Iron Deficiency in Anemic Children Surviving Critical Illness: Post Hoc Analysis of a Single-Center Prospective Cohort in Canada, 2019–2022
Bibliographic record
Abstract
OBJECTIVES: Many children leave the PICU with anemia. The mechanisms of post-PICU anemia are poorly investigated, and treatment of anemia, other than blood, is rarely started during PICU. We aimed to characterize the contributions of iron depletion (ID) and/or inflammation in the development of post-PICU anemia and to explore the utility of hepcidin (a novel iron marker) at detecting ID during inflammation. DESIGN: Post hoc analysis of a single-center prospective study (November 2019 to September 2022). SETTING: PICU, quaternary center, Canada. PATIENTS: Children admitted to PICU with greater than or equal to 48 hours of invasive or greater than or equal to 96 hours of noninvasive ventilation. We excluded patients with preexisting conditions causing anemia or those admitted after cardiac surgery. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Hematological and iron profiles were performed at PICU discharge on 56 participants of which 37 (37/56) were diagnosed with anemia. Thirty-three children (33/56; 59%) were younger than 2 years. Median Pediatric Logistic Organ Dysfunction score was 11 (interquartile range, 6-16). Twenty-four of the 37 anemic patients had repeat bloodwork 2 months post-PICU. Of those, four (4/24; 16%) remained anemic. Hematologic profiles were categorized as: anemia of inflammation (AI), iron deficiency anemia (IDA), IDA with inflammation, and ID (low iron stores without anemia). Seven (7/47; 15%) had AI at discharge, and one had persistent AI post-PICU. Three patients (3/47; 6%) had IDA at discharge; of which one was lost to follow-up and the other two were no longer anemic but had ID post-PICU. Eleven additional patients developed ID post-PICU. In the exploratory analysis, we identified a diagnostic cutoff value for ID during inflammation from the receiver operating characteristic curve for hepcidin of 31.9 pg/mL. This cutoff would increase the detection of ID at discharge from 6% to 34%. CONCLUSIONS: The burden of ID in children post-PICU is high and better management strategies are required. Hepcidin may increase the diagnostic yield of ID in patients with inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".