Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods
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Updated: Aug 15, 2026
Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Andrea K Bowe1, Gordon Lightbody1,2, Anthony Staines3
1INFANT Research Centre, University College Cork, Cork, Ireland.
Insights
A predictive model using routine data can identify very preterm infants at high risk for cognitive delay. This allows for early, targeted interventions to improve cognitive outcomes in this vulnerable population.
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.
Importance:
Early intervention can improve cognitive outcomes for very preterm infants but is resource intensive. Identifying those who need early intervention most is important.
Objective:
To evaluate a model for use in very preterm infants to predict cognitive delay at 2 years of age using routinely available clinical and sociodemographic data.
Design, Setting, And Participants:
This prognostic study was based on the Swedish Neonatal Quality Register. Nationwide coverage of neonatal data was reached in 2011, and registration of follow-up data opened on January 1, 2015, with inclusion ending on September 31, 2022. A variety of machine learning models were trained and tested to predict cognitive delay. Surviving infants from neonatal units in Sweden with a gestational age younger than 32 weeks and complete data for the Bayley Scales of Infant and Toddler Development, Third Edition cognitive index or cognitive scale scores at 2 years of corrected age were assessed. Infants with major congenital anomalies were excluded.
Exposures:
A total of 90 variables (containing sociodemographic and clinical information on conditions, investigations, and treatments initiated during pregnancy, delivery, and neonatal unit admission) were examined for predictability.
Main Outcomes And Measures:
The main outcome was cognitive function at 2 years, categorized as screening positive for cognitive delay (cognitive index score <90) or exhibiting typical cognitive development (score ≥90).
Results:
A total of 1062 children (median [IQR] birth weight, 880 [720-1100] g; 566 [53.3%] male) were included in the modeling process, of whom 231 (21.8%) had cognitive delay. A logistic regression model containing 26 predictive features achieved an area under the receiver operating curve of 0.77 (95% CI, 0.71-0.83). The 5 most important features for cognitive delay were non-Scandinavian family language, prolonged duration of hospitalization, low birth weight, discharge to other destination than home, and the infant not receiving breastmilk on discharge. At discharge from the neonatal unit, the full model could correctly identify 605 of 650 infants who would have cognitive delay at 24 months (sensitivity, 0.93) and 1081 of 2350 who would not (specificity, 0.46).
Conclusions And Relevance:
The findings of this study suggest that predictive modeling in neonatal care could enable early and targeted intervention for very preterm infants most at risk for developing cognitive impairment.
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