Predicting risk of systemic autoimmune disease in patients with positive antinuclear antibodies

项目摘要 跨多个专业的提供者在确定一个 抗核抗体（ANA）阳性。ANA阳性对自身免疫性疾病高度敏感， 疾病，它是非特异性的，高达20%的一般人群具有阳性ANA 没有自身免疫性疾病帮助临床医生对ANA阳性进行分层的风险模型 目前没有病人。通过识别高风险患者，医疗服务提供者可以正确地进行分类 患者及时治疗，以减少自身免疫性疾病相关的发病率和死亡率。 我们的长期目标是在电子健康记录（EHR）中建立自身免疫性疾病的风险模型。 改善结果的疾病。本建议的总体目标是确定积极的 ANA患者有发生自身免疫性疾病的高风险，以促进适当的 分流到风湿科，以便及早诊断和治疗。我们的机构拥有专业知识， 生物统计学，生物医学信息学和实施科学已经证明了成功， 建立和测试强大的EHR风险模型。在这一完善的基础设施上， 我们假设我们可以使用现有的EHR数据来识别ANA阳性患者， 自身免疫性疾病的高风险。我们假设在真实的中使用定制的风险评估- 在EHR中的时间可以减少自身免疫性疾病诊断和治疗的时间。我们将测试 这些假设有以下具体目标：（1）完善和验证功能， EHR用于区分发展为自身免疫性疾病的ANA阳性患者和 ANA患者谁不发展自身免疫性疾病和（2）进行适应性， EHR中自身免疫性疾病风险模型的随机化、实用性评估， 对ANA阳性患者进行分层。对于目标1，我们将验证自身免疫性疾病的风险模型， 使用logistic回归和机器学习的EHR数据分析ANA阳性患者的疾病 方法.对于目标2，我们将在 电子病历我们将ANA阳性患者随机分为两组， 显示并采取行动vs.不显示风险评分或常规护理。我们将评估，如果 使EHR系统随着计算的风险分数而改变，并与订单共享 与常规护理相比，提供者影响自身免疫性疾病诊断和治疗的时间。 我们的建议是创新的，因为它不仅建立了一个预测自身免疫性疾病的风险模型， 疾病，而且还部署和评估该模型是否影响患者的结果。就预期 结果，我们预计部署一个EHR风险模型，识别阳性ANA患者， 发展自身免疫性疾病的风险高，缩短诊断和治疗时间。

Project Summary Providers across multiple specialties face challenges in determining the clinical significance of a positive antinuclear antibody (ANA). While a positive ANA is highly sensitive for autoimmune disease, it is non-specific with up to 20% of the general population having a positive ANA without having autoimmune disease. Risk models to aide clinicians in stratifying positive ANA patients do not currently exist. By identifying high-risk patients, providers could properly triage patients for prompt treatment to reduce autoimmune disease-related morbidity and mortality. Our long-term goal is to build risk models in the electronic health record (EHR) for autoimmune diseases that improve outcomes. The overall objective of this proposal is to identify positive ANA patients who are at high risk for developing autoimmune disease to facilitate appropriate triage to rheumatology for earlier diagnosis and treatment. Our institution with expertise in biostatistics, biomedical informatics, and implementation science has demonstrated success in building and testing robust EHR risk models. Building upon this well-established infrastructure, we hypothesize that we can use available EHR data to identify positive ANA patients that are high risk for autoimmune disease. We hypothesize that using tailored risk assessments in real- time in the EHR can reduce time to autoimmune disease diagnosis and treatment. We will test these hypotheses with the following specific aims: (1) Refine and validate features available in the EHR to distinguish positive ANA patients who develop autoimmune disease from positive ANA patients who do not develop autoimmune disease and (2) Conduct an adaptive, randomized, pragmatic evaluation of an autoimmune disease risk model in the EHR to risk- stratify patients with a positive ANA. For Aim 1, we will validate a risk model for autoimmune disease in positive ANA patients using EHR data with logistic regression and machine learning methods. For Aim 2, we will deploy a risk model for autoimmune disease in real-time in the EHR. We will randomize positive ANA patients to either have a risk score from the model displayed and acted upon vs. not having a risk score displayed or usual care. We will assess if having this EHR system change with a risk score calculated and shared with the ordering provider compared to usual care affects time to autoimmune disease diagnosis and treatment. Our proposal is innovative in that it not only builds a predictive risk model for autoimmune disease but also deploys and assesses if the model impacts patient outcomes. For expected outcomes, we anticipate deploying an EHR risk model that identifies positive ANA patients at high risk for developing autoimmune disease and decreases time to diagnosis and treatment.