Work

Using Vital Signs for the Early Prediction of Necrotizing Enterocolitis in Preterm Neonates with Machine Learning

Year: 2024

Type: article

Abstract: BACKGROUND/OBJECTIVES: Necrotizing enterocolitis (NEC), a devastating neonatal gastrointestinal disease mostly seen in preterm infants, lacks accurate prediction despite known risk factors. This hinders the possibility of applying targeted preventive therapies. This study explores the use of vital signs, including cerebral and splanchnic oxygenation, measured with near-infrared spectroscopy in early NEC prediction. METHODS: Several machine learning algorithms were trained on data from very preterm patients (<30 weeks gestational age). Time Series FeatuRe Extraction on the basis of scalable hypothesis tests (TSFRESH) extracted significant features from the vital signs of the first 5 postnatal days. We present the F1-scores and area under the precision-recall curve (AUC-PR) of the models. The contribution of separate vital signs to the selected TSFRESH features was also determined. RESULTS: < 0.001). Splanchnic and cerebral oxygenation contributed most to the prediction (40.1% and 24.8%, resp.). CONCLUSIONS: Using vital signs, we predicted NEC in the first 5 postnatal days with an F1-score up to 0.82. Splanchnic and cerebral oxygenation were the most contributing vital predictors. This pioneering effort in early NEC prediction using vital signs underscores the potential for targeted preventive measures and also emphasizes the need for additional data in future studies.

Source: Children

DOI: 10.3390/children11121452

Authors: Rosa Verhoeven, Thijmen Kupers, Celina L. Brunsch, Jan B. F. Hulscher, Elisabeth M. W. Kooi

Institutions: University Medical Center Groningen, University of Groningen, Beatrix Kinderziekenhuis

Language: English

Topic: Infant Nutrition and Health

Subfield: Nutrition and Dietetics

Field: Nursing

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,728