Building Resources to Assess Impaired Neurocognition for Care and Research among Adults Aging with HIV (BRAIN Care HIV)

神经认知障碍（NCI）在老年（≥50岁）HIV感染者（PWH）中非常普遍。旧威尔斯亲王医院 NCI的发病率高于同龄的一般人群;患病率高达50% -甚至 在病毒学控制的人群中患有NCI与死亡率增加，独立性下降， 较低的药物依从性，决策能力差，可能更大的痴呆风险。NCI的发病机制 由于PWH的广泛多样性和影响大脑的因素（例如，艾滋病、 人口统计学、社会经济学、慢性炎症、合并症和心理社会压力）。这些知识， 但主要来自高收入国家。然而，艾滋病毒的负担在低和中- 收入国家（LMIC），我们对NCI的理解（例如，患病率;风险;患者/提供者需求） PWH才刚刚出现，风险可能与高收入国家不同。NCI风险的多模式表型可以帮助阐明 国家癌症研究所的机制，并协助制定更有针对性的干预措施。 HIC和LMIC之间（例如，免疫反应，患者年龄，HIV感染持续时间，治疗类型， 和社会经济因素），但大多数表型研究都是在HIC中使用PWH进行的。我们建议 利用多模态数据（例如，人口统计学，医学，炎症）从一个队列的多样化，老龄化，和治疗 PWH在马来西亚从两个时间点跨越4-6年，并增加一个时间点，以开发纵向 NCI风险的表型-马来西亚PWH的第一个。然而，一个先决条件是高质量、准确、公正， 和有效的神经认知测试数据，可以在任何环境中轻松收集，适合跨研究/跨国家 比较和大数据应用。由于马来西亚很少有符合这些要求的测试，我们 建议调整并初步规范一系列测试（NeuroScreen）。NeuroScreen很简短（约25 高度自动化，易于各级工作人员管理，通过互联网传播， 在不同国家/语言之间以及在统一的跨研究数据集中进行调整。它评估了六个神经认知 受艾滋病毒影响最大的领域，越来越多的证据表明，它是公正的， 公平，在青少年和成人艾滋病毒感染者和不同水平的计算机中心理测量有效 在多个国家和语言（美国，南非和邻国泰国）识字。没有测试应用程序 已被改编和规范为种族多样化的马来西亚人，其中最常用的语言是 马来西亚语、普通话、泰米尔语和英语。使用NeuroScreen的数据将增强我们的表型。 此外，有一个易于使用和有效的工具来测量神经认知和筛选NCI可以提高 为马来西亚威尔斯亲王医院提供研究及临床护理服务。我们将在马来西亚建立神经心理学专业知识（ （几乎没有）;促进对艾滋病毒中NCI的原因，风险和检测的多学科研究;并扩大 使用最新统计分析的能力（即，机器学习）。

Neurocognitive impairment (NCI) is highly prevalent in older (≥50 years) people with HIV (PWH). Older PWH have higher rates of NCI than the general population of the same age; prevalence rates are as high as 50% – even among the virologically controlled. Having NCI is associated with increased mortality, decline in independence, lower medication adherence, poor decision making, and possibly greater dementia risk. The pathogenesis of NCI in HIV is likely multifactorial due to extensive diversity of PWH and factors that affect the brain (e.g., HIV, demographic, socioeconomic, chronic inflammation, comorbidities, and psychosocial stress). This knowledge, however, comes mostly from high-income countries (HICs). Yet, the burden of HIV is greatest in low- and middle- income countries (LMICs) where our understanding of NCI (e.g., prevalence; risk; patient/provider needs) in aging PWH is only just emerging and risk may differ than in HICs. Multimodal phenotypes of NCI risk can help elucidate NCI’s mechanisms and assist in developing more targeted interventions for it. Critical aspects of HIV disease differ between HICs and LMICs (e.g., immune responses, age of patients, duration of HIV infection, type of treatment, and socio-economic factors), but most phenotyping studies have been done with PWH in HICs. We propose to leverage multimodal data (e.g., demographic, medical, inflammation) from a cohort of diverse, aging, and treated PWH in Malaysia from two time points across 4-6 years and add one more time point to develop longitudinal phenotypes of NCI risk – a first for PWH in Malaysia. A pre-requisite, however, is high quality, accurate, unbiased, and valid neurocognitive test data that can be easily collected in any setting and is suitable for cross-study/-country comparisons and Big Data applications. Because few tests meeting these requirements exist in Malaysia, we propose to adapt and preliminarily norm a battery of tests (NeuroScreen) that do. NeuroScreen is brief (~25 minutes), highly automated, easy-to-administer by all levels of staff, disseminated via the internet, designed for adaptation across countries/languages and in harmonized cross-study data sets. It assesses six neurocognitive domains most affected by HIV, and has a growing body of evidence demonstrating that it is unbiased, culturally fair, and psychometrically valid in adolescent and adult populations with HIV and varying levels of computer literacy in multiple countries and languages (US, South Africa, and neighboring Thailand). No testing apps have been adapted and normed for ethnically diverse Malaysians, where the most commonly spoken languages are Bahasa Malaysia, Mandarin, Tamil, and English. Using NeuroScreen’s data will enhance our phenotyping. Moreover, having an easy-to-use and valid tool to measure neurocognition and screen for NCI can enhance research and clinical care for PWH in Malaysia. We will build neuropsychological expertise in Malaysia (where there is little); promote multidisciplinary research into the causes, risks, and detection of NCI in HIV; and expand capacity to use state-of-the-art statistical analyses (i.e., machine learning).