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.

Public Health Relevance

Trauma patients receiving massive blood transfusions (10 units or more within 24 hours) are at high risk of death (40%). Early prediction of the need for massive transfusion combined with the transfusion of blood components in more balanced ratios may substantially improve patient outcomes;however, the validity of previous study findings has been challenged by the complex, dynamic nature of patient data and limited statistical methods. New statistical methodology will be developed to enhance existing prediction models and improve the quality of comparative effectiveness research in trauma transfusion and many other clinical practices.

Agency
National Institute of Health (NIH)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21HL109479-02
Application #
8700487
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Zou, Shimian
Project Start
Project End
Budget Start
Budget End
Support Year
2
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Texas Health Science Center Houston
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
City
Houston
State
TX
Country
United States
Zip Code
77225