Optimization of Bi-Pap and PEG intervention protocols for Amyotrophic Lateral Sclerosis

R 03提案-肌萎缩侧索硬化症Bi-Pap和PEG干预方案的优化 摘要/概要 使用双水平气道正压通气（Bi-Pap）作为呼吸干预的积极影响， 经皮内镜下胃肠道营养治疗在肌萎缩侧索硬化症中的应用 硬化症（ALS）已经被很好地引用。虽然存在一些关于使用Bi-Pap和PEG的临床指南， 关于感知到的最佳方案仍然存在一定程度的不确定性和明显的不一致性， 上述干预措施的实施，主要是由于小规模研究造成的结果差异 大小和患者之间固有的异质性。在此R 03应用程序中，我们建议对 我们现有的新的1，587例ALS患者临床信息学数据库，其中包括300多个不同的领域， 定量和定性测量，以根据患者情况优化Bi-Pap和PEG干预方案 特征和定量临床测量。因此，拟议项目需要进行深入分析， 数据的样本量是先前已发表文献中研究的几倍。我们利用 传统方法的组合（例如方差分析（ANOVA）、卡方检验、Kaplan-Meier）， 新的统计技术（关系分析）和大数据技术（分层聚类、贝叶斯 网络，森林图），以确定疾病进展的最佳点，这些治疗方式 以及确定最佳使用参数和结果预测因素。

R03 Proposal- Optimization of Bi-Pap and PEG intervention protocols for Amyotrophic Lateral Sclerosis Abstract/Summary The positive impact of the use of bi-level positive airway pressure (Bi-Pap) as a respiratory intervention and percutaneous endoscopic gastronomy (PEG) as nutritional support intervention in Amyotrophic Lateral Sclerosis (ALS) has been well cited. While a few clinical guidelines regarding the use of Bi-Pap and PEG exist, there remains a level of uncertainty and apparent inconsistency regarding the perceived optimal implementation of the aforementioned interventions, largely due to outcome variance caused by small study sizes and inherent heterogeneity among patients. In this R03 application, we propose secondary analysis of our existing novel 1,587-patient ALS clinical informatics database, which includes over 300 different fields of quantitative and qualitative measures, to optimize Bi-Pap and PEG intervention protocols as function of patient characteristics and quantitative in-clinic measures. As such, the proposed project entails an in-depth analysis of data featuring sample sizes several times larger than previous studies in the published literature. We utilize a combination of traditional approaches (e.g. analysis of variance (ANOVA), chi-squared test, Kaplan-Meier), novel statistical techniques (relational analysis), and big data techniques (hierarchical clustering, Bayesian Networks, Forest Plot) to identify the optimal point in disease progression at which these treatment modalities should be implemented as well as ascertain optimal usage parameters and outcome predictors.