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Applying statistical methods in a registry dataset of cardiopulmonary resuscitation to predict probability of survival by chest compression time in children

机译:在心肺复苏注册表数据集中应用统计方法,通过胸部按压时间预测儿童的生存可能性

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摘要

The focus of this thesis was to apply advanced statistical methods to the American Heart Association Get With The Guidelines-Resuscitation (AHA GWTG-R) registry, a registry data set derived from a prospective multi-sites observational study, the American Heart Association’s National Registry of Cardiopulmonary Resuscitation (NRCPR). The data comprise comprehensive information related to the cardiopulmonary resuscitation (CPR) process, patients’ outcome, and characteristics of both the patients and the hospitals. The purpose of the registry data is to provide information that can be used to improve the outcomes of sudden cardiac arrest (SCA) patients and updates protocol of CPR. udThis thesis has two purposes. The first one is to investigate the relationship between the patients’ disease and survival for SCA patients receiving different durations of chest compression. The second one is to establish a model for predicting the probability of survival according to the duration of CPR. In the clinical setting, a categorized variable may provide more meaningful inferences. To explore this option, a Generalized additive model (GAM) was used to identify cutoff points for the categorization of chest compression duration. This categorized variable was then used for the development of prediction models for survival and the Net reclassification index (NRI) was used to select the appropriate predictors for this model. Logistic regression, generalized estimating equations (GEE), and a generalized linear mixed model (GLMM) were performed to obtain the estimates of parameters. Thereafter, the probability of survival was estimated based on the results of the regression model. udComprehensive registry data have been established for many healthcare problems, which include many observations and variables. A systematic process to analyze registry data is necessary. This thesis used multiple statistical techniques to create meaningful variables, select appropriate predictors, fit regression models, and predict the probabilities of outcome. The public health significance of this thesis is the identification of subgroups of SCA patients who may benefit from prolonged CPR duration and to assess significance of cluster effects in the registry data.
机译:本论文的重点是将先进的统计方法应用于美国心脏协会的《复苏指南》(AHA GWTG-R)注册中心,该注册中心数据来自一项前瞻性多站点观察研究,即美国心脏协会的国家注册中心。心肺复苏术(NRCPR)。数据包含与心肺复苏(CPR)过程,患者结果以及患者和医院特征有关的综合信息。注册表数据的目的是提供可用于改善心脏骤停(SCA)患者预后并更新CPR协议的信息。 ud本论文有两个目的。第一个是研究接受不同胸部按压持续时间的SCA患者的疾病与生存之间的关系。第二个是建立根据心肺复苏持续时间预测生存概率的模型。在临床环境中,分类变量可能会提供更有意义的推论。为了探讨该选项,使用通用加性模型(GAM)来确定胸外按压持续时间分类的临界点。然后,将该分类变量用于生存预测模型的开发,并使用净重分类指数(NRI)为该模型选择适当的预测因子。执行Logistic回归,广义估计方程(GEE)和广义线性混合模型(GLMM),以获得参数的估计。此后,根据回归模型的结果估计生存的可能性。 ud已针对许多医疗保健问题建立了全面的注册表数据,其中包括许多观察值和变量。必须有系统的过程来分析注册表数据。本文使用多种统计技术来创建有意义的变量,选择适当的预测变量,拟合回归模型并预测结果的概率。本文的公共卫生意义是确定可从延长的心肺复苏持续时间中受益的SCA患者亚组,并评估注册表数据中簇效应的重要性。

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    Huang Hsin-Hui;

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  • 年度 2014
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