Single Centre Experience with Hypoxic Ischemic Encephalopathy: Prognostic Factors and Development of a Prognostication Model
Bibliographic record
Abstract
Hypoxic ischemic encephalopathy (HIE) continues to carry prognostic uncertainty despite therapeutic hypothermia (TH) becoming standard of care in high income nations. Prognostic factors have been identified with recent evidence relying on factors that are available later in a hospital admission such as brain magnetic resonance imaging (MRI). Further, most of the research utilizes a composite outcome of death or disability which makes it difficult to apply findings to patient-specific conversations at the bedside. In this thesis, we conducted a systematic review to identify early prognostic factors (those that can be identified in the first 72 hours) from randomized control trials (RCTs) of TH for neonatal HIE. This review identified pre-randomization, biochemical and clinical factors which were then used to guide model development from a cohort of infants with HIE in Edmonton, Alberta since 2006. We developed a regression model for early and late prediction of death in addition to a prediction model for significant/severe neurodevelopmental impairment (sNDI). The early prediction model included receipt of phenobarbital, hypotension receiving inotrope(s), severe HIE (as per Sarnat staging), chest compressions and perinatal sentinel event. The late prediction model for death included the above factors in addition to renal dysfunction. The prediction model for sNDI included receipt of phenobarbital, hypoglycemia, abnormal MRI, electrolyte abnormality and 10-minute Apgar score less than 5. Using the same cohort, classification and regression tree (CART) analysis was used to develop prediction models for death and sNDI. The CART models outperformed the logistic regression models for measures of sensitivity, specificity and negative predictive value, thereby providing a clinical picture of what survival and NDI-free survival would look like. According to the separate models, an infant who did not require inotropes for hypotension or receive phenobarbital would have a very low chance of death or sNDI. The findings from this thesis will improve bedside conversations by decreasing prognostic uncertainty, allow for the identification of high-risk infants and ensure appropriate triage for neonatal follow-up.
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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) | 0.000 | 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) | 0.001 | 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 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".