Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.
Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation.
复制标题
DOI：
10.1126/scitranslmed.aat6177
复制
发表时间：
2019-04-24
影响因子：
17.1
通讯作者：
中科院分区：
文献类型：
--
作者：
通过及时进行有针对性的治疗，快速全基因组测序可以改善患有遗传病的重病儿童的结局，特别是新生儿和儿科重症监护病房(ICU)中的婴儿。然而，需要高素质的专业人员来解读结果，这就排除了广泛实施的可能性。我们描述了一个人口规模的、带有自动表型和解释的遗传病临时诊断平台。通过直接从血液样本中制备基于珠子的基因组文库和在15.5小时内对成对的100个核苷酸片段进行测序，加快了基因组测序。临床自然语言处理(CNLP)从电子健康记录中自动提取儿童深层表现体，准确率为80%，召回率为93%。在101名105种遗传性疾病的儿童中，平均4.3个CNLP提取的表型特征与这些疾病的预期表型特征匹配，而手动解释中使用的表型特征匹配0.9个。我们通过结合患者的CNLP表型与所有遗传疾病的预期表型特征的相似性排名，以及患者所有基因组变异的致病性排名，实现了临时诊断的自动化。自动化、回溯性诊断与专家手动解释很好地一致(在95名患有97种遗传病的儿童中，97%的召回率和99%的准确性)。前瞻性地，我们的平台正确诊断了7名重症ICU婴儿中的3名(100%准确率和召回率)，平均节省时间22：19小时。在每一个病例中，诊断都会影响治疗。基因组测序和自动表型鉴定和解释的中位数为20：10小时，可能会增加ICU的采用率，从而及时实施精确的治疗。自动表型和快速全基因组测序的解释改进了住院儿童遗传病的诊断时间。一条简化的基因诊断管道在治疗重症儿童时，时间至关重要。Clark等人的研究成果。建立了一条自动管道来分析EHR数据和来自干血点的基因组测序数据，以提供对住院的、通常是危重疾病的疑似遗传病儿童的潜在诊断。他们的管道只需要最少的用户干预，提高了可用性，缩短了诊断时间，在不到24小时的中位时间内交付了临时发现。尽管这条管道需要调整以用于不同的医院系统，但这样的自动化工具可以帮助临床医生加快准确的遗传病诊断，潜在地加速患者护理的救命变化。
By informing timely targeted treatments, rapid whole-genome sequencing can improve the outcomes of seriously ill children with genetic diseases, particularly infants in neonatal and pediatric intensive care units (ICUs). The need for highly qualified professionals to decipher results, however, precludes widespread implementation. We describe a platform for population-scale, provisional diagnosis of genetic diseases with automated phenotyping and interpretation. Genome sequencing was expedited by bead-based genome library preparation directly from blood samples and sequencing of paired 100-nt reads in 15.5 hours. Clinical natural language processing (CNLP) automatically extracted children’s deep phenomes from electronic health records with 80% precision and 93% recall. In 101 children with 105 genetic diseases, a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of those diseases, compared with a match of 0.9 phenotypic features used in manual interpretation. We automated provisional diagnosis by combining the ranking of the similarity of a patient’s CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all of the patient’s genomic variants. Automated, retrospective diagnoses concurred well with expert manual interpretation (97% recall and 99% precision in 95 children with 97 genetic diseases). Prospectively, our platform correctly diagnosed three of seven seriously ill ICU infants (100% precision and recall) with a mean time saving of 22:19 hours. In each case, the diagnosis affected treatment. Genome sequencing with automated phenotyping and interpretation in a median of 20:10 hours may increase adoption in ICUs and, thereby, timely implementation of precise treatments. Automated phenotyping and interpretation of rapid whole-genome sequencing improve time to diagnosis of genetic diseases in hospitalized children. A streamlined genetic diagnosis pipeline When treating seriously ill children, time is of the essence. Clark et al. built an automated pipeline to analyze EHR data and genome sequencing data from dried blood spots to deliver a potential diagnosis for hospitalized, often critically ill, children with suspected genetic diseases. Their pipeline required minimal user intervention, increasing usability and shortening time to diagnosis, delivering a provisional finding in a median time of less than 24 hours. Although this pipeline would need to be adapted for use at different hospital systems, such an automated tool could aid clinicians to expedite an accurate genetic disease diagnosis, potentially hastening lifesaving changes to patient care.
登录
查看更多内容
影响因子：
3.8
作者：
Chen DY;Chowdhury S;Farnaes L;Friedman JR;Honold J;Dimmock DP;Gold OBOTRIJJ
通讯作者：
Gold OBOTRIJJ
影响因子：
2.9
作者：
Berry, M. A.;Shah, P. S.;Hellmann, J.
通讯作者：
Hellmann, J.
DOI：
10.15585/mmwr.mm6602a1
发表时间：
2017-01-20
期刊：
MMWR. Morbidity and mortality weekly report
影响因子：
--
作者：
Arth AC;Tinker SC;Simeone RM;Ailes EC;Cragan JD;Grosse SD
通讯作者：
Grosse SD
影响因子：
5.3
作者：
Clark, Michelle M.;Starke, Zornitza;Kingsmore, Stephen F.
通讯作者：
Kingsmore, Stephen F.
影响因子：
1.9
作者：
Dhombres F;Bodenreider O
通讯作者：
Bodenreider O