Anemia in Pediatric Intestinal Failure: Prevalence, Predictors and Etiologies
Bibliographic record
Abstract
Children with intestinal failure (IF) are at high-risk for different types of anemia, including iron deficiency anemia (IDA), anemia of inflammation (AI) and mixed IDA/AI. Data on the prevalence and underlying contributors to the types of anemia in the pediatric IF population is limited. Therefore, the aim of this thesis was to examine the prevalence and contributions of the various types of anemia in children with IF and identify factors associated with these anemias. A 10-year retrospective, multicenter study of pediatric IF patients managed by three separate intestinal rehabilitation programs (IRPs) in Canada was conducted. Anemia was defined by age-specific hemoglobin values, and anemia types were classified using a combination of hematologic measures and iron indices. Univariable regression analysis was performed to evaluate for demographic and clinical factors associated with anemia and anemia types. Among ninety children with IF, the period prevalence of anemia was 83% [75/90], with 76% [55/72] of children experiencing chronic anemia, defined as anemia on ≥2 annual hemoglobin measurements. AI (44%) [40/90] and mixed IDA/AI (36%) [32/90] were more prevalent than IDA (17%) [15/90]; 26% [19/90] children developed >1 type of anemia over time, and 84% [191/227] of anemic hemoglobin measurements occurred while receiving iron supplementation, oral or in parenteral nutrition (PN). The prevalence of mixed IDA/AI was higher at 2 IRPs that did not have access to iron-supplemented PN (75% vs 9%; p<0.001), as was small intestine bacterial overgrowth (SIBO) (58% vs 28%; p=0.004) and gastrointestinal bleeding (39% vs 15%; p=0.001). Children receiving iron-supplemented PN had lower odds of mixed IDA/AI compared to no anemia (OR 0.06, p<0.001), while oral iron supplementation was associated with an increased odds of mixed IDA/AI compared to no anemia (OR 3.40, p=.01). This study demonstrated a high prevalence of anemia in children with IF, specifically mixed IDA/AI and AI. This anemia is often chronic and dynamic with evolving anemia types. Our results suggest that mode of iron supplementation may impact IF-associated complications and anemia types. Future studies exploring the complex interactions between the gut microbiome, mode of iron supplementation and inflammation on anemia in pediatric IF are needed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".