Covert Cerebrovascular Disease Detected by Artificial Intelligence (C2D2AI): A Platform for Pragmatic Evidence Generation for Stroke and Dementia Prevention

人工智能检测隐性脑血管疾病（C2D2AI）：中风和痴呆症预防的实用证据生成平台

• 批准号: 10591063
• 负责人: DAVID M KENT
• 金额: $ 289.48万
• 依托单位: TUFTS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10591063
• 关键词: Address; Age; Algorithms; Alzheimer' s Disease; American Heart Association; American Stroke Association; Articulation; Artificial Intelligence; Attention; Awareness; Blood Platelets; Blood Vessels; Brain; Brain Infarction; California; Caring; Cerebrovascular Disorders; Clinic; Clinical; Clinical Trials; Code; Cognitive; Cohort Studies; Dementia; Development; Disease; Electronic Health Record; Enrollment; Ensure; Event; Foundations; Future; Generations; Goals; Grant; Guidelines; Head; Health; Human; ICD-9; Image; Impaired cognition; Incidence; Incidental Discoveries; Incidental Findings; Individual; Infarction; Integrated Health Care Systems; Location; Magnetic Resonance Imaging; Modification; Morbidity - disease rate; Multi-Institutional Clinical Trial; Natural Language Processing; Neurologic; Patient Selection; Patients; Population; Population Heterogeneity; Population Research; Populations at Risk; Prevention; Prevention Research; Preventive treatment; Primary Prevention; Prognosis; Protocols documentation; Provider; Randomized; Reader; Recommendation; Recording of previous events; Recurrence; Reporting; Research; Risk; Risk Factors; Scanning; Severity of illness; Standardization; Stroke; Stroke prevention; Structure; Subgroup; System; Time; Transient Ischemic Attack; Translating; Vascular Dementia; White Matter Disease; Work; X-Ray Computed Tomography; age related; cardiovascular risk factor; clinical care; clinical practice; cohort; comparative effectiveness study; dementia risk; design; disorder risk; epidemiology study; high risk; high risk population; improved; indexing; insight; mixed dementia; models and simulation; neuroimaging; novel; optimal treatments; population based; prevent; prevention clinical trial; prognostic; prospective; recruit; routine care; screening; stroke risk; treatment effect; treatment strategy; vascular risk factor

Project Summary It is a common clinical occurrence that neuroimaging scans obtained in the course of routine clinical care discover covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), in patients with no history of stroke or transient ischemic attack. Indeed, epidemiologic studies indicate that covert CBI are far more common than clinically-evident strokes and these imaging findings are strong, independent risk factors for future stroke and dementia. However, there are no proven preventive treatments or guidelines for initiating risk factor-modifying therapy. While there is strong evidence that antiplatelet therapy and statin therapy are effective in preventing recurrent stroke in patients with prior stroke, it is unclear the degree to which these results apply to patients with CCD. Additionally, patients and providers are rarely aware of these findings, even when they are detected. As part of our previous grant (R01-NS102233), we developed a natural language processing (NLP) algorithm to identify incidentally discovered (id-) CCD from neuroimaging reports, which we ported into a large integrated healthcare system. We identified a cohort of almost a quarter million patients over age 50 who received either a head CT or MRI and were stroke- and dementia- free at the time of the index scan. Key findings of our analyses include: NLP can identify id-CBI and id-WBD from neuroimage reports as well as human readers; that id-CCD is present in about one-third of these scans in an age- and vascular risk factor dependent manner; that id-CCD increases the risk of future stroke and future dementia by approximately 2- to 3-fold; that NLP is able to extract additional important prognostic information on WMD severity from routinely obtained imaging reports; and finally, these patients are generally not given risk factor modifying treatment following the discovery of id-CCD. Given the difficulty of recruiting this at risk population, we now propose to leverage this NLP system as a platform to plan and conduct prospective randomized comparative effectiveness studies to identify optimal treatment strategies for id-CCD. Thus, our aims are: Aim 1: To inform the enrollment criteria of prevention clinical trials and ensure consistency of findings, we will expand the cohort to Kaiser Permanente Northern California and further characterize patients with id-CCD regarding their future stroke and dementia risk. Aim 2: To determine optimal treatment algorithms, we will leverage established simulation models to estimate the treatment effects of different risk factor modification algorithms in patients with id-CCD on future stroke and dementia. Aim 3: To determine optimal recruitment strategies in demographically diverse populations, we will examine the feasibility of recruiting this novel population based on NLP-identified findings both prospectively (i.e. concurrent with clinical identification) and retrospectively (as identified from pre-existing scans). Aim 4: Based on the above findings we will plan a multicenter clinical trial for the prevention of stroke and dementia in this population with CCD.

项目摘要 这是一个常见的临床发生，神经影像学扫描获得的过程中，常规临床护理 发现隐性脑血管病（CCD），包括隐性脑梗死（CBI）和白色物质 无卒中或短暂性脑缺血发作病史的患者。事实上，流行病学研究 表明隐性脑梗塞比临床上明显中风更常见，这些影像学发现 未来中风和痴呆症的独立危险因素。然而，目前还没有有效的预防措施。 治疗或指导启动风险因子修饰治疗。虽然有强有力的证据表明， 抗血小板治疗和他汀类药物治疗可有效预防既往卒中患者的卒中复发， 目前尚不清楚这些结果在多大程度上适用于CCD患者。此外，患者和供应商 很少意识到这些发现，即使他们被发现。作为我们先前赠款的一部分（R 01-NS 102233）， 我们开发了一种自然语言处理（NLP）算法，用于从 神经成像报告，我们将其移植到一个大型综合医疗系统中。我们发现了一组 近25万50岁以上的中风患者接受了头部CT或MRI检查， 在索引扫描时没有痴呆。我们分析的主要发现包括：NLP可以识别id-CBI， id-WBD来自神经成像报告以及人类读者; id-CCD存在于这些报告中约三分之一 以年龄和血管风险因素依赖的方式进行扫描; id-CCD会增加未来中风的风险 和未来的痴呆症约2至3倍; NLP能够提取额外的重要预后 从常规获得的成像报告中获得的关于大规模毁灭性武器严重程度的信息;最后，这些患者通常 在发现id-CCD后未给予风险因素修正治疗。考虑到招募这些人的难度 在风险人群中，我们现在建议利用这个NLP系统作为一个平台，计划和进行前瞻性的 随机比较有效性研究，以确定id-CCD的最佳治疗策略。所以我们 目的1：告知预防性临床试验的入组标准，并确保 研究发现，我们将把队列扩大到北方加州的Kaiser Permanente，并进一步描述患者的特征 关于他们未来中风和痴呆的风险。目的2：确定最佳治疗算法， 我们将利用已建立的模拟模型来估计不同危险因素的治疗效果 在未来中风和痴呆症的id-CCD患者的修改算法。目标3：确定最佳 在人口统计学上不同的人群中招募策略，我们将研究招募这种 基于NLP确定的结果的新人群，前瞻性（即与临床确定同时进行） 以及回顾性地（如从预先存在的扫描中识别的）。目标4：基于上述发现，我们将计划 一项多中心临床试验，用于预防CCD患者的卒中和痴呆。