PREDICTORS OF SEVERITY IN ALZHEIMERS DISEASE

阿尔茨海默病严重程度的预测因素

• 批准号: 3118426
• 负责人: YAAKOV STERN
• 金额: $ 39.16万
• 依托单位: COLUMBIA UNIV NEW YORK MORNINGSIDE
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-02-01 至 1994-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3118426
• 关键词: Alzheimer's disease; disease /disorder model; histopathology; human subject; human tissue; model design /development; nervous system disorder epidemiology; neurologic manifestations; neuropsychological tests; patient care management; postmortem; prognosis; sign /symptom

Although there is no effective treatment for Alzheimer's disease, clinicians should be able to provide the patient and family with some idea of the natural history of the illness or an accurate prediction of what to expect. Since the rate at which Alzheimer's disease progress is variable, this in not possible. This project is designed to validate a predictor model based on our previous investigations of the natural history of Alzheimer's disease. The objective of clinical prediction rules is to reduce the uncertainty inherent in the management of Alzheimer's disease by defining how to use clinical information to make predictions about the course of disease. Predictive findings are well defined clinical signs that are not part of the diagnostic criteria, yet are relevant to the clinician and help predict disease outcomes. In our previous studies, specific clinical signs, such as muscular rigidity, myoclonus, and hallucinations or delusions were useful in predicting a selected outcome; patients with either sign reached a more severe stage of dementia earlier than patients without these findings. We also began to define a eries of outcomes that occur as a consequence of the disease process. In the current proposal we will validate and expand this predictor model of Alzheimer's disease. This study will e conducted at three study sites in a sample of 240 patients within 1 to 3 years of the onset of their illness. Standardized regular 6 month assessments will be initiated after all subjects are entered in the first year. We will utilize a series of outcomes that are clinically relevant and meaningful to the patient and family. The new cohort will be larger giving us greater power, and will be collected in a manner that will improve our ability to refine predictive techniques and improve their accuracy. The ultimate goal of clinical prediction in improved patient management. The prognostic implications of this study will be of great interest in the design of therapeutic trails in the future. The proposed studies will also help to improve the efficiency and accuracy of physicians' judgements.

尽管阿尔茨海默病尚无有效治疗方法， 临床医生应该能够为患者及其家属提供 对疾病自然史的一些了解或准确的 预测会发生什么。 由于阿尔茨海默病的发病率 疾病进展是可变的，这是不可能的。 这个项目 旨在验证基于我们之前的预测模型 阿尔茨海默病自然史的研究。 这 临床预测规则的目标是减少不确定性 通过定义如何治疗阿尔茨海默病 使用临床信息来预测课程 的疾病。 预测结果是明确定义的临床症状，但并非如此 诊断标准的一部分，但与临床医生相关 并帮助预测疾病结果。 在我们之前的研究中， 特定的临床症状，如肌肉强直、肌阵挛等 幻觉或妄想对于预测选定的事件很有用 结果;具有任一体征的患者已达到更严重的阶段 痴呆症比没有这些发现的患者更早发生。 我们也 开始定义一系列由于以下原因而发生的结果 疾病过程。 在当前的提案中，我们将验证并 扩大阿尔茨海默病的预测模型。 这项研究将在三个研究地点进行，样本为 240 名 发病后1至3年内的患者。 标准化定期 6 个月评估将在 所有科目均在第一年入学。 我们将利用一个 一系列与临床相关且有意义的结果 患者和家属。 新的群体将会更大，给我们带来 更大的力量，并将以改善的方式收集 我们完善预测技术并提高其预测能力的能力 准确性。 改善患者临床预测的最终目标 管理。 这项研究的预后意义将是 对未来治疗路径的设计非常感兴趣。 拟议的研究还将有助于提高效率和 医生判断的准确性。