Developing a Passive Digital Marker for the Prediction of Childhood Asthma Treatment Response

2.0项目总结 儿童哮喘的治疗不足很普遍，而且通常对偶发病例的正确治疗方法尚不清楚。 因此，广泛使用治疗试验作为一种治疗策略。三分之二的儿童哮喘事件 即使在开始治疗后，病例仍有持续性症状。及早错失机会 有效的治疗会增加儿童哮喘相关发病率(即失控)的风险 哮喘)，这给患者、家庭和医疗系统带来了巨大的负担。然而，临床上 在早期阶段确定哪个儿童将从哪种治疗中受益所需的决策工具有 目前还不够。 这一建议的基础是将新的机器学习(ML)方法应用于 日益可用的电子健康记录(EHR)风险/预后数据可以生成预测性分析和 关于儿童哮喘治疗反应的见解。然后，临床医生可以利用这些洞察力来有效地 在护理点做出治疗决策，包括更积极主动和个性化的治疗，以改进 以患者为中心的结果。尽管预测治疗反应所需的风险和预后因素 这些信息通常嵌入在EHR中，但有时会被临床医生忽视。在繁忙的儿科诊所，活跃着 EHR审查以确定告知治疗决定的这些因素可能是昂贵的、耗时的、容易出错的 而且是不可行的。 为了应对这些挑战和技术差距，我们建议开发、验证和评估一个童年 哮喘被动数字标记物治疗反应预测(PDM-TR)，即一种ML算法，它可以 检索和合成‘数字’电子病历中预先存在的‘被动’收集的母婴二联体风险/预后数据 为治疗反应提供一个客观和可量化的“标记物”。 我们假设，当应用于风险/预后EHR数据时，来自暴露于 一线治疗，我们的pdm-tr将高精度地预测2-3个月后哮喘的控制(≥80敏感度 和≥80特异性)。Pdm-tr将“从现有ehr数据中学习”，以预测一种特定的治疗是否可以 对具有一组特定属性(即哮喘)的特定个人成功(即，实现哮喘控制) 风险和预后因素[例如过敏史、湿疹、人口统计学、肺功能、体重 索引]))。将我们的新的pdm-tr实时应用于随时可用的电子病历数据，将有助于 制定及时、准确和可扩展的方法，为儿童哮喘的个性化治疗提供信息，请访问 护理点。

2.0 PROJECT SUMMARY Undertreatment of childhood asthma is prevalent and often the right treatment for incident cases is unknown hence the widespread use of therapeutic trials as a treatment strategy. Two-thirds of incident childhood asthma cases continue to have persistent symptoms even after treatment initiation. Missed opportunities for early efficacious treatment contribute to increased risk of childhood asthma-associated morbidity (i.e., uncontrolled asthma) that exerts a substantial burden on patients, families, and the healthcare system. However, clinical decision-making tools needed to identify which child will benefit from which treatment at an early stage are currently lacking. This proposal is predicated on the notion that applying novel machine learning (ML) methodologies to increasingly available electronic health record (EHR) risk/prognostic data can generate predictive analytics and insights regarding childhood asthma treatment response. Clinicians can then use such insights toward effective treatment decision-making at point of care, including more proactive and personalized treatment, for improved patient-centered outcomes. Although risk and prognostic factors needed for treatment response prediction are often embedded in EHR, this information is sometimes overlooked by clinicians. In busy pediatric clinics, active EHR review to identify such factors to inform treatment decisions can be costly, time consuming, error-prone, and infeasible. To address these challenges and technological gap, we propose to develop, validate, and evaluate a childhood asthma Passive Digital Marker for treatment response prediction (PDM-TR), that is, a ML algorithm that can retrieve and synthesize pre-existing `passively' collected mother-child dyad risk/prognostic data in `digital' EHR to provide an objective and quantifiable `marker' of treatment response. We hypothesize that when applied to risk/prognostic EHR data derived from incident asthma cases exposed to first-line treatments, our PDM-TR will predict asthma control at 2-3 months with high accuracy (≥80 sensitivity and ≥80 specificity). The PDM-TR will `learn from existing EHR data' to predict whether a specific treatment may be successful (i.e., achieve asthma control) for a given individual with a specific set of attributes (i.e., asthma risk and prognostic factors [e.g., history of allergy sensitization, eczema, demographics, lung function, body mass index]). Applying our novel PDM-TR in-real time to readily available EHR data could contribute towards the development of a timely, accurate and scalable approach to inform personalized childhood asthma treatment at point of care.