Multi-modal Health Information Technology Innovations for Precision Management of Glaucoma

项目摘要/摘要 青光眼是世界上导致不可逆转失明的主要原因，将影响1.1亿英镑 到2040年的人口数量。早期发现和治疗至关重要，因为通常不存在症状。 直到疾病进展。需要一种数据驱动的精准医疗方法来更好地 确定哪些人罹患这种疾病的风险最高，哪些人的患病风险最高 进展迅速导致视力丧失的风险。虽然在眼睛方面已经有了相当大的进步 成像和测试改善青光眼监测，精准管理青光眼 不完整，不考虑患者并存的全身情况，并发全身 药物和治疗，以及坚持青光眼处方药治疗。 了解全身状况，特别是血管状况，如 高血压、冲击性青光眼对公共卫生的重要性日益增加 老龄化人口面临的并存问题。初步研究表明， 系统数据的价值，即使没有眼科终点。同样，测量药物 坚持对于指导患者咨询、参与和避免 下游干预措施，如手术，费用高，发病率高。这些因素 对于提供一个更全面的青光眼管理和 对于改善患者的预后，它们的研究相对较少。 我建议应用医疗信息技术(IT)中的多模式进步来解决 这些差距和实现的具体目标如下：(1)开发基于机器学习的 使用全身电子学对青光眼进展风险患者进行分类的预测模型 来自不同的全国患者队列的健康记录(EHR)数据；(2)评估如何整合 新型智能手表家用血压监测仪的血压数据增强了预测性 青光眼风险分层模型，以及(3)测量青光眼用药依从性 使用创新的灵活的电子传感器来验证它们的使用，以供未来的干预措施之用 改善青光眼患者的依从性和临床结局。这些研究将利用国家- 大数据预测建模中的最先进方法以及 传感器技术。这种多方面的方法将为医疗IT奠定基础 旨在改善风险分层和产生新的治疗靶点的框架 适用于青光眼患者。

PROJECT SUMMARY/ABSTRACT Glaucoma is the world's leading cause of irreversible blindness and will affect >110 million people by 2040. Early detection and treatment are critical, as symptoms typically do not present until the disease is advanced. A data-driven precision medicine approach is needed to better identify individuals who are at greatest risk of developing the disease and who are at greatest risk of progressing quickly to vision loss. While there has been considerable progress in eye imaging and testing to improve glaucoma monitoring, precision management of glaucoma is incomplete without accounting for patients' co-existing systemic conditions, concurrent systemic medications and treatments, and adherence with prescribed glaucoma treatment. Understanding how systemic conditions, and specifically vascular conditions such as hypertension, impact glaucoma presents growing public health importance given the increasing co-morbidities facing aging populations. Preliminary studies have demonstrated the predictive value of systemic data, even without ophthalmic endpoints. Similarly, measuring medication adherence is important for guiding patient counseling and engagement and avoiding downstream interventions such as surgeries, which carry high cost and morbidity. These factors are important for providing a more comprehensive perspective of glaucoma management and for improving patient outcomes, yet they are relatively understudied. I propose applying multi-modal advancements in health information technology (IT) to address these gaps and achieve the following specific aims: (1) Develop machine learning-based predictive models classifying patients at risk for glaucoma progression using systemic electronic health record (EHR) data from a diverse nationwide patient cohort; (2) evaluate how integrating blood pressure (BP) data from novel smartwatch-based home BP monitors enhance predictive models for risk stratification in glaucoma, and (3) measure glaucoma medication adherence using innovative flexible electronic sensors to validate their use for future interventions aimed at improving adherence and clinical outcomes in glaucoma. These studies would leverage state- of-the-art methods in big-data predictive modeling as well as cutting-edge advancements in sensor technologies. This multi-faceted approach will build a foundation for a health IT framework geared toward improving risk stratification and generating novel therapeutic targets for glaucoma patients.