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.

项目概要 在常规临床护理过程中获得的神经影像扫描是一种常见的临床现象 发现隐性脑血管疾病 (CCD)，包括隐性脑梗塞 (CBI) 和白质 疾病（WMD），发生在没有中风或短暂性脑缺血发作病史的患者中。事实上，流行病学研究 表明隐性 CBI 比临床明显的中风更为常见，这些影像学发现 未来中风和痴呆的强大、独立的危险因素。然而，目前还没有经过证实的预防措施 开始危险因素改变治疗的治疗或指南。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， 尚不清楚这些结果在多大程度上适用于 CCD 患者。此外，患者和提供者 很少有人意识到这些发现，即使它们被发现了。作为我们之前拨款 (R01-NS102233) 的一部分， 我们开发了一种自然语言处理 (NLP) 算法来识别偶然发现的 (id-) CCD 神经影像报告，我们将其移植到大型综合医疗保健系统中。我们确定了一组 近 25 万 50 岁以上的患者接受了头部 CT 或 MRI 检查后患有中风和 索引扫描时无痴呆。我们分析的主要结果包括： NLP 可以识别 id-CBI 和 来自神经影像报告和人类读者的 id-WBD；其中大约三分之一采用了 id-CCD 以年龄和血管危险因素相关的方式进行扫描； id-CCD 会增加未来中风的风险 以及未来痴呆症的发生率大约是原来的 2 到 3 倍； NLP 能够提取额外的重要预后信息 从常规获得的成像报告中获得有关大规模杀伤性武器严重程度的信息；最后，这些患者通常 发现 id-CCD 后未给予危险因素改变治疗。鉴于招聘此人的难度 针对高危人群，我们现在建议利用该 NLP 系统作为平台来规划和开展前瞻性研究 随机比较有效性研究以确定 id-CCD 的最佳治疗策略。因此，我们的 目标是： 目标 1：告知预防性临床试验的入组标准并确保试验的一致性 研究结果表明，我们将把队列扩大到北加州凯撒医疗机构，并进一步描述患者的特征 与 id-CCD 了解他们未来中风和痴呆的风险。目标 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 id-CCD 患者未来中风和痴呆症的修改算法。目标 3：确定最优 在人口结构多样化的人群中采取招募策略，我们将研究招募这种人员的可行性 基于 NLP 前瞻性发现的新人群（即与临床鉴定同时进行） 以及回顾性（根据预先存在的扫描确定）。目标 4：根据上述发现，我们将计划 预防 CCD 人群中风和痴呆的多中心临床试验。