Background Outcome prediction after preterm birth is important for long-term neonatal care, but has proven notoriously challenging for neurocognitive outcome. This study investigated the potential of machine learning to improve neurocognitive outcome prediction at two and five years of corrected age in preterm infants, using readily available predictors from the neonatal setting. Methods Predictors originating from the antenatal and neonatal period of preterm infants born <30 weeks gestation were used to predict adverse neurocognitive outcome on the Bayley Scale and Wechsler Preschool and Primary Scale of Intelligence. Machine learning models were compared to conventional logistic regression and validated using internal cross-validation. Results Best performing models were a random forest (two-year outcome) and a support vector machine (five-year outcome) with an area under the receiver operating characteristic curve (AUC) of 0.682 and 0.695 respectively, reaching high negative predictive values (95% and 91%, respectively). These models performed significantly better than the conventional models. Conclusions The models reached moderate overall predictive performance, yet with promising potential for early identification of children without adverse neurocognitive outcome. Machine learning modestly improved neurocognitive outcome prediction. Future research may harvest the predictive potential of a wider variety of routine (clinical) data, such as vital sign time series. Impact <UnorderedList Mark="Bullet"> <ItemContent> Early prediction of neurocognitive outcome in preterm infants will enable targeted follow-up and deployment of early (preventative) interventions to improve outcome. </ItemContent> <ItemContent> Neurocognitive outcome remains notoriously challenging using conventional models, while existing machine learning models depend on advanced MRI-derived predictors with limited potential for implementation into daily clinical practice. </ItemContent> <ItemContent> This study developed machine learning models for neurocognitive outcome prediction using predictors that are readily available in neonatal settings. </ItemContent> <ItemContent> Neurocognitive outcome prediction remains challenging due to low AUC and PPV, however, the models demonstrate high NPV, indicating potential for identifying children at low risk for adverse outcome. </ItemContent> </UnorderedList>

Machine learning models for neurocognitive outcome prediction in preterm born infants

期刊论文
发表日期：

2025年1月18日

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Menne R. van Boven,
Frank C. Bennis,
Wes Onland,
Cornelieke S. H. Aarnoudse-Moens,
Max Frings,
Kevin Tran,
Trixie A. Katz,
Michelle Romijn,
Mark Hoogendoorn,
Anton H. van Kaam,
Aleid G. Leemhuis,
Jaap Oosterlaan,
Marsh Königs

摘要

Background

Outcome prediction after preterm birth is important for long-term neonatal care, but has proven notoriously challenging for neurocognitive outcome. This study investigated the potential of machine learning to improve neurocognitive outcome prediction at two and five years of corrected age in preterm infants, using readily available predictors from the neonatal setting.

Methods

Predictors originating from the antenatal and neonatal period of preterm infants born <30 weeks gestation were used to predict adverse neurocognitive outcome on the Bayley Scale and Wechsler Preschool and Primary Scale of Intelligence. Machine learning models were compared to conventional logistic regression and validated using internal cross-validation.

Results

Best performing models were a random forest (two-year outcome) and a support vector machine (five-year outcome) with an area under the receiver operating characteristic curve (AUC) of 0.682 and 0.695 respectively, reaching high negative predictive values (95% and 91%, respectively). These models performed significantly better than the conventional models.

Conclusions

The models reached moderate overall predictive performance, yet with promising potential for early identification of children without adverse neurocognitive outcome. Machine learning modestly improved neurocognitive outcome prediction. Future research may harvest the predictive potential of a wider variety of routine (clinical) data, such as vital sign time series.

Impact

Early prediction of neurocognitive outcome in preterm infants will enable targeted follow-up and deployment of early (preventative) interventions to improve outcome.

Neurocognitive outcome remains notoriously challenging using conventional models, while existing machine learning models depend on advanced MRI-derived predictors with limited potential for implementation into daily clinical practice.

This study developed machine learning models for neurocognitive outcome prediction using predictors that are readily available in neonatal settings.

Neurocognitive outcome prediction remains challenging due to low AUC and PPV, however, the models demonstrate high NPV, indicating potential for identifying children at low risk for adverse outcome.