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Clinical Trial
. 2020 Sep;89(3):505-513.
doi: 10.1097/TA.0000000000002819.

Dynamic impact of transfusion ratios on outcomes in severely injured patients: Targeted machine learning analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios randomized clinical trial

Minh Nguyen  1 , Romain Pirracchio, Lucy Z Kornblith, Rachael Callcut, Erin E Fox, Charles E Wade, Martin Schreiber, John B Holcomb, Jeremy Coyle, Mitchell Cohen, Alan Hubbard
Affiliations
Clinical Trial

Dynamic impact of transfusion ratios on outcomes in severely injured patients: Targeted machine learning analysis of the Pragmatic, Randomized Optimal Platelet and Plasma Ratios randomized clinical trial

Minh Nguyen et al. J Trauma Acute Care Surg. 2020 Sep.

Abstract

Background: Massive transfusion protocols to treat postinjury hemorrhage are based on predefined blood product transfusion ratios followed by goal-directed transfusion based on patient's clinical evolution. However, it remains unclear how these transfusion ratios impact patient outcomes over time from injury.

Methods: The Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) is a phase 3, randomized controlled trial, across 12 Level I trauma centers in North America. From 2012 to 2013, 680 severely injured patients required massive transfusion. We used semiparametric machine learning techniques and causal inference methods to augment the intent-to-treat analysis of PROPPR, estimating the dynamic relationship between transfusion ratios and outcomes: mortality and hemostasis at different timepoints during the first 24 hours after admission.

Results: In the intention-to-treat analysis, the 1:1:1 group tended to have decreased mortality, but with no statistical significance. For patients in whom hemostasis took longer than 2 hours, the 1:1:1 ratio was associated with a higher probability of hemostasis, statistically significant from the 4 hour on. In the per-protocol, actual-transfusion-ratios-received analysis, during four successive time intervals, no significant association was found between the actual ratios and mortality. When comparing patient groups who received both high plasma/PRBC and high platelet/PRBC ratios to the group of low ratios in both, the relative risk of achieving hemostasis was 2.49 (95% confidence interval, 1.19-5.22) during the third hour after admission, suggesting a significant beneficial impact of higher transfusion ratios of plasma and platelets on hemostasis.

Conclusion: Our results suggest that the impact of transfusion ratios on hemostasis is dynamic. Overall, the transfusion ratios had no significant impact on mortality over time. However, receiving higher ratios of platelets and plasma relative to red blood cells hastens hemostasis in subjects who have yet to achieve hemostasis within 3 hours after hospital admission.

Level of evidence: Therapeutic IV.

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Conflict of interest statement

Disclosure:

The authors declare no conflicts of interest.

Figures

Figure 1:
Figure 1:
Flow diagram of patient process through the Pragmatic Randomized Optimal Platelet and Plasma Ratios (PROPPR) trial.
Figure 2:
Figure 2:
Cumulative counts of deaths and hemostasis over 24 hours since patient arrivals at the Emergency Departments
Figure 3:
Figure 3:
Cumulative actual transfused Plasma:RBC and Platelets:RBC ratios by assigned treatment groups 1:1:1 vs. 1:1:2
Figure 4:
Figure 4:
Adjusted causal relative risk (RR) estimates, 1:1:1 group / 1:1:2 group, with 95% confidence intervals for mortality and hemostasis over 24 hours since patient arrivals at the Emergency Departments.
See this image and copyright information in PMC

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References

    1. Heron M Deaths: Leading Causes for 2015. National Vital Statistics Reports, 66(5), 2017. Available at: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_05.pdf. Accessed December 18, 2019. - PubMed
    1. Callcut RA, Kornblith LZ, Conroy AS, Robles AJ, Meizoso JP, Namias N, Meyer DE, Haymaker A, Truitt SM, Agrawal V, et al. The why and how our trauma patients die: A prospective Multicenter Western Trauma Association study. J Trauma Acute Care Surg. 2019;86(5): 864–870. - PMC - PubMed
    1. Holcomb JB, Tilley BC, Baraniuk S, Fox EE; Wade CE, Podbielski JM, del Junco DJ, Brasel KJ, Bulger EM, Callcut RA, et al. Transfusion of plasma, platelets, and red blood cells in a 1:1:1 vs a 1:1:2 ratio and mortality in patients with severe trauma: the PROPPR randomized clinical trial. JAMA. 2015;313(5):471–482. - PMC - PubMed
    1. Cotton BA, Reddy N, Hatch QM, LeFebvre E, Wade CE, Kozar RA, Gill BS, Albarado R, McNutt MK, Holcomb JB. 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. - PMC - PubMed
    1. Langan NR, Eckert M, Martin MJ. Changing patterns of in-hospital deaths following implementation of damage control resuscitation practices in us forward military treatment facilities. JAMA Surg. 2014;149(9):904–912. - PubMed

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