19 Treatment of Neonatal Hyperbilirubinemia – Ontario Cohort 2014-2018
Bibliographic record
Abstract
Abstract Background Serious complications that could result from severe neonatal hyperbilirubinemia include acute and chronic bilirubin encephalopathy. In Ontario, the incidence of severe neonatal hyperbilirubinemia and its associated treatments in term and near-term infants is currently unknown. Although IVIG therapy has been increasingly discussed in the literature, a recent Cochrane review (2018) indicated that there was a lack of evidence for recommending IVIG therapy for routine use. Objectives The purpose of this study was to determine the current incidence of severe neonatal hyperbilirubinemia and its treatments (Intravenous Immunoglobulin (IVIG) therapy, exchange transfusion and phototherapy) most often used in Ontario. Design/Methods A population-based retrospective cohort study of all term and near-term infants (≥ 35 weeks’ gestation) born in Ontario from April 2014 to March 2018 was conducted. National and provincial databases including Better Outcomes Registry Network Ontario (BORN) and Canadian Neonatal Network (CNN) were utilized. Results Data was collected from 533,084 infants born in Ontario over the 4 years. Of the total infants screened, 29,756 (6%) infants were diagnosed with neonatal hyperbilirubinemia. In terms of treatments, 24,646 (83%) infants received phototherapy, 54 (0.18%) infants received an exchange transfusion and 458 (1.5%) infants received IVIG therapy. In Ontario, neonatal hyperbilirubinemia had a statistically significant increase from 2014 to 2018 (P<0.0001). Although phototherapy was used on almost all neonates with hyperbilirubinemia (83%) there was a significant decrease from 2014 to 2018 (from 88% to 80%) (P<0.0001). Of the babies with hyperbilirubinemia in 2014, 71 (1.06%) infants received IVIG therapy and 15 (0.22%) infants received exchange transfusion. Within 4 years, IVIG therapy incidence had a significant increase from 71 (1.06%) infants to 156 (2.04%) infants (P<0.0001), while exchange transfusion remained relatively constant (P=.315). Exchange transfusion rates allows for the prediction that the rate of severe neonatal hyperbilirubinemia is stable in Ontario. Conclusion In conclusion, (severe) hyperbilirubinemia still exists amongst neonates in Ontario, despite the advancements in managing hyperbilirubinemia, indicating the need for better treatments and/or monitoring. There was also a significant rise in the use of IVIG despite the continued debate about its utility. Further research should be conducted nationally to determine the incidence of severe neonatal hyperbilirubinemia and to indicate the usage of IVIG therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".