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Original Article | ONLINE FIRST

The Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) Study:  Comparative Effectiveness of a Time-Varying Treatment With Competing Risks FREE

John B. Holcomb, MD; Deborah J. del Junco, PhD; Erin E. Fox, PhD; Charles E. Wade, PhD; Mitchell J. Cohen, MD; Martin A. Schreiber, MD; Louis H. Alarcon, MD; Yu Bai, MD, PhD; Karen J. Brasel, MD, MPH; Eileen M. Bulger, MD; Bryan A. Cotton, MD, MPH; Nena Matijevic, PhD; Peter Muskat, MD; John G. Myers, MD; Herb A. Phelan, MD, MSCS; Christopher E. White, MD; Jiajie Zhang, PhD; Mohammad H. Rahbar, PhD; for the PROMMTT Study Group
[+] Author Affiliations

Author Affiliations: Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery (Drs Holcomb, del Junco, Wade, Cotton, and Matijevic) and Department of Pathology and Laboratory Medicine (Dr Bai), Medical School, Biostatistics/Epidemiology/Research Design Core, Center for Clinical and Translational Sciences (Drs del Junco, Fox, and Rahbar), School of Biomedical Informatics (Dr Zhang), and Division of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health (Dr Rahbar), University of Texas Health Science Center at Houston; Division of General Surgery, Department of Surgery, School of Medicine, University of California, San Francisco (Dr Cohen); Division of Trauma, Critical Care, and Acute Care Surgery, School of Medicine, Oregon Health and Science University, Portland (Dr Schreiber); Division of Trauma and General Surgery, Department of Surgery, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania (Dr Alarcon); Division of Trauma and Critical Care, Department of Surgery, Medical College of Wisconsin, Milwaukee (Dr Brasel); Division of Trauma and Critical Care, Department of Surgery, School of Medicine, University of Washington, Seattle (Dr Bulger); Division of Trauma/Critical Care, Department of Surgery, College of Medicine, University of Cincinnati, Cincinnati, Ohio (Dr Muskat); Division of Trauma, Department of Surgery, School of Medicine, University of Texas Health Science Center at San Antonio (Dr Myers) and Department of Surgery, Brooke Army Medical Center, Fort Sam Houston (Dr White), San Antonio; and Division of Burn/Trauma/Critical Care, Department of Surgery, Medical School, University of Texas Southwestern Medical Center at Dallas (Dr Phelan).


JAMA Surg. 2013;148(2):127-136. doi:10.1001/2013.jamasurg.387.
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Objective To relate in-hospital mortality to early transfusion of plasma and/or platelets and to time-varying plasma:red blood cell (RBC) and platelet:RBC ratios.

Design Prospective cohort study documenting the timing of transfusions during active resuscitation and patient outcomes. Data were analyzed using time-dependent proportional hazards models.

Setting Ten US level I trauma centers.

Patients Adult trauma patients surviving for 30 minutes after admission who received a transfusion of at least 1 unit of RBCs within 6 hours of admission (n = 1245, the original study group) and at least 3 total units (of RBCs, plasma, or platelets) within 24 hours (n = 905, the analysis group).

Main Outcome Measure In-hospital mortality.

Results Plasma:RBC and platelet:RBC ratios were not constant during the first 24 hours (P < .001 for both). In a multivariable time-dependent Cox model, increased ratios of plasma:RBCs (adjusted hazard ratio = 0.31; 95% CI, 0.16-0.58) and platelets:RBCs (adjusted hazard ratio = 0.55; 95% CI, 0.31-0.98) were independently associated with decreased 6-hour mortality, when hemorrhagic death predominated. In the first 6 hours, patients with ratios less than 1:2 were 3 to 4 times more likely to die than patients with ratios of 1:1 or higher. After 24 hours, plasma and platelet ratios were unassociated with mortality, when competing risks from nonhemorrhagic causes prevailed.

Conclusions Higher plasma and platelet ratios early in resuscitation were associated with decreased mortality in patients who received transfusions of at least 3 units of blood products during the first 24 hours after admission. Among survivors at 24 hours, the subsequent risk of death by day 30 was not associated with plasma or platelet ratios.

Figures in this Article

Injury is increasing in incidence, the second leading cause of death worldwide, and the leading cause of years of life lost in the United States.1,2 Uncontrolled hemorrhage after injury is the leading cause of potentially preventable death.39 As opposed to other major causes of traumatic death (eg, traumatic brain injury and multiple organ failure), hemorrhagic deaths occur quickly and are frequently associated with massive transfusion (MT) (traditionally defined as ≥10 units of red blood cells [RBCs] in 24 hours).10,11 Current transfusion practices consist of infusing crystalloid, RBCs, plasma, and platelets and date back to the 1970s when separation of donated whole blood into its component parts became commonplace.1216

Quiz Ref IDA new resuscitation strategy, termed damage control resuscitation, is challenging the status quo.17 The term originated in the US military and refers to the guidelines developed for combat casualties with substantial bleeding in Iraq and Afghanistan. Among other interventions, this approach recommends earlier and more balanced transfusion of plasma and platelets along with the first units of RBCs (ie, maintaining plasma:platelet:RBC ratios closer to the 1:1:1 ratio of whole blood) while simultaneously minimizing crystalloid use1827 in patients to avert or reverse the triad of coagulopathy, acidosis, and hypothermia25,2830 and decrease endothelial permeability.3133

Quiz Ref IDConflicting findings regarding the association between transfusion ratios closer to 1:1 and survival in MT trauma patients have been reported29,3436 and attributed to multiple issues, including survival bias.34,35,37,38 Survival bias, also known as reverse causation, is a prevalent, important, and often neglected problem in clinical observational studies, systematic reviews, and comparative effectiveness research.39,40 In trauma resuscitation research, the conundrum of reverse causation is whether treatment caused patients to survive longer or patients received treatment only because they survived long enough. Without compelling evidence to guide uniform transfusion practice for trauma patients with substantial bleeding after injury, considerable variation persists across level I trauma centers.14,19,41

Using prospective, minute-to-minute observational data from 10 level I trauma centers, our objectives were to accurately describe when RBCs, plasma, and platelets were infused and to assess the association between in-hospital mortality and the timing and amount of blood products. One purpose of observational clinical studies is to inform the design of future randomized trials, and exploratory analysis can provide critical information regarding trial feasibility, realistic estimates of expected effect size, and unique insights from real-world health care settings. Thus, we describe the rationale, results, and lessons learned from our exploratory analyses of Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) study data.42 We hypothesized that early transfusion of plasma and platelets in higher ratios would be associated with decreased in-hospital mortality in bleeding patients.

STUDY SAMPLES

Quiz Ref IDThe PROMMTT Study was a prospective, multicenter, observational cohort study conducted at 10 level I trauma centers in the United States. At each study site and the Data Coordinating Center, the local institutional review board approved the study. The US Army Human Research Protections Office provided a second-level review and approval.42

Quiz Ref IDTrauma patients were enrolled in the PROMMTT Study and data collection was begun at emergency department arrival. Patients were eligible if they required the highest level of trauma activation, were aged 16 years or older, and received a transfusion of at least 1 unit of RBCs in the first 6 hours after admission. Patients were excluded if they met the following criteria: (1) were transferred from other facilities; (2) were declared dead within 30 minutes of admission; (3) had received more than 5 minutes of cardiopulmonary resuscitation prior to or within 30 minutes of admission; (4) were prisoners; (5) had a burn injury of more than 20% of the total body surface area; (6) had inhalation injury as diagnosed by bronchoscopy; or (7) were pregnant. If ineligibility was first identified sometime after enrollment, the patient was withdrawn from the study and postenrollment data were destroyed. No changes in clinical practice were implemented in this observational study. All participating centers had MT protocols in place.42

DATA COLLECTION AND MANAGEMENT

Standard operating procedure manuals were developed and site coordinators were trained in a series of meetings. Research assistants available at all hours screened and enrolled patients, recording the exact times of fluid infusion and blood product transfusion as well as patient outcomes during direct observation. Direct bedside observation began at trauma team activation and continued until active resuscitation ended (defined as the time the center transfusion protocol was discontinued, death occurred, or 2 hours elapsed since the last blood product transfusion, whichever came first). After direct observation ended, new interventions, complications, and outcomes were recorded daily while the patient was in the intensive care unit and weekly thereafter during hospitalization. Cause of in-hospital death was ascribed by individual site clinicians without confirmation or central adjudication. Sites of bleeding were ascertained by data collectors. The Data Coordinating Center audited study data for missing values and outliers.42 Some severely injured patients did not undergo routine baseline assessments (eg, base deficit, temperature, international normalized ratio, pH) owing to the emergent nature of their injuries (Table 1).

Table Graphic Jump LocationTable 1. Admission and Treatment Characteristics and Unadjusted Survival in 1245 Prospective, Observational, Multicenter, Major Trauma Transfusion Study Patients
STATISTICAL ANALYSIS

The primary outcome of interest was in-hospital mortality. In the original analysis plan, the primary independent variables were single plasma:RBC and platelet:RBC transfusion ratios.42 Under the assumption that each patient would receive constant ratios of plasma and platelets during the period of active resuscitation, the PROMMTT Study was designed to enroll 1200 transfused and 300 MT patients. Previous retrospective studies suggested that higher plasma and platelet ratios occurred in about 25% to 50% of MT patients19 and were associated with at least a 50% decrease in mortality relative to lower ratios.19,23,43 Thus, at the α = .05 significance level, a total of at least 300 PROMMTT Study MT patients was expected to provide 80% power44 to detect differences of at least 50% in mortality between 2 groups of patients classified by transfusion ratios (ratios closer to 1:1 vs ratios closer to 1:2).

Previous retrospective trauma transfusion studies have focused on the subgroup of MT patients, effectively excluding bleeding patients who did not survive long enough to receive 10 RBC units and heightening the concern for survival bias.19,37 Finding reliable and immediate indicators for patients' blood loss and continuing hemorrhage rates is a challenge in trauma transfusion practice and research.45 Cumulative counts of patients' total RBC units received within 6 to 24 hours (especially to identify the MT subgroup) remain a standard, but poor, surrogate. Soon after the PROMMTT Study began, we realized the need to revise the original analysis plan to account for heterogeneity among patients (eg, variations in the severity of blood loss and rates of continuing hemorrhage) and trauma centers (eg, variations in blood product availability, MT protocols, and blood bank to bedside transit times).3437 We therefore sought an exploratory approach to analysis that would incorporate the requirements for time-dependent and multilevel techniques and thereby reduce the potential for bias.

To test the hypothesis that plasma:RBC and platelet:RBC ratios closer to 1:1 were independently and jointly associated with lower in-hospital mortality than transfusion ratios closer to 1:2, we reasoned that only PROMMTT Study patients surviving long enough to receive at least 3 blood product units (including 1 unit of RBCs) should be eligible to be included in the analysis. Patients who had received a transfusion of less than 3 units by hour 24 (or death) had no opportunity to attain 1:1 ratios for both plasma:RBCs and platelets:RBCs (ie, the same ratios as whole blood). Follow-up time at risk of death for each patient began at minute 31 or the start of the third unit transfused, whichever occurred last because eligible PROMMTT Study patients had to survive the first 30 minutes after admission and long enough to receive at least 3 blood product units. Cumulative ratios of plasma:RBCs and platelets:RBCs and summed counts of blood products transfused were computed at baseline (entry to follow-up) and for up to 14 consecutive time intervals: (1) two 15-minute intervals between minute 31 and hour 1; (2) ten 30-minute intervals between more than 1 and 6 hours; (3) one 18-hour interval between more than 6 and 24 hours; and (4) one 29-day interval between more than 24 hours and 30 days. The timing of transfusion was defined by the time of initiation of each transfusion. Cell-saver transfusions were not enumerated or combined with donor blood products in these analyses.

We first examined whether transfusion ratios among PROMMTT Study patients in the analysis cohort were constant across time by using mixed linear regression models for both continuous plasma:RBC and platelet:RBC ratios. We then performed multilevel time-dependent Cox proportional hazards regression that uses time as a continuous variable to accommodate the following: (1) varying entry times for this dynamic analysis cohort; (2) time-varying cumulative sums of transfusion, plasma:RBC ratios, and platelet:RBC ratios; (3) important patient baseline covariates; and (4) any residual variation in mortality rates due to unmeasured center influences. Center random effects were assessed using shared frailty, which assumed a single hazard factor (eg, unmeasured clinical practices) for each trauma center shared by all of its patients. Hazard ratios (as an estimate of standard relative risk), 95% CIs, and P values were estimated.

Similar to previous retrospective studies of the association between transfusion ratios and in-hospital mortality among trauma patients,19 our initial time-dependent Cox analysis spanned the entire follow-up period of 30 days, and a separate analysis focused on the first 24 hours after emergency department admission. The proportional hazards assumption was tested using Schoenfeld residuals for each covariate and the global test proposed by Grambsch et al.46 Results from these tests suggested significant violations of the assumptions underlying the Cox models for both the full 30-day period (global test, P < .001) and the first 24 hours of follow-up (global test, P < .001), so subsequent analyses are presented in 3 intervals (30 minutes to 6 hours, >6 hours to 24 hours, and >24 hours to 30 days). In the models stratified by these time intervals, the proportional hazards assumptions were not violated (global test, P = .13, .48, and .40, respectively). Because transfusions were generally completed by 6 hours, only the proportional hazards model for the first interval (30 minutes to 6 hours) included time-dependent covariates.

We applied purposeful variable selection strategies47 that retained in all models the plasma and platelet ratios as the primary independent variables of interest and the sum of transfusions, age, time interval at cohort entry, and Injury Severity Score as the primary potential confounders of interest. The remaining covariates of head, chest, and limb bleeding sites were retained in all models because they were significant at the α = .05 level and changed the magnitude of the plasma or platelet ratio coefficients by more than 20% when compared with models excluding them for 1 or more of the separate time intervals examined. The other candidate covariates listed in Table 1 did not change the magnitude of the plasma or platelet ratio coefficients by more than 20% and were not significant when compared with models excluding them; they were therefore not retained in the final models.48 No interactions (each transfusion ratio multiplied by the alternative ratio or a primary covariate) were significant at the α = .05 level. The transfusion ratios were also modeled categorically using clinically relevant cut points. The lowest ratios (<1:2) defined the reference group; ratios of 1:2 or higher and of less than 1:1 defined the moderate group; and ratios of 1:1 or higher defined the high group. Patients discharged in less than 30 days were censored alive at 30 days.

All analyses were performed using SAS/STAT version 9.2 statistical software for Windows (SAS Institute, Inc) and Stata/MP version 11 statistical software (StataCorp LP). Manuscript preparation was guided by the Strengthening the Reporting of Observational Studies in Epidemiology statement for the reporting of cohort studies in epidemiology49 and the Standards for Quality Improvement Reporting Excellence standards for the reporting of improvement studies in health care.50

There were 34 362 trauma admissions in the 10 centers over an average of 58 weeks. Data collection was initiated for 12 560 patients; of these, 11 315 became ineligible and were withdrawn from the study and 1245 met all PROMMTT Study eligibility criteria. Of these, 905 received a transfusion of 3 or more units of blood products, thus meeting the eligibility criteria for the analysis cohort (eFigure). Overall in-hospital mortality was 21% for all 1245 transfused patients and 25% for patients included in the analysis cohort (Table 1).

Among cohort patients, 94% of hemorrhagic deaths occurred within 24 hours, the majority of these deaths (60%) occurred within 3 hours of admission (Table 2), and the median time to hemorrhagic death was 2.6 hours (interquartile range, 1.7-5.4 hours). The principal causes of in-hospital death after 24 hours were multiple organ failure and brain injury.

Table Graphic Jump LocationTable 2. Distribution of Reported Cause of Death for Decedent Patients in the Analysis Cohort by the Time Period of Deatha

Neither plasma:RBC nor platelet:RBC ratios were constant across the first 24 hours among individual patients (Figure 1) (P < .001 for each patient in the analysis cohort). The time-varying nature of plasma and platelet transfusion practice across the analysis cohort is illustrated in Figure 2. Quiz Ref IDThirty minutes after admission, 67% of cohort patients had not received plasma, while 99% had not received platelets. Three hours after admission (the peak time of hemorrhagic death), 10% of surviving cohort patients had not received any plasma, while 28% of survivors had not received platelets. For each successive hour survived (up to hour 6), patients were more likely to receive plasma and platelets and hence were more likely to approach ratios of 1:1. By 30 minutes, 1 hour, 2 hours, 3 hours, and 6 hours after admission, ratios exceeded 1:2 in 29%, 47%, 69%, 78%, and 84% of surviving cohort patients for plasma and in 1%, 14%, 40%, 60%, and 80% for platelets, respectively.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Blood product use in the first 6 hours in 2 Prospective, Observational, Multicenter, Major Trauma Transfusion Study patients. Patient 1 (A) had an Injury Severity Score of 48 and died of hemorrhage at 1 hour 7 minutes after emergency department admission. Patient 2 (B) had an Injury Severity Score of 57 and was discharged to another acute care hospital at 27 days. Note the constantly changing ratios over time. For example, patient 1 received cumulative plasma:platelet:red blood cell (RBC) ratios of 0:0:1, 0:0:3, 0:0:6, 4:6:6, and 5:6:6 at 15, 30, 45, 60, and 75 minutes, respectively, while patient 2 received cumulative plasma:platelet:RBC ratios of 0:0:1, 0:0:4, 0:0:4, 2:0:6, and 2:0:10 at those same times.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. The bars represent cumulative ratios at the start of each time interval. Most patients received a plasma:red blood cell (RBC) ratio of 1:2 or higher by 3 hours (A) and a platelet:RBC ratio of 1:2 or higher by 6 hours (B). In the last time interval (24 hours), the percentage of patients receiving 0 units of platelets or plasma increases, reflecting the dynamic cohort with newly eligible patients entering and others exiting owing to death in the previous interval.

The protective association between higher transfusion ratios and mortality in the first time interval (minute 31 to hour 6) diminished during the next 2 time intervals (Table 3). The trend for plasma ratios suggested that the decreased mortality risk observed during the first 6 hours (adjusted hazard ratio = 0.31; P = <.001) switched direction and became nonsignificant by the final follow-up period of more than 24 hours to 30 days (adjusted hazard ratio = 1.21; P = .20). The association between the platelet:RBC ratio and mortality remained below the null but was not significant for either of the later periods. Additionally, bleeding from the chest was associated with higher mortality during the first 6 hours; in contrast, among patients who survived longer than 6 hours, bleeding from the chest was associated with lower mortality.

Table Graphic Jump LocationTable 3. Multivariable Cox Regression Models Examining the Association of Plasma and Platelet Transfusion Ratios With In-hospital Mortality

To facilitate clinical use, we repeated the same Cox models but substituted patients' continuous transfusion ratio values with 3 categorical ones (Table 3). In the initial 6-hour interval, patients in the moderate- and high-ratio groups had lower mortality rates than the low-ratio group (P < .001 for each of the higher plasma ratio groups; P = .04 for the high platelet ratio group). In both subsequent intervals, mortality among survivors was not associated with the categorical ratios.

In-hospital mortality among 1245 trauma patients receiving at least a single unit of RBCs within 6 hours of admission was 21% (Table 1), while cohort patients with 3 or more units transfused had in-hospital mortality of 25%, among the highest of any acute surgical disease process. The major findings were that patients did not receive a constant ratio during the period of active resuscitation and that early infusion of higher plasma and platelet ratios was associated with decreased mortality within 6 hours of admission, during which 81% of the hemorrhagic deaths had occurred (Table 2).

The protective association between higher transfusion ratios and in-hospital mortality appears strongest within 6 hours and diminishes over time as the primary causes of mortality shift from exsanguination to head injury, respiratory distress, organ failure, and infection after the first 24 hours. These time trends reflect heterogeneity as the dynamic cohort of injured patients changes during the course of hospitalization in composition and risk profile owing to mortality. Survivors avoiding early hemorrhage-related mortality face the longer-term competing risks of death from complications (eg, multiple organ failure) or multiple injuries (eg, head injury). The significant protective association between higher blood product ratios and mortality that we observed was concentrated in the first 24 hours for plasma and the first 6 hours for platelets. Thereafter, during the later time periods of high competing risks for nonhemorrhagic causes of death among severely injured patients, plasma and platelet ratios were not significantly associated with mortality.

Survival bias may have threatened previous studies that used (1) the traditional definition of MT and therefore excluded patients who had substantial bleeding but died early19,29,34,35,51; (2) a single cumulative ratio for plasma or platelets up to the time of death or 6 to 24 hours after admission and therefore did not account for time-dependent treatment19,23,29,35,36,5255; and (3) 30-day or overall in-hospital mortality as the primary end point, which conflates competing mortality risks.19,23,28,29,3436,5156 Our prospective study design, detailed real-time data collection methods, and analysis strategies attempted to minimize the effect of survival bias.

In rapidly and substantially bleeding trauma patients, inadequate transfusion of plasma and platelets is associated with early death. However, the actual transfusion of blood products is a complicated balance between rapid recognition of need, ordering of appropriate products, product availability in the blood bank and emergency department, obtaining those products quickly, and appropriate infusion. Unless these steps are orchestrated in an integrated fashion, delayed infusion and suboptimal ratios will occur (Figure 1 and Figure 2). Clinicians must rapidly identify patients who are substantially bleeding, and several predictive algorithms have been developed to do this.5767

Once bleeding patients have been identified, constant ratios are not infused and heterogeneous transfusion practice persists (Figure 2). Clinicians at PROMMTT level I trauma centers ultimately delivered plasma ratios of 1:1 and 1:2 within 6 to 24 hours to surviving patients. However, platelet infusion lagged behind with only 72% of patients receiving platelets by hour 3, the median time to hemorrhagic death.

Stratifying by time interval and including time-dependent covariates (Table 3) revealed how early infusion and increased ratios were associated with decreased mortality (30 minutes to 6 hours). However, it is difficult to translate hazard ratios for continuous variables into a physician's order to the blood bank for the delivery of specific blood product amounts. Therefore, we created 3 clinically relevant categories and found that a 1:1 ratio of plasma and platelets was associated with decreased early mortality compared with lower ratios (Table 3).

The strengths of this study are its prospective multicenter design and teaming a dedicated Data Coordinating Center (epidemiologists, informatics experts, and biostatisticians) with a group of level I trauma centers. By identifying patients who received at least 3 units of blood products instead of focusing on MT patients, we reduced one important source of survival bias. Accurate recording of the actual timing of blood product transfusions combined with appropriate data analysis strategies addressed another source of survival bias, ie, the time-varying nature of blood transfusions and mortality. Limitations of our observational study include missing values on potentially important covariates, which are unavoidable in observational studies of severely injured trauma patients, and other unmeasured but potentially important confounders and effect modifiers (eg, the time of and rationale for physicians' orders for RBCs, plasma, and platelets). Survival was not ascertained after discharge; however, deaths within days of discharge from an acute care hospital are infrequent (<2%).68 Finally, causes of death were assigned by individual site clinicians without confirmation or central adjudication.

In summary, these prospective data suggest that the association between earlier and higher ratios of plasma and platelets and decreased in-hospital mortality is concentrated in the first 6 hours in patients with substantial bleeding. In the first 6 hours, patients with ratios lower than 1:2 were 3 to 4 times more likely to die than patients with ratios of 1:1 or higher. Among survivors at 6 hours, the subsequent risk of death by hour 24 was higher for patients with low plasma ratios. Among survivors at 24 hours, the subsequent risk of death by day 30 was not associated with plasma or platelet ratios. Furthermore, these data highlight the serious problems of survival bias and competing risks in most previous trauma resuscitation studies37,56 and emphasize the need for definitive comparative effectiveness trauma transfusion research.

Survival bias can be eliminated only in a randomized trial with appropriate design and analysis strategies. However, it can threaten even a randomized trial if study patients are stratified by postrandomization events such as the conventional MT definition. This study supports a potential net survival benefit of early and higher plasma and platelet ratios to be assessed in a randomized trial.69 Our findings offer guidance and evidence for designing a rigorous, multicenter, randomized transfusion trial by identifying the following: (1) transfusion ratios in common use at level I trauma centers; (2) well-defined end points (eg, 3, 6, and 24 hours and 30-day mortality); (3) appropriate data analysis strategies accounting for time-varying covariates; (4) effect size estimates for power and sample size considerations; (5) patients for whom interventions should be targeted; and (6) procedures that promote integrated, consistent transfusion practices across individual clinicians, blood banks, research teams, and trauma centers.

Correspondence: John B. Holcomb, MD, Center for Translational Injury Research, Division of Acute Care Surgery, Department of Surgery, University of Texas Health Science Center at Houston, 6410 Fannin, Ste 1100, Houston, TX 77030 (john.holcomb@uth.tmc.edu).

Accepted for Publication: August 24, 2012.

Published Online: October 15, 2012. doi:10.1001/2013.jamasurg.387

Author Contributions: Drs del Junco and Fox had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Dr Rahbar served as principal investigator of the PROMMTT Study and is the senior author on this article. Study concept and design: Holcomb, del Junco, Fox, Cohen, Schreiber, Bai, Bulger, Cotton, Muskat, and Rahbar. Acquisition of data: Holcomb, Fox, Cohen, Schreiber, Brasel, Bulger, Matijevic, Muskat, Myers, Phelan, White, Zhang, and Rahbar. Analysis and interpretation of data: Holcomb, del Junco, Fox, Wade, Cohen, Alarcon, Matijevic, Muskat, Phelan, and Rahbar. Drafting of the manuscript: Holcomb, del Junco, Fox, Wade, Cotton, Muskat, and White. Critical revision of the manuscript for important intellectual content: Holcomb, del Junco, Fox, Wade, Cohen, Schreiber, Alarcon, Bai, Brasel, Bulger, Cotton, Matijevic, Muskat, Myers, Phelan, Zhang, and Rahbar. Statistical analysis: Holcomb, del Junco, Fox, and Rahbar. Obtained funding: Holcomb, del Junco, and Rahbar. Administrative, technical, and material support: Holcomb, del Junco, Fox, Wade, Cohen, Schreiber, Bai, Bulger, Matijevic, Myers, Phelan, White, Zhang, and Rahbar. Study supervision: Holcomb, del Junco, Fox, Cohen, Schreiber, Alarcon, Cotton, Myers, and Rahbar.

PROMMTT Study Group:Data Coordinating Center, University of Texas Health Science Center at Houston: Mohammad H. Rahbar, PhD (principal investigator), John B. Holcomb, MD (coinvestigator), Erin E. Fox, PhD (coinvestigator and study coordinator), Deborah J. del Junco, PhD (coinvestigator), Bryan A. Cotton, MD, MPH (coinvestigator), Charles E. Wade, PhD (coinvestigator), Jiajie Zhang, PhD (coinvestigator), Nena Matijevic, PhD (coinvestigator), Yu Bai, MD, PhD (coinvestigator), Weiwei Wang, PhD (coinvestigator), Jeanette Podbielski, RN (study coordinator), Sarah J. Duran, MSCIS (data manager), Ruby Benjamin-Garner, PhD (data manager), and Robert J. Reynolds, MPH (data manager); PROMMTT clinical sites: Brooke Army Medical Center, Fort Sam Houston, San Antonio, Texas: Christopher E. White, MD (principal investigator), Kimberly L. Franzen, MD (coinvestigator), and Elsa C. Coates, MS, RN (study coordinator); Medical College of Wisconsin, Milwaukee: Karen J. Brasel, MD, MPH (principal investigator), and Pamela Walsh (study coordinator); Oregon Health and Science University, Portland: Martin A. Schreiber, MD (principal investigator), Samantha J. Underwood, MS (study coordinator), and Jodie Curren, RN, BSN (study coordinator); University of California, San Francisco: Mitchell J. Cohen, MD (principal investigator), M. Margaret Knudson, MD (coinvestigator), Mary Nelson, RN, MPA (study coordinator), and Mariah S. Call, BS (study coordinator); University of Cincinnati, Cincinnati, Ohio: Peter Muskat, MD (principal investigator), Jay A. Johannigman, MD (coinvestigator), Bryce R. H. Robinson, MD (coinvestigator), Richard Branson (coinvestigator), Dina Gomaa, BS, RRT (study coordinator), and Cendi Dahl (study coordinator); University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania: Louis H. Alarcon, MD (principal investigator), Andrew B. Peitzman, MD (coinvestigator), Stacy D. Stull, MS, CCRC (study coordinator), Mitch Kampmeyer, MPAS, CCRC, PA-C (study coordinator), Barbara J. Early, RN, BSN, CCRC (study coordinator), Helen L. Shnol, BS, CRC (study coordinator), Samuel J. Zolin, BS (research associate), and Sarah B. Sears, BS (research associate); University of Texas Health Science Center at Houston: John B. Holcomb, MD (coprincipal investigator), Bryan A. Cotton, MD, MPH (coprincipal investigator), Marily Elopre, RN (study coordinator), Quinton M. Hatch, MD (research associate), Michelle Scerbo (research associate), and Zerremi Caga-Anan, MD (research associate); University of Texas Health Science Center at San Antonio: John G. Myers, MD (coprincipal investigator), Ronald M. Stewart, MD (coprincipal investigator), Rick L. Sambucini, RN, BS (study coordinator), Marianne Gildea, RN, BSN, MS (study coordinator), Mark DeRosa, CRT (study coordinator), Rachelle Jonas, RN, BSN (study coordinator), and Janet McCarthy, RN (study coordinator); University of Texas Southwestern Medical Center at Dallas: Herb A. Phelan, MD, MSCS (principal investigator), Joseph P. Minei, MD (coinvestigator), and Elizabeth Carroll, BS, BA (study coordinator); and University of Washington, Seattle: Eileen M. Bulger, MD (principal investigator), Patricia Klotz, RN (study coordinator), and Keir J. Warner, BS (research coordinator).

Financial Disclosure: Dr Holcomb has served on the board for Tenaxis, the Regional Advisory Council for Trauma, and the National Trauma Institute; has provided expert testimony for the Department of Justice; and has received grants funded by Haemonetics Corp and KCI USA, Inc and consultant fees from The Winkenwerder Co. Dr Wade has served on the science board for Resuscitation Products, Inc and the advisory board for AstraZeneca.

Funding/Support: This work was supported by subcontract W81XWH-08-C-0712 from the US Army Medical Research and Materiel Command. Infrastructure for the Data Coordinating Center was supported by Clinical and Translational Science Awards funds of grant UL1 RR024148 from the National Institutes of Health.

Role of the Sponsors: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.

Disclaimer: The views and opinions expressed in this article are those of the authors and do not reflect the official policy or position of the Army Medical Department, the Department of the Army, the Department of Defense, or the US government.

Previous Presentation: This paper was presented in part at the Advanced Technology Applications for Combat Causality Care Annual Scientific Meeting; August 16, 2011; Fort Lauderdale, Florida.

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Evans JA, van Wessem KJ, McDougall D, Lee KA, Lyons T, Balogh ZJ. Epidemiology of traumatic deaths: comprehensive population-based assessment.  World J Surg. 2010;34(1):158-163
PubMed   |  Link to Article
Kauvar DS, Lefering R, Wade CE. Impact of hemorrhage on trauma outcome: an overview of epidemiology, clinical presentations, and therapeutic considerations.  J Trauma. 2006;60(6):(suppl)  S3-S11
PubMed   |  Link to Article
Kelly JF, Ritenour AE, McLaughlin DF,  et al.  Injury severity and causes of death from Operation Iraqi Freedom and Operation Enduring Freedom: 2003-2004 vs 2006.  J Trauma. 2008;64(2):(suppl)  S21-S27
PubMed   |  Link to Article
Holcomb JB, McMullin NR, Pearse L,  et al.  Causes of death in US Special Operations Forces in the global war on terrorism: 2001-2004.  Ann Surg. 2007;245(6):986-991
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PubMed
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PubMed
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PubMed   |  Link to Article
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PubMed   |  Link to Article
Duchesne JC, Barbeau JM, Islam TM, Wahl G, Greiffenstein P, McSwain NE Jr. Damage control resuscitation: from emergency department to the operating room.  Am Surg. 2011;77(2):201-206
PubMed
Holcomb JB, Wade CE, Michalek JE,  et al.  Increased plasma and platelet to red blood cell ratios improves outcome in 466 massively transfused civilian trauma patients.  Ann Surg. 2008;248(3):447-458
PubMed
Johansson PI, Stensballe J. Effect of Haemostatic Control Resuscitation on mortality in massively bleeding patients: a before and after study.  Vox Sang. 2009;96(2):111-118
PubMed   |  Link to Article
Maegele M, Lefering R, Paffrath T, Tjardes T, Simanski C, Bouillon B.Working Group on Polytrauma of the German Society of Trauma Surgery (DGU).  Red-blood-cell to plasma ratios transfused during massive transfusion are associated with mortality in severe multiple injury: a retrospective analysis from the Trauma Registry of the Deutsche Gesellschaft für Unfallchirurgie.  Vox Sang. 2008;95(2):112-119
PubMed   |  Link to Article
Shaz BH, Dente CJ, Nicholas J,  et al.  Increased number of coagulation products in relationship to red blood cell products transfused improves mortality in trauma patients.  Transfusion. 2010;50(2):493-500
PubMed   |  Link to Article
Borgman MA, Spinella PC, Perkins JG,  et al.  The ratio of blood products transfused affects mortality in patients receiving massive transfusions at a combat support hospital.  J Trauma. 2007;63(4):805-813
PubMed   |  Link to Article
Cotton BA, Gunter OL, Isbell J,  et al.  Damage control hematology: the impact of a trauma exsanguination protocol on survival and blood product utilization.  J Trauma. 2008;64(5):1177-1183
PubMed   |  Link to Article
Duchesne JC, Islam TM, Stuke L,  et al.  Hemostatic resuscitation during surgery improves survival in patients with traumatic-induced coagulopathy.  J Trauma. 2009;67(1):33-39
PubMed   |  Link to Article
Cotton BA, Reddy N, Hatch QM,  et al.  Damage control resuscitation is associated with a reduction in resuscitation volumes and improvement in survival in 390 damage control laparotomy patients.  Ann Surg. 2011;254(4):598-605
PubMed   |  Link to Article
Holcomb JB, Zarzabal LA, Michalek JE,  et al; Trauma Outcomes Group.  Increased platelet:RBC ratios are associated with improved survival after massive transfusion.  J Trauma. 2011;71(2):(suppl 3)  S318-S328
PubMed   |  Link to Article
Gonzalez EA, Moore FA, Holcomb JB,  et al.  Fresh frozen plasma should be given earlier to patients requiring massive transfusion.  J Trauma. 2007;62(1):112-119
PubMed   |  Link to Article
Kashuk JL, Moore EE, Johnson JL,  et al.  Postinjury life threatening coagulopathy: is 1:1 fresh frozen plasma:packed red blood cells the answer?  J Trauma. 2008;65(2):261-271
PubMed   |  Link to Article
Davenport R, Curry N, Manson J,  et al.  Hemostatic effects of fresh frozen plasma may be maximal at red cell ratios of 1:2.  J Trauma. 2011;70(1):90-96
PubMed   |  Link to Article
Haywood-Watson RJ, Holcomb JB, Gonzalez EA,  et al.  Modulation of syndecan-1 shedding after hemorrhagic shock and resuscitation.  PLoS One. 2011;6(8):e23530
PubMed   |  Link to Article
Kozar RA, Peng Z, Zhang R,  et al.  Plasma restoration of endothelial glycocalyx in a rodent model of hemorrhagic shock.  Anesth Analg. 2011;112(6):1289-1295
PubMed   |  Link to Article
Pati S, Matijevic N, Doursout MF,  et al.  Protective effects of fresh frozen plasma on vascular endothelial permeability, coagulation, and resuscitation after hemorrhagic shock are time dependent and diminish between days 0 and 5 after thaw.  J Trauma. 2010;69:(suppl 1)  S55-S63
PubMed   |  Link to Article
Snyder CW, Weinberg JA, McGwin G Jr,  et al.  The relationship of blood product ratio to mortality: survival benefit or survival bias?  J Trauma. 2009;66(2):358-364
PubMed   |  Link to Article
Magnotti LJ, Zarzaur BL, Fischer PE,  et al.  Improved survival after hemostatic resuscitation: does the emperor have no clothes?  J Trauma. 2011;70(1):97-102
PubMed   |  Link to Article
Scalea TM, Bochicchio KM, Lumpkins K,  et al.  Early aggressive use of fresh frozen plasma does not improve outcome in critically injured trauma patients.  Ann Surg. 2008;248(4):578-584
PubMed
Ho AM, Dion PW, Yeung JH,  et al.  Prevalence of survivor bias in observational studies on fresh frozen plasma:erythrocyte ratios in trauma requiring massive transfusion.  Anesthesiology. 2012;116(3):716-728
PubMed   |  Link to Article
Dimick JB, Livingston EH. Comparing treatments using observational study designs: what can we do about selection bias?  Arch Surg. 2010;145(10):927
PubMed   |  Link to Article
van Walraven C, Davis D, Forster AJ, Wells GA. Time-dependent bias was common in survival analyses published in leading clinical journals.  J Clin Epidemiol. 2004;57(7):672-682
PubMed   |  Link to Article
Austin PC, Platt RW. Survivor treatment bias, treatment selection bias, and propensity scores in observational research.  J Clin Epidemiol. 2010;63(2):136-138
PubMed   |  Link to Article
Wade CE, del Junco DJ, Holcomb JB,  et al; Trauma Outcomes Group.  Variations between level I trauma centers in 24-hour mortality in severely injured patients requiring a massive transfusion.  J Trauma. 2011;71(2):(suppl 3)  S389-S393
PubMed   |  Link to Article
Rahbar MH, Fox EE, del Junco DJ,  et al; PROMMTT Investigators.  Coordination and management of multicenter clinical studies in trauma: experience from the PRospective Observational Multicenter Major Trauma Transfusion (PROMMTT) Study.  Resuscitation. 2012;83(4):459-464
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Gunter OL Jr, Au BK, Isbell JM, Mowery NT, Young PP, Cotton BA. Optimizing outcomes in damage control resuscitation: identifying blood product ratios associated with improved survival.  J Trauma. 2008;65(3):527-534
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Wafaisade A, Maegele M, Lefering R,  et al; Trauma Registry of DGU.  High plasma to red blood cell ratios are associated with lower mortality rates in patients receiving multiple transfusion (4≤red blood cell units<10) during acute trauma resuscitation.  J Trauma. 2011;70(1):81-89
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Yücel N, Lefering R, Maegele M,  et al; Polytrauma Study Group of the German Trauma Society.  Trauma Associated Severe Hemorrhage (TASH) Score: probability of mass transfusion as surrogate for life threatening hemorrhage after multiple trauma.  J Trauma. 2006;60(6):1228-1237
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Schreiber MA, Perkins J, Kiraly L, Underwood S, Wade C, Holcomb JB. Early predictors of massive transfusion in combat casualties.  J Am Coll Surg. 2007;205(4):541-545
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Nunez TC, Dutton WD, May AK, Holcomb JB, Young PP, Cotton BA. Emergency department blood transfusion predicts early massive transfusion and early blood component requirement.  Transfusion. 2010;50(9):1914-1920
PubMed   |  Link to Article
Nunez TC, Voskresensky IV, Dossett LA, Shinall R, Dutton WD, Cotton BA. Early prediction of massive transfusion in trauma: simple as ABC (assessment of blood consumption)?  J Trauma. 2009;66(2):346-352
PubMed   |  Link to Article
McLaughlin DF, Niles SE, Salinas J,  et al.  A predictive model for massive transfusion in combat casualty patients.  J Trauma. 2008;64(2):(suppl)  S57-S63
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Rainer TH, Ho AM, Yeung JH,  et al.  Early risk stratification of patients with major trauma requiring massive blood transfusion.  Resuscitation. 2011;82(6):724-729
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Borgman MA, Spinella PC, Holcomb JB,  et al.  The effect of FFP:RBC ratio on morbidity and mortality in trauma patients based on transfusion prediction score.  Vox Sang. 2011;101(1):44-54
PubMed   |  Link to Article
Larson CR, White CE, Spinella PC,  et al.  Association of shock, coagulopathy, and initial vital signs with massive transfusion in combat casualties.  J Trauma. 2010;69:(suppl 1)  S26-S32
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Cancio LC, Wade CE, West SA, Holcomb JB. Prediction of mortality and of the need for massive transfusion in casualties arriving at combat support hospitals in Iraq.  J Trauma. 2008;64(2):(suppl)  S51-S56
PubMed   |  Link to Article
Maegele M, Lefering R, Wafaisade A,  et al; Trauma Registry of Deutsche Gesellschaft für Unfallchirurgie (TR-DGU).  Revalidation and update of the TASH Score: a scoring system to predict the probability for massive transfusion as a surrogate for life-threatening haemorrhage after severe injury.  Vox Sang. 2011;100(2):231-238
PubMed   |  Link to Article
Krumrei NJ, Park MS, Cotton BA, Zielinski MD. Comparison of massive blood transfusion predictive models in the rural setting.  J Trauma Acute Care Surg. 2012;72(1):211-215
PubMed
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Holcomb J. Pragmatic, Randomized Optimal Platelets and Plasma Ratios (PROPPR). http://clinicaltrials.gov/ct2/show/NCT01545232. Accessed March 8, 2012

Figures

Place holder to copy figure label and caption
Graphic Jump Location

Figure 1. Blood product use in the first 6 hours in 2 Prospective, Observational, Multicenter, Major Trauma Transfusion Study patients. Patient 1 (A) had an Injury Severity Score of 48 and died of hemorrhage at 1 hour 7 minutes after emergency department admission. Patient 2 (B) had an Injury Severity Score of 57 and was discharged to another acute care hospital at 27 days. Note the constantly changing ratios over time. For example, patient 1 received cumulative plasma:platelet:red blood cell (RBC) ratios of 0:0:1, 0:0:3, 0:0:6, 4:6:6, and 5:6:6 at 15, 30, 45, 60, and 75 minutes, respectively, while patient 2 received cumulative plasma:platelet:RBC ratios of 0:0:1, 0:0:4, 0:0:4, 2:0:6, and 2:0:10 at those same times.

Place holder to copy figure label and caption
Graphic Jump Location

Figure 2. The bars represent cumulative ratios at the start of each time interval. Most patients received a plasma:red blood cell (RBC) ratio of 1:2 or higher by 3 hours (A) and a platelet:RBC ratio of 1:2 or higher by 6 hours (B). In the last time interval (24 hours), the percentage of patients receiving 0 units of platelets or plasma increases, reflecting the dynamic cohort with newly eligible patients entering and others exiting owing to death in the previous interval.

Tables

Table Graphic Jump LocationTable 1. Admission and Treatment Characteristics and Unadjusted Survival in 1245 Prospective, Observational, Multicenter, Major Trauma Transfusion Study Patients
Table Graphic Jump LocationTable 2. Distribution of Reported Cause of Death for Decedent Patients in the Analysis Cohort by the Time Period of Deatha
Table Graphic Jump LocationTable 3. Multivariable Cox Regression Models Examining the Association of Plasma and Platelet Transfusion Ratios With In-hospital Mortality

References

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PubMed   |  Link to Article
Holcomb JB, McMullin NR, Pearse L,  et al.  Causes of death in US Special Operations Forces in the global war on terrorism: 2001-2004.  Ann Surg. 2007;245(6):986-991
PubMed   |  Link to Article
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Moore FA, Nelson T, McKinley BA,  et al; StO2 Study Group.  Massive transfusion in trauma patients: tissue hemoglobin oxygen saturation predicts poor outcome.  J Trauma. 2008;64(4):1010-1023
PubMed   |  Link to Article
Counts RB, Haisch C, Simon TL, Maxwell NG, Heimbach DM, Carrico CJ. Hemostasis in massively transfused trauma patients.  Ann Surg. 1979;190(1):91-99
PubMed   |  Link to Article
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PubMed
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PubMed   |  Link to Article
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PubMed   |  Link to Article
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PubMed   |  Link to Article
Duchesne JC, Barbeau JM, Islam TM, Wahl G, Greiffenstein P, McSwain NE Jr. Damage control resuscitation: from emergency department to the operating room.  Am Surg. 2011;77(2):201-206
PubMed
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PubMed
Johansson PI, Stensballe J. Effect of Haemostatic Control Resuscitation on mortality in massively bleeding patients: a before and after study.  Vox Sang. 2009;96(2):111-118
PubMed   |  Link to Article
Maegele M, Lefering R, Paffrath T, Tjardes T, Simanski C, Bouillon B.Working Group on Polytrauma of the German Society of Trauma Surgery (DGU).  Red-blood-cell to plasma ratios transfused during massive transfusion are associated with mortality in severe multiple injury: a retrospective analysis from the Trauma Registry of the Deutsche Gesellschaft für Unfallchirurgie.  Vox Sang. 2008;95(2):112-119
PubMed   |  Link to Article
Shaz BH, Dente CJ, Nicholas J,  et al.  Increased number of coagulation products in relationship to red blood cell products transfused improves mortality in trauma patients.  Transfusion. 2010;50(2):493-500
PubMed   |  Link to Article
Borgman MA, Spinella PC, Perkins JG,  et al.  The ratio of blood products transfused affects mortality in patients receiving massive transfusions at a combat support hospital.  J Trauma. 2007;63(4):805-813
PubMed   |  Link to Article
Cotton BA, Gunter OL, Isbell J,  et al.  Damage control hematology: the impact of a trauma exsanguination protocol on survival and blood product utilization.  J Trauma. 2008;64(5):1177-1183
PubMed   |  Link to Article
Duchesne JC, Islam TM, Stuke L,  et al.  Hemostatic resuscitation during surgery improves survival in patients with traumatic-induced coagulopathy.  J Trauma. 2009;67(1):33-39
PubMed   |  Link to Article
Cotton BA, Reddy N, Hatch QM,  et al.  Damage control resuscitation is associated with a reduction in resuscitation volumes and improvement in survival in 390 damage control laparotomy patients.  Ann Surg. 2011;254(4):598-605
PubMed   |  Link to Article
Holcomb JB, Zarzabal LA, Michalek JE,  et al; Trauma Outcomes Group.  Increased platelet:RBC ratios are associated with improved survival after massive transfusion.  J Trauma. 2011;71(2):(suppl 3)  S318-S328
PubMed   |  Link to Article
Gonzalez EA, Moore FA, Holcomb JB,  et al.  Fresh frozen plasma should be given earlier to patients requiring massive transfusion.  J Trauma. 2007;62(1):112-119
PubMed   |  Link to Article
Kashuk JL, Moore EE, Johnson JL,  et al.  Postinjury life threatening coagulopathy: is 1:1 fresh frozen plasma:packed red blood cells the answer?  J Trauma. 2008;65(2):261-271
PubMed   |  Link to Article
Davenport R, Curry N, Manson J,  et al.  Hemostatic effects of fresh frozen plasma may be maximal at red cell ratios of 1:2.  J Trauma. 2011;70(1):90-96
PubMed   |  Link to Article
Haywood-Watson RJ, Holcomb JB, Gonzalez EA,  et al.  Modulation of syndecan-1 shedding after hemorrhagic shock and resuscitation.  PLoS One. 2011;6(8):e23530
PubMed   |  Link to Article
Kozar RA, Peng Z, Zhang R,  et al.  Plasma restoration of endothelial glycocalyx in a rodent model of hemorrhagic shock.  Anesth Analg. 2011;112(6):1289-1295
PubMed   |  Link to Article
Pati S, Matijevic N, Doursout MF,  et al.  Protective effects of fresh frozen plasma on vascular endothelial permeability, coagulation, and resuscitation after hemorrhagic shock are time dependent and diminish between days 0 and 5 after thaw.  J Trauma. 2010;69:(suppl 1)  S55-S63
PubMed   |  Link to Article
Snyder CW, Weinberg JA, McGwin G Jr,  et al.  The relationship of blood product ratio to mortality: survival benefit or survival bias?  J Trauma. 2009;66(2):358-364
PubMed   |  Link to Article
Magnotti LJ, Zarzaur BL, Fischer PE,  et al.  Improved survival after hemostatic resuscitation: does the emperor have no clothes?  J Trauma. 2011;70(1):97-102
PubMed   |  Link to Article
Scalea TM, Bochicchio KM, Lumpkins K,  et al.  Early aggressive use of fresh frozen plasma does not improve outcome in critically injured trauma patients.  Ann Surg. 2008;248(4):578-584
PubMed
Ho AM, Dion PW, Yeung JH,  et al.  Prevalence of survivor bias in observational studies on fresh frozen plasma:erythrocyte ratios in trauma requiring massive transfusion.  Anesthesiology. 2012;116(3):716-728
PubMed   |  Link to Article
Dimick JB, Livingston EH. Comparing treatments using observational study designs: what can we do about selection bias?  Arch Surg. 2010;145(10):927
PubMed   |  Link to Article
van Walraven C, Davis D, Forster AJ, Wells GA. Time-dependent bias was common in survival analyses published in leading clinical journals.  J Clin Epidemiol. 2004;57(7):672-682
PubMed   |  Link to Article
Austin PC, Platt RW. Survivor treatment bias, treatment selection bias, and propensity scores in observational research.  J Clin Epidemiol. 2010;63(2):136-138
PubMed   |  Link to Article
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Holcomb JB, del Junco DJ, Fox EE, et al; PROMMTT Study Group. The Prospective, Observational, Multicenter, Major Trauma Transfusion (PROMMTT) Study: comparative effectiveness of a time-varying treatment with competing risks.. Arch Surg.. Published online October 15, 2012. doi:10.1001/2013.jamasurg.387.

eFigure. PROMMTT CONSORT diagram.

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