癫痫发作的新生儿脑病是一种表现,其中快速全基因组测序(rWGS)已显示出临床实用性和改善的结果。我们报告了一名新生儿,他在生命的第三天出现了对抗癫痫药物,神经系统和计算机断层扫描结果难以治疗的癫痫发作,并伴有严重的全身性脑肿胀。rWGS揭示了钼辅因子合成基因中的复合杂合变体,1A型(MOCS1c.*7+5G>A和c.377G>A);出生当天钼辅因子缺乏症的临时诊断4。用于静脉内替代MOCS1产品的紧急研究性新药申请,环状吡喃蝶呤单磷酸盐,被考虑,但鉴于疾病的严重程度和治疗开始的延迟,感到不合适。尽管在生命的第一周内进行了精确的分子诊断,但患者在生命的第9天死亡。这种情况表明,在生命的第一周内进行基于rWGS的分子诊断可能不足以改善结果。然而,它确实为复苏和预测长期结局的临床决策提供了依据.我们建议,为了实现发病率和死亡率的最佳降低,rWGS必须在全面的快速精准医疗系统(CRPM)中实施。类似于新生儿筛查(NBS),CRPM将加入,诊断,以及为响应患者和父母的需求而开发的精准医疗实施组件。在学习模型中对医疗保健提供者进行教育,在该模型中,正在进行的数据分析告知系统改进对于CRPM的最佳有效性至关重要。
Neonatal encephalopathy with seizures is a presentation in which rapid whole-genome sequencing (rWGS) has shown clinical utility and improved outcomes. We report a neonate who presented on the third day of life with seizures refractory to antiepileptic medications and neurologic and computerized tomographic findings consistent with severe generalized brain swelling. rWGS revealed compound heterozygous variants in the molybdenum cofactor synthesis gene, type 1A (MOCS1 c.*7 + 5G > A and c.377G > A); a provisional diagnosis of molybdenum cofactor deficiency on day of life 4. An emergency investigational new drug application for intravenous replacement of the MOCS1 product, cyclic pyranopterin monophosphate, was considered, but felt unsuitable in light of the severity of disease and delay in the start of treatment. The patient died on day of life 9 despite having a precise molecular diagnosis within the first week of life. This case illustrates that an rWGS-based molecular diagnosis within the first week of life may be insufficient to improve outcomes. However, it did inform clinical decision-making with regard to resuscitation and predicted long-term outcome. We suggest that to achieve optimal reductions in morbidity and mortality, rWGS must be implemented within a comprehensive rapid precision medicine system (CRPM). Akin to newborn screening (NBS), CRPM will have onboarding, diagnosis, and precision medicine implementation components developed in response to patient and parental needs. Education of health-care providers in a learning model in which ongoing data analyses informs system improvement will be essential for optimal effectiveness of CRPM.