73 Predictors of neurodevelopmental impairment in extreme preterm infants with unremarkable cranial ultrasound: A Canadian population-based cohort
Bibliographic record
Abstract
Abstract Background Extreme preterm infants are at high-risk of brain injury and neurodevelopmental impairment (NDI). Previous studies reported significant NDI (sNDI) in preterm infants with no abnormalities on routine cranial ultrasound screening (CUS), traditionally perceived to have favorable outcomes. Recent data from population-based outlining clinical predictors of sNDI in those infants are lacking. Objectives (1) to describe the incidence, perinatal and neonatal characteristics of NDI and sNDI in a large population-based cohort of extreme preterm infants with unremarkable CUS; (2) to develop a prediction model of sNDI (any of: CP ≥ GMFCS stage 3, Bayley III < 70 in any domain, deafness requiring aids, or bilateral blindness) in those infants. Design/Methods A retrospective review of a national cohort of preterm infants born at <29 weeks’ gestation between April 2009 and December 2018. We included surviving infants with unremarkable routine CUS (no hemorrhage, periventricular leukomalacia or ventricular dilatation) who had neurodevelopmental assessment at 18-24 month corrected age at one of participating centres in the Canadian Neonatal Follow Up Network. We compared the baseline characteristics between infants with no NDI and those with any NDI and sNDI. The population sample was then randomly split into training and testing datasets in 70:30 ratio for prediction model’s development and validation, respectively. Multivariate logistic regression with GEE accounting for clustering within site was used, adjusted for variables with p-value<0.1 using backward selection procedure. The AUC and the diagnostic properties of the model developed using training dataset was then validated in the testing dataset. Results Out of 7463 eligible infants, 4327 (58%) were included. Of those, 2501 (59%) had no NDI and 1736 (41%) had NDI. sNDI accounted for 14% of the cohort and one third of those with NDI. Perinatal and neonatal characteristics of the three groups are shown in Table 1. On internal validation, maternal hypertension, maternal level of education, gestational age, male sex, ROP≥ stage 3 and systemic steroids were predictive of sNDI (Table 2). The prediction model had Sensitivity 0.91, NPV 0.90, and AUC 0.69 (95% CI 0.64-0.73). Conclusion In this national cohort, 2 out of 5 extreme preterm infants with unremarkable CUS had NDI and 1 in 7 had sNDI. The developed prediction model had high sensitivity and NPV, but poor discrimination. The findings of this study can help clinicians to identify at-risk infants who may benefit from early targeted interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".