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

通过电子病历数据对阻塞性睡眠呼吸暂停结果进行公平的风险概况和预测模型

• 批准号: 10678108
• 负责人: Victor Borza
• 金额: $ 3.28万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10678108
• 关键词: Accounting; Acute; Address; Affect; Age; Algorithms; Asian population; COVID-19; Cardiovascular Diseases; Characteristics; Chronic; Classification; Clinical; Clinical Data; Computer Models; Computerized Medical Record; Computing Methodologies; Continuous Positive Airway Pressure; Data; Data Set; Dementia; Diabetic Nephropathy; Diagnosis; Disease; Disparity; Drowsy Driving; Ethnic Origin; Exhibits; Foundations; Functional disorder; Future; Gender; Geographic Locations; Grant; Individual; Intervention; Intuition; Investigation; Machine Learning; Measurement; Metabolic syndrome; Methods; Modeling; Modernization; Obstructive Sleep Apnea; Operative Surgical Procedures; Outcome; Outcomes Research; Patient Care; Patients; Pattern; Performance; Persons; Phenotype; Physicians; Population; Prevalence; Process; Questionnaires; Race; Recommendation; Research; Research Ethics; Resources; Risk; Severities; Sleep Apnea Syndromes; Socioeconomic Status; Subgroup; Symptoms; Testing; Time; Translating; Vehicle crash; Woman; benefit sharing; career; clinical practice; clinically actionable; comorbidity; compliance behavior; demographics; improved; individual patient; insight; machine learning classifier; machine learning prediction; model building; neuropsychiatric disorder; outcome disparities; patient screening; predictive modeling; risk prediction; skills; treatment effect; treatment guidelines; treatment response; treatment strategy

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.

项目摘要 阻塞性睡眠呼吸暂停（OSA）是一种与睡眠有关的呼吸障碍，与主要合并症有关，是 估计在全球范围内影响近10亿人。此外，患病率，诊断有差异 基于年龄，种族和性别等患者的人口统计学的OSA的费率和OSA的合并结果。 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- 诊断后年）OSA合并后结果的风险，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治疗策略。 当前的OSA结果研究具有关键局限性。先前的研究表征了OSA患者组 暴露了相似的特征，称为OSA的子表型。但是，这些研究已经 通过分析从问卷调查获得的相对较少的变量来限制。为了解决这个限制，我们将 使用丰富的纵向电子病历（EMR）数据来表征OSA子表型并预测 单个患者的OSA结果风险。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性是 某些组的预测模型性能较差。模型偏见具有现实世界的负面影响。这 无处不在的停止爆炸问卷用于筛查患者的进一步OSA测试，女性的性能较差 和亚洲人，导致OSA在这些群体中的潜在延迟，未诊断。解决 这个限制，该提议的项目将评估和减轻我们的预测模型中存在的偏见。 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 聚类方法将应用于识别具有相似子表型的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 - 表型将通过暴露不同OSA结果的速率比较表型，从而提供直觉 潜在的潜在病理生理差异。在AIM 2中，将应用ML分类器来构建和验证 未来OSA结果风险以及OSA治疗的影响的算法上公平的预测模型。病人- 通过目标1和2始终与OSA结果风险差异相关的特定因素。 对治疗方案提供个性化见解，也可以提供潜在的病理生理学的更有力的证据 值得进一步调查。