Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants

Moon-Yeon Oh; Sol Kim; Minsoo Kim; Yu Mi Seo; Sook Kyung Yum

doi:10.1016/j.pedneo.2024.09.003

Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants

Pediatr Neonatol. 2024 Sep 25:S1875-9572(24)00163-3. doi: 10.1016/j.pedneo.2024.09.003. Online ahead of print.

Authors

Moon-Yeon Oh¹, Sol Kim¹, Minsoo Kim¹, Yu Mi Seo², Sook Kyung Yum³

Affiliations

¹ Department of Pediatrics, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea; Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
² Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea; Department of Pediatrics, Incheon St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
³ Department of Pediatrics, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea; Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. Electronic address: neoped@catholic.ac.kr.

PMID: 39370350
DOI: 10.1016/j.pedneo.2024.09.003

Abstract

Background: Unlike in adult and pediatric patients, the usefulness of lactate in preterm infants has not been thoroughly discussed. This study aimed to evaluate whether the lactate level in the first hours of life is an important factor associated with neonatal death in very-low-birth-weight (VLBW) preterm infants.

Methods: Electronic medical records from a level 4 neonatal intensive care unit in South Korea were reviewed to obtain perinatal and neonatal outcomes. Data on lactate levels of preterm infants in the first 12 h of life were collected. Neonatal mortality and morbidities were compared based on lactate levels. Subsequently, machine-learning models incorporating 20 independent variables, both with and without lactate, were compared for model performances and feature importance of lactate for predicting in-hospital mortality in the applicable models.

Results: One hundred and sixty-eight preterm infants were included. Death rates on days 7 and 30 of life (D30-mortality) were significantly higher in infants with high lactate levels (≥3rd interquartile range) than in those with lower levels (<3rd interquartile range). Though statistically insignificant, the overall in-hospital mortality was more than twice as high in the high lactate level group than in the lower lactate level group. Based on the machine learning results, Random Forest, Gradient Boosting, and LightGBM models all showed greater area under the curves when lactate was included. Lactate consistently ranked in the variables of top five feature importance, particularly showing the greatest value in the Gradient Boosting model.

Conclusion: Lactate levels during the early hours of life may be an important factor associated with in-hospital death of preterm VLBW infants. Based on the enhanced performance of the above-mentioned machine learning models, lactate levels in the early postnatal period may add to assessing the clinical status and predicting the hospital course in this population.

Keywords: Lactate; Machine learning; Mortality; Preterm; Very-low-birth-weight.