Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease
Improving the phenotype risk score as a scalable approach to identifying patients with Mendelian disease
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DOI：
10.1093/jamia/ocz179
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发表时间：
2019-12-01
影响因子：
6.4
通讯作者：
Denny, Joshua C.
中科院分区：
文献类型：
--
作者：
Bastarache, Lisa;Hughey, Jacob J.;Denny, Joshua C.
目的：表型风险评分（PheRS）是一种利用电子健康记录（EHR）中的表型检测孟德尔疾病模式的方法。我们比较了不同的方法映射EHR表型Mendelian disease features.Materials and Methods的性能：PheRS利用孟德尔疾病的描述注释与人类表型本体论（HPO）条款。在以前的工作中，我们提出了一个地图连接phecodes（基于国际疾病分类[ICD]-第九次修订版）HPO条款。在本研究中，我们整合了ICD-第十版代码和实验室数据。我们还使用ICD代码的自定义分组创建了HPO术语之间的新映射。我们使用250万份去识别的医疗记录比较了16种孟德尔疾病的病例和对照的性能。结果：PheRS有效地区分了所有15种阳性对照和所有测试方法的病例和对照（P < 4 x 10（16））。增加实验室数据导致14种疾病中有4种的统计学显著改善。自定义ICD分组提高了特异性，导致100的精确度平均提高8%（-2%至22%）。10名接受测试的囊性纤维化成人中有8名在诊断前的第95百分位数中有PheRS。讨论：phecodes和自定义ICD分组都能够检测到受影响病例和对照组之间在人群水平上的差异。ICD地图显示得分最高的个体具有更好的精确度。添加实验室数据提高了检测人口水平differentiation.Conclusions的性能：PheRS是一种可扩展的方法，研究孟德尔疾病在人口水平上使用电子健康记录数据，并可能被用来寻找未确诊的孟德尔疾病的患者。
Objective: The Phenotype Risk Score (PheRS) is a method to detect Mendelian disease patterns using phenotypes from the electronic health record (EHR). We compared the performance of different approaches mapping EHR phenotypes to Mendelian disease features.Materials and Methods: PheRS utilizes Mendelian diseases descriptions annotated with Human Phenotype Ontology (HPO) terms. In previous work, we presented a map linking phecodes (based on International Classification of Diseases [ICD]-Ninth Revision) to HPO terms. For this study, we integrated ICD-Tenth Revision codes and lab data. We also created a new map between HPO terms using customized groupings of ICD codes. We compared the performance with cases and controls for 16 Mendelian diseases using 2.5 million de-identified medical records.Results: PheRS effectively distinguished cases from controls for all 15 positive controls and all approaches tested (P < 4 x 10(16)). Adding lab data led to a statistically significant improvement for 4 of 14 diseases. The custom ICD groupings improved specificity, leading to an average 8% increase for precision at 100 (-2% to 22%). Eight of 10 adults with cystic fibrosis tested had PheRS in the 95th percentile prio to diagnosis.Discussion: Both phecodes and custom ICD groupings were able to detect differences between affected cases and controls at the population level. The ICD map showed better precision for the highest scoring individuals. Adding lab data improved performance at detecting population-level differences.Conclusions: PheRS is a scalable method to study Mendelian disease at the population level using electronic health record data and can potentially be used to find patients with undiagnosed Mendelian disease.