genetics and pediatrics providers from three regional craniofacial centers (four institutions).MethodsCollaborative participants agreed upon a shared initial framework, developed algorithms independently, and presented/tested the algorithms with a national audience. Algorithms were modified based on consensus feedback.ResultsThe collaborative group developed final algorithms for genetic testing in patients with orofacial cleft, branchial arch conditions, and craniosynostosis.ConclusionsTimely and accurate diagnosis of genetic conditions can support medical management recommendations that result in safer surgical interventions. Algorithms can help guide best-practices for testing, particularly in institutions without easy access to genetics providers.
目的
制定基于共识的常见颅面部疾病患者基因检测算法。
设计
一项在线协作项目,包括在线会议、独立工作以及跨组反馈。由来自三个地区颅面部中心(四个机构)的遗传学和儿科医疗服务提供者组成的协作团队。
方法
协作参与者商定了一个共享的初始框架,独立开发算法,并向全国受众展示/测试这些算法。根据共识反馈对算法进行修改。
结果
协作小组制定了唇腭裂、鳃弓疾病和颅缝早闭患者基因检测的最终算法。
结论
及时准确地诊断基因疾病有助于支持医疗管理建议,从而实现更安全的手术干预。算法有助于指导检测的最佳实践,特别是在难以获得遗传学医疗服务提供者的机构中。