Papanastasiou Giorgos, Yang Guang, Fotiadis Dimitris I, Dikaios Nikolaos, Wang Chengjia, Huda Ahsan, Sobolevsky Luba, Raasch Jason, Perez Elena, Sidhu Gurinder, Palumbo Donna
Pfizer Inc, New York, NY, USA.
National Heart and Lung Institute, Imperial College London, London, UK.
Commun Med (Lond). 2023 Dec 20;3(1):189. doi: 10.1038/s43856-023-00412-8.
Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality.
We developed a deep learning-based method (named "TabMLPNet") to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls.
The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID.
We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level.
原发性免疫缺陷（PI）是一组由免疫系统缺陷导致的异质性疾病。超过70%的PI未被诊断出来，这导致死亡率、合并症和医疗成本增加。在PI疾病中，联合免疫缺陷（CID）的特征是复杂的免疫缺陷。常见变异型免疫缺陷（CVID）是最常见的PI类型之一。鉴于现有的治疗方法，在严重发病和死亡发生之前识别有CID和CVID风险的成年患者至关重要。
我们开发了一种基于深度学习的方法（名为“TabMLPNet”），用于分析来自电子健康记录的具有全国代表性的医疗理赔数据（Optum®数据，覆盖全美国），并在识别成人CID/CVID的背景下进行评估。此外，我们揭示了与CID/CVID相关的最重要的前驱表型组合。生成了四个大型队列：总共47,660例PI病例和（1:1匹配的）对照。
TabMLPNet模型在各队列中的敏感性/特异性范围为0.82 - 0.88/0.82 - 0.85。识别出了与CID/CVID相关的前驱表型的独特组合，包括呼吸道感染/病症、遗传异常、心脏缺陷、自身免疫性疾病、血液疾病和恶性肿瘤，这可能有助于系统地识别CID和CVID。
我们在大规模医疗理赔数据上评估了一种在CID和CVID检测方面准确的方法。我们的预测方案可能会带来新的临床见解，并扩展识别有CID和CVID风险的成年患者的指南，还可用于在人群层面改善患者预后。
