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

2.0项目摘要 儿童哮喘的治疗不足是普遍存在的，而且对于偶发病例的正确治疗往往是未知的 因此广泛使用治疗试验作为治疗策略。三分之二的儿童哮喘 病例甚至在治疗开始后仍有持续症状。错过了早期的机会 有效的治疗有助于增加儿童哮喘相关发病率的风险（即，不受控 哮喘），这对患者、家庭和医疗保健系统造成了巨大的负担。但临床 在早期阶段确定哪个儿童将受益于哪种治疗所需的决策工具， 目前缺乏。 这一提议是基于这样一个概念，即应用新的机器学习（ML）方法来 越来越多可用电子健康记录（EHR）风险/预后数据可以生成预测分析， 关于儿童哮喘治疗反应的见解。然后，临床医生可以使用这些见解， 护理点的治疗决策，包括更积极主动和个性化的治疗， 以患者为中心的结果。尽管治疗反应预测所需的风险和预后因素 这些信息通常嵌入在EHR中，有时会被临床医生忽视。在忙碌的儿科门诊，活跃 EHR审查以识别这些因素来告知治疗决策可能是昂贵的、耗时的、容易出错的， 而且不可行 为了应对这些挑战和技术差距，我们建议开发，验证和评估童年 哮喘治疗反应预测被动数字标记（PDM-TR），即ML算法， 在“数字”电子健康记录中检索和综合先前存在的“被动”收集的母婴二联体风险/预后数据 以提供治疗反应的客观和可量化的“标志物”。 我们假设，当应用于风险/预后EHR数据来自哮喘事件暴露于 一线治疗，我们的PDM-TR将预测2-3个月时的哮喘控制，具有高准确性（灵敏度≥80 特异性≥80）。PDM-TR将“从现有的EHR数据中学习”，以预测特定的治疗是否可能 取得成功（即，实现哮喘控制）对于具有特定属性集合的给定个体（即，哮喘 风险和预后因素[例如，过敏史、湿疹、人口统计学、肺功能、体重 index]）。将我们的新型PDM-TR实时应用于现成的EHR数据可以有助于 开发及时、准确和可扩展的方法，为个性化的儿童哮喘治疗提供信息， 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.