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

项目摘要 跨多个专科的提供者在确定 抗核抗体(ANA)阳性。而ANA阳性对自身免疫高度敏感 这种疾病是非特异性的，高达20%的普通人群ANA呈阳性 没有患上自身免疫性疾病。辅助临床医生对ANA阳性进行分层的风险模型 患者目前还不存在。通过识别高危患者，医疗服务提供者可以适当地进行分类 患者需要及时治疗，以降低与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)中为自身免疫建立风险模型 改善结果的疾病。这项提案的总体目标是确定积极的 患有自身免疫性疾病的高危ANA患者应适当 对风湿病进行分诊，以便更早诊断和治疗。我们的机构具有以下专业知识 生物统计学、生物医学信息学和实施科学在以下方面取得了成功 构建和测试健壮的电子病历风险模型。建立在这个完善的基础设施上， 我们假设我们可以使用可用的EHR数据来识别ANA阳性患者，这些患者 自身免疫性疾病的高风险。我们假设在现实中使用量身定制的风险评估- 在EHR中的时间可以减少自身免疫性疾病的诊断和治疗时间。我们将测试 这些假设具有以下特定目的：(1)改进和验证中提供的功能 EHR用于区分自身免疫性疾病ANA阳性患者与阳性患者 不发生自身免疫性疾病的ANA患者和(2)进行适应性治疗， EHR中自身免疫性疾病风险模型的随机、实用评估 对ANA阳性的患者进行分层。对于目标1，我们将验证自身免疫的风险模型 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：研究方法。对于目标2，我们将实时部署自身免疫性疾病的风险模型 啊哈。我们将随机将ANA阳性患者随机分为两组，一组是模型中的风险评分 显示并采取行动，而不是显示风险分数或通常的注意事项。我们将评估是否 更改此EHR系统，并计算风险分值并与订单共享 提供者与平时的护理相比，影响自身免疫性疾病的诊断和治疗时间。 我们的建议是创新的，因为它不仅为自身免疫建立了一个预测性风险模型 但也会部署和评估模型是否会影响患者的预后。对于预期的 结果，我们预计将部署EHR风险模型，以识别ANA阳性患者 罹患自身免疫性疾病的风险高，诊断和治疗时间缩短。

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.