Using Informatics to Evaluate and Predict Cataract Surgery Impact on Alzheimer's Disease and Related Dementias and Mild Cognitive Impairment Outcomes

利用信息学评估和预测白内障手术对阿尔茨海默病和相关痴呆症以及轻度认知障碍结果的影响

• 批准号: 10525214
• 负责人: Suzann Pershing
• 金额: $ 79.38万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10525214
• 关键词: Affect; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Archives; Benefits and Risks; Biometry; Cataract; Cataract Extraction; Characteristics; Clinical; Clinical Trials; Clinical Trials Design; Cognitive; Contrast Sensitivity; Cox Proportional Hazards Models; Cross-Sectional Studies; Data; Data Set; Delirium; Dementia; Electronic Health Record; Epidemiology; Eye diseases; Eyeglasses; Family Caregiver; Foundations; Goals; Healthcare Systems; Home Nursing Care; Impaired cognition; Impairment; Incidence; Informatics; Intervention; Investigation; Link; Longitudinal Studies; Machine Learning; Mediator of activation protein; Methods; Modeling; Natural Language Processing; Operative Surgical Procedures; Ophthalmology; Outcome; Outcomes Research; Patient Selection; Patient-Focused Outcomes; Patients; Phenotype; Pragmatic clinical trial; Provider; Public Health; Research; Resources; Risk; Sensory Deprivation; Severities; Structure; Symptoms; Testing; Text; Time; Veterans Health Administration; Vision; Visual; Visual Acuity; Visual impairment; Work; base; cognitive function; cognitive testing; dementia risk; hazard; high risk; improved; individual patient; machine learning prediction; mild cognitive impairment; neurosensory; open source; patient stratification; patient subsets; precision medicine; predictive modeling; prevent; public health relevance; sex; shared decision making; social engagement; standard of care; structured data; unstructured data; visual deprivation; visual information; visual processing

PROJECT SUMMARY/ABSTRACT Background. Visual impairment has been strongly associated with Alzheimer’s disease and related dementias (ADRD) in numerous cross-sectional and longitudinal studies, and we have found that worse baseline vision is tied to increasingly higher risk of subsequent dementia. Neurosensory deprivation from visual impairment may place greater demands on cognitive resources, accelerating cognitive decline and increasing the incidence of cognitive impairment. Conversely, improving vision could improve cognitive outcomes by increasing neurosensory input and reducing cognitive demand for processing visual information. Cataracts are the most common cause of visual impairment—fortunately reversible with surgery, however, we have found that ADRD patients are only half as likely to undergo cataract surgery as those without ADRD. This may reflect concerns regarding less potential benefit and greater perceived risks. Objectives. Our long-term goal is to evaluate cataract surgery as a potential intervention to “bend the curve” for risk of ADRD onset and progression, including optimizing patient selection and timing for surgery. The objective of this proposal is to investigate how cataract surgery may affect incidence and progression of mild cognitive impairment (MCI) and ADRD, develop models to predict individual patients’ ADRD/MCI outcomes following cataract surgery, and identify key confounders, mediators, and effect modifiers. We hypothesize that cataract surgery is associated with (1) reduction in incidence of new MCI and ADRD and (2) reduced cognitive decline and impairment progression among patients with baseline MCI or ADRD, and that (3) we will be able to predict individual patient outcomes. We propose to use methods our group has developed to archive and analyze electronic health record (EHR) data, to develop a curated data set and achieve three Aims: (1) Determine impact of cataract surgery on ADRD and MCI incidence; (2) Determine impact of cataract surgery on cognitive decline and impairment among patients with baseline ADRD or MCI, and (3) Develop patient-level predictive models for ADRD and MCI outcomes after cataract surgery. Impact. EHR-based machine learning analysis has not been applied to ADRD research to date, and the influence of cataract surgery on cognitive outcomes is not yet known. Finding that a widely-available cataract surgery intervention improves cognitive outcomes would be transformative. We estimate a potential unmet need for cataract surgery affecting almost 350,000 patients annually—just among the subset of patients with existing Alzheimer’s disease. Results from this work will directly inform discussion of cataract surgery risks and benefits and will also facilitate future research, including pragmatic clinical trial design. By developing and disseminating open source EHR-based algorithms to identify and classify cognitive and visual impairment, this proposal will enable investigation of other ADRD risk factors and interventions, eye disease research, and a more precise approach to managing individual patients.

项目摘要/摘要 背景。视觉障碍与阿尔茨海默氏病及相关性密切相关 痴呆症（ADRD）在众多横断面和纵向研究中，我们发现 基线视力与随后的痴呆症风险增加相关。神经感觉剥夺 视觉障碍可能对认知资源提出更大的需求，加速认知能力下降和 增加认知障碍的发生率。相反，改善视力可以改善认知能力 通过增加神经感觉输入并减少对视觉处理的认知需求来取得的结果 信息。白内障是视觉障碍的最常见原因 - 不幸的是 但是，我们发现ADRD患者接受白内障手术的可能性仅为 那些没有adrd的人。这可能反映出对潜在收益和更大的风险更大的担忧。 目标。我们的长期目标是评估白内障手术作为“弯曲的潜在干预措施 曲线”出于ADRD发作和进展的风险，包括优化患者选择和手术时间。 该提案的目的是研究白内障手术如何影响 轻度认知障碍（MCI）和ADRD，开发模型来预测患者的ADRD/MCI 白内障手术后的结果，并识别关键的混杂因素，介体和效果修饰符。我们 假设白内障手术与（1）新MCI和ADRD的发生率降低有关 （2）基线MCI或ADRD患者的认知能力下降和损害进展，以及 （3）我们将能够预测个别的患者预后。我们建议使用我们小组的方法 开发用于存档和分析电子健康记录（EHR）数据，以开发策划的数据集和 实现三个目标：（1）确定白内障手术对ADRD和MCI事件的影响； （2）确定 白内障手术对基线ADRD或MCI患者认知能力下降和损害的影响， （3）在白内障手术后开发用于ADRD和MCI结果的患者级预测模型。 影响。迄今为止，基于EHR的机器学习分析尚未应用于ADRD研究， 白内障手术对认知结果的影响尚不清楚。发现广泛的白内障 手术干预改善认知结果将是变化的。我们估计潜在的未满足 每年都需要白内障手术影响近35万名患者的需求 - 现有的阿尔茨海默氏病。这项工作的结果将直接告知白内障手术风险的讨论 和好处，还将促进未来的研究，包括务实的临床试验设计。通过开发和 传播基于EHR的开源EHR算法以识别和分类认知和视觉障碍， 该建议将使其他ADRD风险因素和干预措施，眼病研究以及 一种管理个别患者的更精确的方法。