Division of Medical Genetics and Genomic Medicine, Vanderbilt University Medical Center, Nashville, TN.
Vanderbilt University Medical Center, Department of Medicine, Nashville, TN; Vanderbilt University Medical Center, Department of Biomedical Informatics, Nashville, TN.
Genet Med. 2023 Oct;25(10):100921. doi: 10.1016/j.gim.2023.100921. Epub 2023 Jun 17.
To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR).
We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders.
We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results.
Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.
在电子健康记录（EHR）中调查孟德尔疾病在诊断轨迹中的表型表现。
我们应用一个概念模型来描绘孟德尔疾病的诊断轨迹，将其应用于受 9 种孟德尔疾病之一影响的患者的 EHR 中。我们使用表型风险评分评估了诊断轨迹中的数据可用性和表型确定情况，并通过遗传性结缔组织疾病患者的图表审查验证了我们的发现。
我们确定了 896 名具有基因证实诊断的个体，其中 216 名（24%）具有完全确定的诊断轨迹。表型风险评分在临床怀疑和诊断后增加（P < 1×10，Wilcoxon 秩和检验）。我们发现，在 EHR 中的所有基于国际疾病分类的表型中，有 66%是在临床怀疑后记录的，并且手动图表审查得出了一致的结果。
我们使用一种新的概念模型来研究 EHR 中遗传疾病的诊断轨迹，结果表明，表型确定在很大程度上是由临床怀疑引发的对遗传疾病的临床检查和研究驱动的，我们将这一过程称为诊断趋同。用于检测未诊断的遗传疾病的算法应考虑在首次临床怀疑日期处对 EHR 数据进行删失，以避免数据泄露。
