Statistical Methodology Development in Blood Transfusion Protocol Research

输血方案研究中统计方法的发展

• 批准号: 8445911
• 负责人: JING NING
• 金额: $ 23.07万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2015-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8445911
• 关键词: Accident and Emergency department; Address; Admission activity; Algorithms; Blood Component Transfusion; Blood Platelets; Blood Transfusion; Cessation of life; Clinical; Clinical Research; Complex; Data; Data Analyses; Data Set; Development; Early Intervention; Equilibrium; Erythrocyte Transfusion; Erythrocytes; Event; Future; Gold; Hemorrhage; Hemostatic function; Hospital Mortality; Hour; Injury; Intervention; Knowledge; Life; Literature; Measures; Methodology; Methods; Modeling; Nature; Operative Surgical Procedures; Outcome; Patients; Performance; Plasma; Platelet Transfusion; Prospective Studies; Protocols documentation; Recurrence; Reporting; Research; Research Activity; Research Design; Resources; Resuscitation; Risk; Series; Statistical Methods; Subgroup; Sum; Survival Rate; Testing; Time; Transfusion; Translational Research; Translations; Trauma; Trauma Research; United States; bench to bedside; blood product; clinical practice; comparative effectiveness; design; effectiveness research; hazard; high risk; improved; injured; innovation; insight; interest; medical attention; mortality; predictive modeling; prospective; public health relevance; simulation; trauma centers; years of life lost

DESCRIPTION (provided by applicant): In the U.S., injury is the leading cause of productive years of life lost and consumes 10-15% of all donated red blood cell (RBC) transfusions. While 25% of patients admitted to level 1 trauma centers receive at least one unit of RBCs, those receiving massive transfusion (MT), defined as 10 or more units within 24 hours, consume 71% of all RBC transfusions with a 40% risk of in-hospital mortality. Plasma and platelets (separate components) are given to 90% and 71% of MT patients, respectively. Despite high levels of recent clinical and translational research, significant gaps in knowledge and barriers remain. The most urgent include whether 1) more accurate prediction of the patients in need of MT, and 2) earlier intervention with the optimum MT protocol (i.e., sufficient volumes and ratios of plasma, platelet and RBC units) can improve patient outcomes. These unresolved issues in trauma transfusion practice persist largely because of constraints in study design (retrospective) and statistical analysis methods (standard regression modeling) that are poorly suited to the highly dynamic nature of the data. Subgrouping patients according to the standard definition of MT introduces survival bias by excluding the hemorrhaging patients who truly needed an MT protocol, but died or achieved hemostasis due to surgical or other intervention before receiving the 10th RBC unit. Survival bias also threatens previous studies because the standard use of cumulative 24 hour transfusion ratios and regression modeling of mortality cannot resolve whether the treatment prolonged survival or patients had to survive long enough to receive treatment (e.g., to achieve high plasma:platelet:RBC ratios). The use of alternate statistical strategies like time-dependent proporational hazards regression may not overcome these problems because of the potential for informative censoring and time-dependent confounding. Our objective is to address these issues by developing relevant methodology for latent class analysis and recurrent event data analysis. Two specific aims will be undertaken: 1) to develop and evaluate a latent class model to accurately identify the hemorrhaging patients who truly needed an MT and replace the existing MT definition as the gold standard in assessing the performance of prediction algorithms. Furthermore, the new gold standard will help us enhance the performance of existing predictive algorithms with the addition of new candidate predictors; and 2) to develop a multi-type recurrent event model for estimating time-dependent RBC, plasma, and platelet transfusion rates, and evaluating their impact on patient survival. The developed methods will be extensively tested by simulation studies and thereafter validated with data from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) study. Results from this research will guide the design and conduct of future comparative effectiveness research and facilitate more rapid translation of innovative improvements in MT protocols from bench to bedside. Our new statistical methods are expected to have broad application across many different clinical contexts and dynamic data sets.

描述（由申请人提供）：在美国，伤害是生命损失的主要原因，消耗了所有捐赠的红细胞（RBC）输血的10-15％。虽然25％被录取1级创伤中心的患者至少接受一个RBC，但接受大规模输血（MT）的患者在24小时内被定义为10个或更多单位，但所有RBC输血中的71％的占40％的输血风险。血浆和血小板（单独的组件）分别为90％和71％的MT患者。尽管最近的临床和转化研究很高，但知识和障碍的差距仍然存在。最紧迫的包括1）更准确地预测需要MT的患者，以及2）最佳MT方案（即血浆，血小板和RBC单位的足够体积和比率）的早期干预可以改善患者的结果。创伤输血实践中的这些未解决的问题持续存在很大程度上是由于研究设计中的限制（回顾性）和统计分析方法（标准回归模型），这些方法不适合数据的高度动态性质。根据MT的标准定义对患者进行亚组，通过排除真正需要MT方案但在接受第10个RBC单元之前由于外科手术或其他干预而死亡或达到止血的患者，引入了生存偏见。生存偏见也威胁到先前的研究，因为累积24小时输血比率的标准使用和死亡率的回归模型无法解决是否长期生存或患者必须存活足够长以接受治疗（例如，达到高血浆：达到高血浆：血小板：RBC比率）。使用替代统计策略（例如时间依赖的支持性危害回归）可能无法克服这些问题，因为可能会进行信息审查和时间依赖的混杂。我们的目标是通过开发相关的方法来解决这些问题，以进行潜在类别分析和经常性事件数据分析。将实现两个具体的目标：1）开发和评估潜在类模型，以准确确定真正需要MT并替代现有MT定义作为评估预测算法性能的金标准的患者。此外，新的黄金标准将通过增加新的候选预测指标来帮助我们提高现有预测算法的性能； 2）开发一个多型复发事件模型，用于估计时间依赖性的RBC，血浆和血小板输血率，并评估其对患者生存的影响。开发的方法将通过模拟研究对开发的方法进行广泛的测试，此后通过前瞻性观察性多中心大创伤输血（Prommtt）研究对数据进行验证。这项研究的结果将指导未来的比较有效性研究的设计和行为，并促进MT方案从长凳到床边的创新改进的更快转换。我们的新统计方法有望在许多不同的临床环境和动态数据集中具有广泛的应用。