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.

项目总结/摘要 背景视力障碍与阿尔茨海默病和相关疾病密切相关。 痴呆症（ADRD）在许多横断面和纵向研究中，我们发现， 基线视力与后续痴呆症的风险越来越高有关。神经感觉剥夺 视力障碍可能会对认知资源提出更高的要求，加速认知能力的下降， 增加认知障碍的发生率。相反，改善视力可以提高认知能力， 通过增加神经感觉输入和减少对处理视觉的认知需求 信息.白内障是视力障碍的最常见原因，幸运的是， 然而，我们发现，ADRD患者接受白内障手术的可能性仅为 没有ADRD的人这可能反映了对潜在获益较少和感知风险较大的担忧。 目标.我们的长期目标是评估白内障手术作为一种潜在的干预措施，以“弯曲 ADRD发生和进展风险的“曲线”，包括优化患者选择和手术时机。 本提案的目的是研究白内障手术如何影响白内障的发病率和进展， 轻度认知功能障碍（MCI）和ADRD，开发模型来预测个体患者的ADRD/MCI 白内障手术后的结果，并确定关键的混杂因素，介质和效应调节剂。我们 假设白内障手术与（1）新MCI和ADRD发生率降低相关， (2)基线MCI或ADRD患者的认知下降和损伤进展减少，以及 （3）我们将能够预测个体患者的结果。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，开发精心策划的数据集， 达到三个目的：（1）确定白内障手术对ADRD和MCI发病率的影响;（2）确定 白内障手术对基线ADRD或MCI患者认知功能下降和损害的影响， （3）建立白内障术后ADRD和MCI预后的患者水平预测模型。 冲击基于EHR的机器学习分析迄今尚未应用于ADRD研究， 白内障手术对认知结果的影响尚不清楚。发现一种广泛应用的白内障 手术干预改善认知结果将是变革性的。我们估计有一个潜在的 每年有近35万患者需要进行白内障手术， 老年痴呆症这项工作的结果将直接为白内障手术风险的讨论提供信息 并将促进未来的研究，包括实用的临床试验设计。通过开发和 传播基于EHR的开源算法，以识别和分类认知和视觉障碍， 该提案将使其他ADRD风险因素和干预措施的调查，眼病研究， 一种更精确的方法来管理个体患者。