Rider Nicholas L, Coffey Michael, Kurian Ashok, Quinn Jessica, Orange Jordan S, Modell Vicki, Modell Fred
Division of Clinical Informatics, Liberty University College of Osteopathic Medicine and the Liberty Mountain Medical Group, Lynchburg, Va.
Department of Information Services, Texas Children's Hospital, Houston, Tex.
J Allergy Clin Immunol. 2023 Jan;151(1):272-279. doi: 10.1016/j.jaci.2022.10.005. Epub 2022 Oct 13.
Identification of patients with underlying inborn errors of immunity and inherent susceptibility to infection remains challenging. The ensuing protracted diagnostic odyssey for such patients often results in greater morbidity and suboptimal outcomes, underscoring a need to develop systematic methods for improving diagnostic rates.
The principal aim of this study is to build and validate a generalizable analytical pipeline for population-wide detection of infection susceptibility and risk of primary immunodeficiency.
This prospective, longitudinal cohort study coupled weighted rules with a machine learning classifier for risk stratification. Claims data were analyzed from a diverse population (n = 427,110) iteratively over 30 months. Cohort outcomes were enumerated for new diagnoses, hospitalizations, and acute care visits. This study followed TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) standards.
Cohort members initially identified as high risk were proportionally more likely to receive a diagnosis of primary immunodeficiency compared to those at low-medium risk or those without claims of interest respectively (9% vs 1.5% vs 0.2%; P < .001, chi-square test). Subsequent machine learning stratification enabled an annualized individual snapshot of complexity for triaging referrals. This study's top-performing machine learning model for visit-level prediction used a single dense layer neural network architecture (area under the receiver-operator characteristic curve = 0.98; F1 score = 0.98).
A 2-step analytical pipeline can facilitate identification of individuals with primary immunodeficiency and accurately quantify clinical risk.
识别患有潜在先天性免疫缺陷和易感染的患者仍然具有挑战性。对于这些患者而言，随之而来的漫长诊断过程往往会导致更高的发病率和欠佳的治疗结果，这凸显了开发提高诊断率的系统方法的必要性。
本研究的主要目的是构建并验证一种可推广的分析流程，用于在全人群中检测感染易感性和原发性免疫缺陷风险。
这项前瞻性纵向队列研究将加权规则与用于风险分层的机器学习分类器相结合。在30个月内对来自不同人群（n = 427,110）的索赔数据进行了迭代分析。对新诊断、住院和急性护理就诊的队列结果进行了统计。本研究遵循TRIPOD（个体预后或诊断多变量预测模型的透明报告）标准。
与低-中风险人群或无相关索赔的人群相比，最初被确定为高风险的队列成员被诊断为原发性免疫缺陷的比例更高（分别为9% 、1.5%和0.2%；P <.001，卡方检验）。随后的机器学习分层能够实现对转诊进行分类的复杂性的年度个体快照。本研究中用于就诊水平预测的表现最佳的机器学习模型使用了单密集层神经网络架构（受试者工作特征曲线下面积 = 0.98；F1分数 = 0.98）。
一个两步分析流程可以促进原发性免疫缺陷个体的识别，并准确量化临床风险。
