Fair risk profiles and predictive models for outcomes of obstructive sleep apnea through electronic medical record data

项目摘要 阻塞性睡眠呼吸暂停（OSA）是一种与主要并发症相关的睡眠相关呼吸障碍， 估计影响了全球近10亿人。此外，在患病率、诊断 基于患者的人口统计学特征（诸如年龄、种族和性别）的OSA的发病率和共病结果。 OSA的临床表现、客观测量和结果（表型）的多样性 强调了预测模型改善OSA患者护理的机会。预测未来（即5- OSA共病结局的风险，并预测OSA的不同治疗如何影响 这些风险可以帮助临床医生和患者选择最佳的治疗策略。 目前的OSA结果研究有关键的局限性。先前的研究已经描述了OSA患者的特征 表现出相似的特征，称为OSA的亚表型。然而，这些研究一直 由于分析的变量相对较少，只能从问卷中获得。为了解决这个问题，我们将 使用丰富的纵向电子病历（EMR）数据来表征OSA亚表型并预测 个体患者的OSA结局风险。为了从EMR数据中提取见解，我们将利用现代 基于机器学习（ML）的计算方法。现有OSA研究的第二个主要局限是 一些群体的预测模型性能较差。模型偏差会对现实世界产生负面影响。的 普遍使用的STOP-BANG问卷用于筛查患者进行进一步的OSA测试，女性表现更差 和亚洲个体，导致这些群体中OSA的潜在延迟、低估或误诊。解决 这个限制，这个拟议的项目将评估和减轻偏见，目前在我们的预测模型。 为了更好地了解与OSA结局相关的患者因素，该项目有两个目标。目标1 将应用聚类方法来识别具有相似亚表型的OSA患者组 根据EMR数据中存在的临床特征和客观测量的组合。然后，子- 表型将通过它们表现出不同OSA结果的比率进行比较， 潜在的潜在病理生理差异。在目标2中，ML分类器将用于构建和验证 算法公平的预测模型，未来的OSA结果的风险，以及OSA治疗的效果。病人- 通过目标1和2，与OSA结局风险差异一致相关的特定因素将 为治疗方案提供个性化见解，并为潜在病理生理学提供更有力的证据 值得进一步调查。

PROJECT SUMMARY Obstructive sleep apnea (OSA) is a sleep-related breathing disorder associated with major co-morbidities and is estimated to affect nearly one billion people worldwide. Moreover, there are differences in prevalence, diagnosis rates, and co-morbid outcomes for OSA based on the demographics of a patient, such as age, race, and gender. The diversity of the clinical manifestations, objective measurements, and outcomes – the phenotype – of OSA underscores the opportunity for predictive models to improve care of patients with OSA. Predicting future (i.e. 5- year post-diagnosis) risks of OSA co-morbid outcomes and predicting how different treatments for OSA affect these risks can help clinicians and patients choose the best treatment strategies. Current OSA outcomes research has key limitations. Prior studies have characterized groups of OSA patients that exhibit similar characteristics, referred to as sub-phenotypes of OSA. However, these studies have been limited by analyzing relatively few variables obtainable from questionnaires. To address this limitation, we will use rich longitudinal electronic medical records (EMR) data to characterize OSA sub-phenotypes and to predict OSA outcome risks for individual patients. To extract insights from EMR data, we will leverage modern computational methods based in machine learning (ML). A second major limitation of existing OSA research is worse predictive model performance for some groups. Model biases have real-world negative implications. The ubiquitous STOP-BANG questionnaire used to screen patients for further OSA testing performs worse for women and Asian individuals, leading to potential delayed, under-, or misdiagnosis of OSA in these groups. To address this limitation, this proposed project will assess and mitigate biases present in our predictive models. To better understand patient factors associated with OSA outcomes, this project has two aims. In Aim 1 clustering methods will be applied to identify groups of OSA patients who share similar sub-phenotypes according to combinations of clinical features and objective measurements present in EMR data. Then, sub- phenotypes will be compared by the rates at which they exhibit different OSA outcomes, providing intuition into potential underlying pathophysiologic differences. In Aim 2, ML classifiers will be applied to build and validate algorithmically fair predictive models for future OSA outcome risks as well as effects of OSA treatments. Patient- specific factors that are consistently associated with differences in OSA outcome risks through Aims 1 and 2 will provide both personalized insights into treatment options and stronger evidence of underlying pathophysiology worthy of further investigation.