Developing Robust Chronic Critical Illness Risk Models

 描述(申请人提供)：慢性危重病(CCI)导致在重症监护病房长时间停留，降低生活质量，每年增加近200亿美元的与健康相关的费用，是其他疾病的先兆，如持续性炎症、免疫抑制和分解代谢综合征(PICS)。在任何时候，仅在美国就有超过10万名患者患有CCI。外科创伤患者的CCI被定义为重症监护病房住院时间大于或等于14天，并有持续器官功能障碍的证据。具有CCI特征的现有临床数据的可用性提供了这样的机会 应用先进的统计学方法，为创伤和脓毒症研究开发强有力的患者水平CCI风险模型。更有效的风险模型对从业者、管理人员和政策制定者来说是无价的，并导致更好的决策，从而提高患者的生活质量并降低医疗成本。尽管临床事件的患者级预测模型被广泛使用(例如，用死亡率来计算疾病的严重程度评分)，但患者级CCI风险模型目前还不可用。此外，可以应用于稳健风险的统计方面的最新进展 对于卫生保健研究人员来说，对潜在的病理模型进行建模并不容易。因此，利用改进的统计学方法开发CCI风险模型，不仅可以预测发病，还可以揭示疾病的病因，这将极大地帮助科学家理解， 评估、预防和治疗CCI。这项第一阶段的研究调查了应用最佳近似模型(BAM)方法在NIGMS赞助的数据集上为一组严重损伤的钝性创伤患者开发改进的CCI风险模型的可行性。BAM方法是一种系统的模型开发方法，它在广义可加模型的单一模型选择/验证框架内结合了稳健估计、规范分析、随机/穷举模型搜索和模型验证。BAM旨在处理在开发预测风险模型时遇到的常见问题，包括可能的模型错误指定、缺值和过度拟合；以及多重共线性、小样本大小偏差和由于多个模型比较而导致的I型误差膨胀。在这项第一阶段的研究中，将使用内部BAM方法开发一个稳健的CCI风险模型，随后将进行一系列模拟研究，以评估其性能。模拟研究还将表征BAM策略在开发稳健的CCI风险模型方面的优势，而不是传统的统计方法，如逐步回归。可行性研究结果将为更高级的第二阶段CCI风险模型的开发、评估和传播提供所需的初步研究，这反过来又将为先进预测技术的第三阶段商业化奠定必要的基础。

 DESCRIPTION (provided by applicant): Chronic critical illness (CCI) leads to extended stays in intensive care units, reduces quality of life, adds nearly $20 billion annually in health-relate costs, and is a precursor to other conditions such as persistent inflammation, immunosuppression, and catabolism syndrome (PICS). At any one time, more than 100,000 patients suffer from CCI in the United States alone. CCI for surgical trauma patients is defined as an intensive care unit stay greater than or equal to 14 days with evidence of ongoing organ dysfunction. The availability of existing clinical data characterizing CCI provides the opportunity to apply advanced statistical methods to develop robust patient-level CCI risk models for trauma and sepsis research. More effective risk models are invaluable to practitioners, administrators, and policy makers, and lead to better decisions resulting in increased patient quality of life and reduced health care costs. Despite widespread use of patient-level prediction models for clinical events (e.g., mortality to compute severity of illness scores) patient-level CCI risk models are not currently available. Moreover, recent advances in statistics that can be applied to robust risk modeling of underlying pathologies are not easily accessible to health care researchers. Thus, utilizing improved statistical methods for developing a CCI risk model that can reveal the etiology for the disease, not merely predict onset, would significantly help scientists understand, assess, prevent, and treat CCI. This Phase I study investigates the feasibility of applying a Best Approximating Model (BAM) method to develop improved risk models for CCI on a NIGMS-sponsored dataset for a population of severely injured blunt trauma patients. The BAM method is a systematic model development approach that combines robust estimation, specification analyses, stochastic/exhaustive model search, and model validation within the single model selection/validation framework of a generalized additive model. A BAM is designed to handle common problems encountered in developing predictive risk models including possible model misspecification, missing values, and overfitting; as well as multicollinearity, small sample size bias, and Type I error inflation due to multiple model comparisons. In this Phase I study, a robust CCI risk model will be developed using an in-house BAM method, followed by a series of simulation studies designed to evaluate its performance. The simulation studies will also characterize the advantages of the BAM strategy for developing a robust CCI risk model over conventional statistical methods such as stepwise regression. Feasibility study results will provide the preliminary research needed for more advanced Phase II CCI risk model development, evaluation, and dissemination that, in turn, will establish the essential foundation for Phase III commercialization of an advanced prognostic technology.