Department of Biomedical Informatics, Harvard University, Boston, Massachusetts.
Department of Computer Science, University of Massachusetts, Lowell.
JAMA Netw Open. 2021 Feb 1;4(2):e2036220. doi: 10.1001/jamanetworkopen.2020.36220.
The Undiagnosed Diseases Network (UDN) is a national network that evaluates individual patients whose signs and symptoms have been refractory to diagnosis. Providing reliable estimates of admission outcomes may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases.
To develop computational models that effectively predict admission outcomes for applicants seeking UDN evaluation and to rank the applications based on the likelihood of patient admission to the UDN.
DESIGN, SETTING, AND PARTICIPANTS: This prognostic study included all applications submitted to the UDN from July 2014 to June 2019, with 1209 applications accepted and 1212 applications not accepted. The main inclusion criterion was an undiagnosed condition despite thorough evaluation by a health care professional; the main exclusion criteria were a diagnosis that explained the objective findings or a review of the records that suggested a diagnosis. A classifier was trained using information extracted from application forms, referral letters from health care professionals, and semantic similarity between referral letters and textual description of known mendelian disorders. The admission labels were provided by the case review committee of the UDN. In addition to retrospective analysis, the classifier was prospectively tested on another 288 applications that were not evaluated at the time of classifier development.
The primary outcomes were whether a patient was accepted or not accepted to the UDN and application order ranked based on likelihood of admission. The performance of the classifier was assessed by comparing its predictions against the UDN admission outcomes and by measuring improvement in the mean processing time for accepted applications.
The best classifier obtained sensitivity of 0.843, specificity of 0.738, and area under the receiver operating characteristic curve of 0.844 for predicting admission outcomes among 1212 accepted and 1210 not accepted applications. In addition, the classifier can decrease the current mean (SD) UDN processing time for accepted applications from 3.29 (3.17) months to 1.05 (3.82) months (68% improvement) by ordering applications based on their likelihood of acceptance.
A classification system was developed that may assist clinical evaluators to distinguish, prioritize, and accelerate admission to the UDN for patients with undiagnosed diseases. Accelerating the admission process may improve the diagnostic journeys for these patients and serve as a model for partial automation of triaging or referral for other resource-constrained applications. Such classification models make explicit some of the considerations that currently inform the use of whole-genome sequencing for undiagnosed disease and thereby invite a broader discussion in the clinical genetics community.
未确诊疾病网络（UDN）是一个全国性的网络，用于评估那些症状和体征难以确诊的个体患者。提供可靠的入院结果估计可能有助于临床评估人员区分、优先考虑并加快患有未确诊疾病的患者进入 UDN。
开发能够有效预测申请 UDN 评估的患者入院结果的计算模型，并根据患者进入 UDN 的可能性对申请进行排名。
设计、环境和参与者：这项预后研究纳入了 2014 年 7 月至 2019 年 6 月期间向 UDN 提交的所有申请，共纳入 1209 份接受申请和 1212 份不接受申请。主要纳入标准是尽管经过医疗保健专业人员的全面评估仍未确诊的疾病；主要排除标准是诊断可以解释客观发现或审查记录提示诊断。使用从申请表中提取的信息、医疗保健专业人员的转诊信以及转诊信与已知孟德尔疾病文本描述之间的语义相似性来训练分类器。入院标签由 UDN 的病例审查委员会提供。除了回顾性分析外，该分类器还前瞻性地测试了另外 288 份在分类器开发时未进行评估的申请。
主要结果是患者是否被 UDN 接受或拒绝，以及根据入院可能性对申请进行排名。通过将预测结果与 UDN 入院结果进行比较，并通过衡量接受申请的平均处理时间的改善来评估分类器的性能。
在 1212 份接受申请和 1210 份不接受申请中，最佳分类器预测入院结果的敏感性为 0.843，特异性为 0.738，受试者工作特征曲线下面积为 0.844。此外，通过根据接受申请的可能性对申请进行排序，该分类器可将当前 UDN 接受申请的平均（SD）处理时间从 3.29（3.17）个月减少到 1.05（3.82）个月（提高 68%）。
开发了一种分类系统，可帮助临床评估人员区分、优先考虑并加快患有未确诊疾病的患者进入 UDN。加快入院流程可以改善这些患者的诊断之旅，并为其他资源受限的申请的分诊或转诊的部分自动化提供模型。这种分类模型明确了一些目前用于未确诊疾病的全基因组测序的考虑因素，从而在临床遗传学社区引发了更广泛的讨论。
