美国范德比尔特大学医学中心Douglas M. Ruderfer及其小组的研究发现利用临床数据中的表型特征使系统性对患者进行基因检测成为可能。2021年6月3日出版的《自然-医学》发表了这项成果。

在本研究中，研究人员利用接受染色体微阵测试的 2,286 名患者和 9,144 名匹配的对照电子健康记录 (EHR) 的纵向临床数据诊断账单信息来构建模型以预测应该接受基因测试的个体。将该模型应用于一个独立的医院系统（AUROC，0.95），研究发现在保留的测试样本（临床应用性能曲线下面积  (AUROC) 0.97；精确召回曲线下面积 (AUPRC)，0.92）中实现了高预测精度; （AUPRC, 0.62)，并且在一个由 172,265 名患者组成的独立组中，这些病例被认为与遗传学提供的数据有关联（AUROC，0.9；AUPRC，0.63）。

该模型还可以准确识别携带假定致病性拷贝数变异的患者。与目前的基因检测方法相比，该模型可以对更多的患者进行检测，同时还增加了患有遗传疾病患者的检出比例。研究证明，可以从 EHR 中获得具有遗传疾病的表型模式，以使基因检测系统化，具有加快诊断、改善护理和降低成本的潜力。

据悉，有大约 5% 的人口受到罕见遗传病的影响，在接受基因检测之前大多数人要忍受多年的不确定性。遗传疾病的一个共同特征是具有多通常跨器官系统的罕见表型。

附：英文原文

Title: Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing

Author: Theodore J. Morley, Lide Han, Victor M. Castro, Jonathan Morra, Roy H. Perlis, Nancy J. Cox, Lisa Bastarache, Douglas M. Ruderfer

Issue&Volume: 2021-06-03

Abstract: Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision–recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs.

DOI: 10.1038/s41591-021-01356-z

Source: https://www.nature.com/articles/s41591-021-01356-z