Developing Robust Chronic Critical Illness Risk Models

开发稳健的慢性危重疾病风险模型

• 批准号: 8979823
• 负责人: Steven S Henley
• 金额: $ 22.5万
• 依托单位: MARTINGALE RESEARCH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8979823
• 关键词: Accounting; Administrator; Algorithm Design; Blunt Trauma; Catabolism; Chronic; Clinical; Clinical Data; Cohort Studies; Complex; Computer software; Critical Illness; Data; Data Set; Development; Disease; Economic Inflation; Etiology; Evaluation; Event; Feasibility Studies; Foundations; Functional disorder; Health; Health Care Costs; Healthcare; Hemorrhagic Shock; Housing; Immunosuppression; Inflammation; Injury; Intensive Care Units; Investigation; Lead; Literature; Logistic Regressions; Marketing; Methodology; Methods; Modeling; National Institute of General Medical Sciences; Operative Surgical Procedures; Organ; Outcome; Pathology; Patients; Performance; Phase; Policy Maker; Population; Process; Quality of life; Reporting; Research; Research Design; Research Personnel; Risk; Sample Size; Sampling; Sampling Biases; Scientist; Selection Criteria; Sepsis; Series; Severity of illness; Statistical Methods; Statistical Models; Syndrome; Technology; Testing; Time; Trauma; Trauma patient; United States; Validation; clinical predictors; commercialization; cost; design; improved; injured; innovation; model development; models and simulation; mortality; novel; phase 1 study; prevent; prognostic; prospective; prototype; public health relevance; simulation; software development; statistics; technology development; therapy design

 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.

 描述（由申请人提供）：慢性危重病（CCI）导致在重症监护室的长期停留，降低生活质量，每年增加近200亿美元的健康相关成本，并且是其他疾病的前兆，如持续性炎症，免疫抑制和卡他卡他综合征（PICS）。在任何时候，仅在美国就有超过10万名患者患有CCI。手术创伤患者的CCI定义为重症监护室停留大于或等于14天，并有持续器官功能障碍的证据。现有的CCI临床数据的可用性提供了机会， 应用先进的统计方法，为创伤和脓毒症研究开发强大的患者水平CCI风险模型。更有效的风险模型对从业者、管理者和政策制定者来说是非常宝贵的，并导致更好的决策，从而提高患者的生活质量，降低医疗保健成本。尽管广泛使用用于临床事件的患者级预测模型（例如，死亡率来计算疾病严重程度评分）患者水平的CCI风险模型目前不可用。此外，统计学的最新进展可以应用于稳健风险 健康护理研究人员不容易获得潜在病理的建模。因此，利用改进的统计方法来开发CCI风险模型，可以揭示疾病的病因，而不仅仅是预测发病，这将大大有助于科学家理解， 评估、预防和治疗CCI。这项I期研究调查了应用最佳近似模型（BAM）方法在NIGMS赞助的数据集上为严重受伤的钝性创伤患者群体开发CCI改进风险模型的可行性。BAM方法是一种系统的模型开发方法，它结合了鲁棒估计，规范分析，随机/穷举模型搜索和模型验证，在广义加法模型的单一模型选择/验证框架内。BAM旨在处理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、缺失值和过拟合;以及多重共线性、小样本量偏倚和由于多个模型比较而导致的I型误差膨胀。在第一阶段研究中，将使用内部BAM方法开发一个强大的CCI风险模型，然后进行一系列旨在评估其性能的模拟研究。模拟研究还将描述BAM策略的优势，以开发一个强大的CCI风险模型，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先进的II期CCI风险模型开发、评估和传播提供所需的初步研究，这反过来将为先进预后技术的III期商业化奠定必要的基础。