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

开发用于预测儿童哮喘治疗反应的被动数字标记

• 批准号: 10670853
• 负责人: Arthur Hamie Owora
• 金额: $ 4.16万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10670853
• 关键词: Developing; Passive; Digital; Marker; Prediction

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.

2.0 项目概要 儿童哮喘治疗不足的现象普遍存在，并且通常不知道事件病例的正确治疗方法 因此，三分之二的儿童哮喘事件被广泛使用治疗试验作为治疗策略。 即使在开始治疗后，病例仍然有持续的症状，错过了早期治疗的机会。 有效的治疗会增加儿童哮喘相关发病的风险（即不受控制的哮喘） 哮喘）给患者、家庭和医疗保健系统带来沉重负担。 需要决策工具来确定哪些孩子将从早期阶段的哪种治疗中受益 目前缺乏。 该提案的基础是应用新颖的机器学习 (ML) 方法来 越来越多的电子健康记录 (EHR) 风险/预后数据可以生成预测分析和 然后，临床医生可以利用这些见解来获得有效的儿童哮喘治疗反应。 护理点的治疗决策，包括更主动和个性化的治疗，以改善 尽管治疗反应预测所需的风险和预后因素是以患者为中心的。 这些信息通常嵌入在电子病历中，但在繁忙的儿科诊所中，这些信息有时会被维护者忽视。 通过 EHR 审查来确定这些因素以指导治疗决策可能会成本高昂、耗时且容易出错， 且不可行。 为了解决这些挑战和技术差距，我们建议开发、验证和评估儿童 用于治疗反应预测的哮喘被动数字标记 (PDM-TR)，即一种 ML 算法，可以 检索并综合“数字”电子病历中预先存在的“被动”收集的母子二元风险/预后数据 提供客观且可量化的治疗反应“标记”。 我们目睹了一个事件，当应用于从接触过的哮喘病例中获得的风险/预后 EHR 数据时， 作为一线治疗，我们的 PDM-TR 将高精度预测 2-3 个月的哮喘控制情况（灵敏度≥80 且特异性≥80）。PDM-TR 将“从现有 EHR 数据中学习”来预测特定治疗是否可能。 对于具有一组特定属性（即哮喘）的特定个体来说，取得成功（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、肺功能、体重] 将我们新颖的 PDM-TR 实时应用于现成的 EHR 数据可以有助于实现 开发及时、准确和可扩展的方法，为个性化儿童哮喘治疗提供信息 护理点。