Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods

Area of Science:

• Neonatal Medicine
• Developmental Pediatrics
• Machine Learning in Healthcare

Background:

• Early intervention is crucial for improving cognitive outcomes in very preterm infants.
• Identifying infants most in need of intervention is essential due to resource limitations.

Purpose of the Study:

• To evaluate a predictive model for cognitive delay at 2 years of age in very preterm infants.
• The model utilizes routinely available clinical and sociodemographic data.

Main Methods:

• A prognostic study using the Swedish Neonatal Quality Register (2015-2022).
• Machine learning models were trained to predict cognitive delay (Bayley Scales score <90) at 2 years corrected age.
• Included infants born <32 weeks gestational age, excluding major congenital anomalies.

Main Results:

• A logistic regression model with 26 features predicted cognitive delay with an area under the receiver operating curve of 0.77.
• Key predictors included non-Scandinavian family language, prolonged hospitalization, low birth weight, discharge destination, and lack of breastfeeding.
• The model achieved high sensitivity (0.93) in identifying infants with cognitive delay.

Conclusions:

• Predictive modeling in neonatal care can facilitate early and targeted interventions.
• This approach can help identify very preterm infants at highest risk for cognitive impairment.
• Optimizing interventions based on predicted risk can improve developmental trajectories.