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Objective <p>Extrauterine growth restriction (EUGR) affects 30–97% of preterm infants and is associated with poor outcomes. We used machine learning (ML) to assess how clinical and nutritional factors, particularly during the transition from parenteral to enteral nutrition, influence EUGR.</p> Study design <p>This retrospective observational study included 1165 patients (46% with EUGR) born below 33 weeks’ gestation or 1500 g. We developed 10 models to predict EUGR combining two sets of features (all and nutritional features only) across five subgroups of patients (all, extremely preterm, very preterm, moderately preterm, small for gestational age).</p> Results <p>Model accuracy was 0.71 (F1-score = Recall = AUROC = 0.71, Precision = 0.72) with nutritional features and 0.79 (F1-score = AUROC = 0.79, Precision = 0.80, Recall = 0.79) with all features. Lower EUGR risk was linked to female sex, higher growth velocity, and lipid intake in week one. Influential factors differed by subgroup.</p> Conclusion <p>ML models accurately predicted EUGR across preterm subgroups, highlighting the role of early nutritional and clinical variables.</p>

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AI to predict extrauterine growth restriction during transitional nutrition of preterm infants: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月13日

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  • Valentina Bozzetti,
  • Linda Greta Dui,
  • Emanuela Zannin,
  • Silvia Riccò,
  • Paola Coglianese,
  • Valeria Cavalleri,
  • Lucia Iozzi,
  • Maria Luisa Ventura,
  • Simona Ferrante

摘要

Objective

Extrauterine growth restriction (EUGR) affects 30–97% of preterm infants and is associated with poor outcomes. We used machine learning (ML) to assess how clinical and nutritional factors, particularly during the transition from parenteral to enteral nutrition, influence EUGR.

Study design

This retrospective observational study included 1165 patients (46% with EUGR) born below 33 weeks’ gestation or 1500 g. We developed 10 models to predict EUGR combining two sets of features (all and nutritional features only) across five subgroups of patients (all, extremely preterm, very preterm, moderately preterm, small for gestational age).

Results

Model accuracy was 0.71 (F1-score = Recall = AUROC = 0.71, Precision = 0.72) with nutritional features and 0.79 (F1-score = AUROC = 0.79, Precision = 0.80, Recall = 0.79) with all features. Lower EUGR risk was linked to female sex, higher growth velocity, and lipid intake in week one. Influential factors differed by subgroup.

Conclusion

ML models accurately predicted EUGR across preterm subgroups, highlighting the role of early nutritional and clinical variables.