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Predictors of Respiratory Insufficiency in Amyotrophic Lateral Sclerosis

肌萎缩侧索硬化症呼吸功能不全的预测因素

基本信息

批准号：
9610247
负责人：
Jason Ackrivo
金额：
$ 6.96万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9610247
关键词：
Age Amyotrophic Lateral Sclerosis Anatomy Biometry Body mass index Breathing Caring Cessation of life Characteristics Chronic Clinic Visits Clinical Clinical Trials Clinical Trials Database Clinical Trials Design Communication Coughing Data Data Set Development Diagnosis Disease Dyspnea Early Intervention Early identification Environmental air flow Epidemiology Evolution Functional disorder Goals Guidelines Health system Hypercapnic respiratory failure Individual Institutes Intervention Intervention Studies K-Series Research Career Programs Knowledge Lead Location Logistic Regressions Lung infections Maintenance Measures Methodology Modeling Morbidity - disease rate Muscle Muscle Weakness Neurodegenerative Disorders Neurologic Neurology Outcome Outcome Study Participant Patient Care Patient-Focused Outcomes Patients Pattern Pennsylvania Phase Phenotype Prevention approach Probability Progressive Disease Prospective cohort Quality of life Research Research Personnel Research Training Resources Respiratory Failure Respiratory Insufficiency Respiratory Muscles Respiratory physiology Risk Risk Factors Secondary Prevention Severities Spirometry Symptoms Time Uncertainty Universities Variant Vital capacity Work base cohort evidence based guidelines high risk improved innovation mortality muscle strength nervous system disorder novel personalized medicine predictive modeling prospective pulmonary function rate of change reduced muscle strength respiratory symptomatic improvement time use

项目摘要

Project Summary/Abstract Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with high morbidity and universal mortality due to respiratory failure. The onset of respiratory weakness heralds an increased risk of aspiration related to bulbar muscle weakness and ineffective cough, hypercapnic respiratory failure due to chronic hypoventilation, and pulmonary infections, ultimately leading to death. Despite the key role of respiratory failure in the morbidity and mortality associated with ALS, there remains uncertainty concerning optimum initiation and maintenance of respiratory care for this disease. ALS has a very heterogeneous clinical presentation and symptom progression, which causes variable evolution of respiratory involvement. Given the significance of respiratory morbidity with this disease combined with the unclear timing, a better understanding of the risk factors for onset and progression of respiratory muscle weakness could improve quality of life and even survival in ALS. Unfortunately, there is no prediction model for respiratory failure in ALS, no established phenotypes for ALS respiratory progression (most importantly, “rapid progressors”), and there is significant variation between clinicians and centers regarding the management of respiratory failure in ALS. A prediction model and defined phenotypes of respiratory weakness in ALS could improve timeliness of interventions, facilitate communication, inform clinical trial design, and elucidate novel disease mechanisms. The goals of this study are to identify predictors of onset of respiratory failure and to identify distinct phenotypes of rapidity and pattern of the progression of respiratory dysfunction. First, I aim to develop and validate a prediction model for developing respiratory insufficiency within six months of diagnosis using a logistic regression model and predictive model analytics. Second, I aim to categorize individuals based on the rate of change of respiratory muscle strength (as measured by spirometry) over time using group-based trajectory modeling. Next, I will identify group membership probabilities according to baseline characteristics using a multinomial logistic regression model. This project will provide essential preliminary data for a Career Development Award that will (1) examine the impact of risk of respiratory failure on short and long-term outcomes of patients with ALS, (2) prospectively validate the prediction model, (3) develop an intervention to mitigate patient-centered outcomes associated with respiratory insufficiency in ALS, and (4) identify which characteristics are associated with different trajectories of respiratory function, thus allowing for personalized medicine.

项目总结/摘要肌萎缩侧索硬化症（ALS）是一种进行性神经退行性疾病，由于呼吸衰竭而普遍死亡。呼吸无力的发作预示着与球肌无力和无效咳嗽相关的误吸，由于慢性换气不足和肺部感染最终导致死亡尽管关键作用呼吸衰竭的发病率和死亡率与ALS相关，仍然存在不确定性，最佳的启动和维持呼吸护理这种疾病。ALS具有非常异质性的临床表现和症状进展，这导致呼吸系统受累的可变演变。鉴于呼吸道发病的意义与本病合并时间不清，需进一步了解呼吸肌无力发作和进展的危险因素可以改善生活质量，甚至是在ALS中的生存。不幸的是，目前还没有ALS呼吸衰竭的预测模型， ALS呼吸进展的表型（最重要的是，“快速进展者”），临床医生和医疗中心之间关于ALS呼吸衰竭管理的差异。预测模型和定义的ALS呼吸无力表型可以提高干预的及时性，促进沟通，为临床试验设计提供信息，并阐明新的疾病机制。本研究的目的是确定呼吸衰竭发作的预测因素，呼吸功能障碍进展的速度和模式的表型。首先，我的目标是发展和使用以下方法验证诊断后6个月内发生呼吸功能不全的预测模型：逻辑回归模型和预测模型分析。其次，我的目标是根据个人的使用基于组的呼吸肌强度随时间的变化率（通过肺活量测定法测量）弹道建模接下来，我将根据基线特征确定组成员资格概率使用多项逻辑回归模型。该项目将为职业发展奖提供必要的初步数据，该奖项将（1）审查呼吸衰竭风险对ALS患者短期和长期结局的影响，（2）前瞻性验证预测模型，（3）制定干预措施，以减轻以患者为中心的结果，与ALS呼吸功能不全，（4）确定哪些特征与不同的呼吸功能的轨迹，从而允许个性化医疗。