[Early prediction of the neurological result at 12 months in newborns at neurological risk]

Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study

BACKGROUND

The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches.

METHODS

Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians' opinions about the predictability of the same features were compared with machine learning.

RESULTS

According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians' opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome.

CONCLUSIONS

The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.

Influence of perinatal complications on the development of a sample of 36-month-old premature infants

OBJECTIVE

The lack of clear results in previous studies for this context makes us consider an exploratory study. The objective of this research is to examine the influence of certain perinatal factors on the development of premature infants over their first 36 months of life.

METHOD

The sample consisted of 59 preterm infants born between 25 and 34 weeks of gestational age in an NICU of a third-level hospital. At 36 months of age, the Bayley-III Infant Development Scale (Spanish adaptation) and a clinical history were collected.

RESULTS

The average scores on the Bayley-III Infant Development Scale were generally within the normal range, but significantly lower than normal for Fine Motor Function, Gross Motor Function, and Expressive Language. These differences remained when considering the degree of prematurity, gender, and perinatal complications. Infants who received mechanical ventilation, oxygen therapy or corticosteroid treatment due to bronchopulmonary dysplasia showed the greatest discrepancies from normal levels.

CONCLUSION

Our results support prior studies that show that a combination of perinatal risk factors constitutes the largest determinant for developmental issues at 36 months of age. This information establishes the need for a priority follow-up in this population beyond 24 months of corrected age.