Mortality associated with restrictive threshold for red blood cell transfusion in pediatric patients with sepsis
Bibliographic record
Abstract
The authors reply: As mortality is low in pediatric intensive care patients, other outcome measures have been developed. Because a surrogate outcome is defined as “a variable that provides an indirect measurement of effect in situations where direct measurement of clinical effect is not feasible,” (1) most would agree that organ dysfunction scores could be a valid surrogate for death. In our study (2), the primary outcome was the development or progression of a multiple organ dysfunction syndrome, as defined by Proulx et al (3). We also measured other multiple daily morbidity outcomes, such as the Pediatric Logistic Organ Dysfunction Score (4), PaO2/FIO2 ratio, blood lactate level, nosocomial infections, duration of mechanical ventilation, pediatric intensive care unit length of stay, pediatric intensive care unit mortality rate, and mortality at 28 days. None of these outcome measures differed significantly between the patients in the two groups. Among the 137 patients, the primary outcome, new or progressive multiple organ dysfunction syndrome, was 13 (18.8%) vs. 13 (19.1%) (p = .97) for patients in the restrictive and liberal transfusion groups, respectively. The 28-day mortality was 7 (10%) vs. 2 (3%) (p = .08). Because death can be viewed as a dysfunction of all organs, it was also measured in the primary outcome. As both the primary outcome and all the secondary outcomes were so similar in both groups, we believe that observation of this nonsignificant difference in mortality at 28 days is nothing more than statistical artifact, as no other variable correlates with the observed mortality. This is why we wrote in our manuscript: “The number of deaths during [pediatric intensive care unit] stay and at 28 days were not significantly different between both groups, but these results should be interpreted cautiously, as the number of deaths was low. Nevertheless, no other secondary outcome measuring morbidity was different between the two groups, which further supports the findings with regard to mortality.” (2) Furthermore, mortality is a secondary outcome in a secondary analysis. Therefore, we would advise against overinterpreting this outcome (5). Indeed, the sample size of the main trial was based on the main outcome and not on a rare secondary outcome. All subgroup analyses suffer de facto from a lack of power. This is why we wrote “the most important limitation of our study are the pitfalls inherent to any subgroup analysis which preclude the possibility of generating definitive conclusions and at best allow for hypothesis generation only. Hence, no definitive recommendations regarding transfusion thresholds in stabilized septic children can be made; it is nonetheless striking that the frequency of the primary outcome in the two transfusion groups was very similar.” (2). We agree that absence of evidence is not evidence of absence and that our sample size does not allow us to demonstrate the noninferiority of a restrictive strategy, but it might seem misleading to compute a post hoc sample size and extrapolate a hypothetically increased mortality in such a way (5). Therefore, as both the main study (6) and all three subgroup analyses (2,7,8) do not find evidence that a restrictive red cell transfusion strategy, when compared to a liberal one, increase the rate of morbidity, we maintain our suggestion that a hemoglobin level of 7/dL is safe in stabilized critically ill children. The authors have not disclosed any potential conflicts of interest. Oliver Karam, MD, MSc, Marisa Tucci, MD, BSc, Thierry Ducruet, MSc, Heather Ann Hume, MD, Jacques Lacroix, MD, France Gauvin, MD, MSc, CHU Sainte-Justine, Montreal, Quebec, Canada
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".