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
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".