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

项目总结 阻塞性睡眠呼吸暂停(OSA)是一种与睡眠相关的呼吸障碍，与主要的共病有关，并 据估计，全球有近10亿人受到影响。此外，在患病率、诊断率等方面存在差异 根据患者的人口统计数据，如年龄、种族和性别，对阻塞性睡眠呼吸暂停综合征的发病率和合并症结果进行分析。 阻塞性睡眠呼吸暂停综合征的临床表现、客观测量和结果--表型的多样性 强调了预测模型改善阻塞性睡眠呼吸暂停综合征患者护理的机会。预测未来(即5- 诊断后一年)OSA合并症结局的风险，并预测OSA的不同治疗方法如何影响 这些风险可以帮助临床医生和患者选择最佳的治疗策略。 目前对阻塞性睡眠呼吸暂停综合征结果的研究存在重大局限性。先前的研究已经描述了阻塞性睡眠呼吸暂停综合征患者的群体 表现出相似的特征，称为阻塞性睡眠呼吸暂停综合征的亚型。然而，这些研究一直是 通过分析从调查问卷中获得的相对较少的变量而受到限制。为了解决这一限制，我们将 使用丰富的纵向电子病历(EMR)数据来表征OSA亚型并预测 个别患者的OSA结局风险。为了从电子病历数据中提取见解，我们将利用现代 基于机器学习(ML)的计算方法。现有OSA研究的第二个主要限制是 某些群体的预测模型性能较差。模型偏差具有现实世界的负面影响。这个 无处不在的Stop-bang问卷用于筛查患者进行进一步的OSA测试，但对女性的效果较差 和亚洲人，导致这些群体中OSA的潜在延迟、漏诊或误诊。致信地址 考虑到这一局限性，这个拟议的项目将评估和减轻我们预测模型中存在的偏差。 为了更好地了解与OSA结果相关的患者因素，该项目有两个目标。在目标1中 将应用聚类方法来识别具有相似亚型的OSA患者组 根据临床特征和EMR数据中存在的客观测量的组合。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，从而提供对 潜在的病理生理差异。在Aim 2中，将应用ML分类器来构建和验证 对未来阻塞性睡眠呼吸暂停的结果风险以及阻塞性睡眠呼吸暂停治疗效果的算法公平预测模型。病人- 通过目标1和目标2持续与OSA结局风险差异相关的特定因素将 提供对治疗方案的个性化见解，并提供更有力的潜在病理生理证据 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.