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  • Clinical Research Article
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Reassessing acquired neonatal intestinal diseases using unsupervised machine learning

Pediatric Research (2024)Cite this article

Abstract

Background

Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning.

Methods

Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013–September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis.

Results

Five clusters of intestinal injury were identified: Cluster 1 deemed the “Low Mortality” cluster, Cluster 2 deemed the “Mature with Inflammation” cluster, Cluster 3 deemed the “Immature with High Mortality” cluster, Cluster 4 deemed the “Late Injury at Full Feeds” cluster, and Cluster 5 deemed the “Late Injury with High Rate of Intestinal Necrosis” cluster.

Conclusion

Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases.

Impact

  • Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury.

  • Five major clusters of acquired neonatal intestinal injury are described, each with unique features.

  • The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.

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Fig. 1: Heatmap and corresponding hierarchal dendrogram.

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Data availability

The dataset generated and analyzed during this study is included as a supplement.

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Funding

No financial assistance was received in support of this study.

Author information

Author notes

  1. These authors jointly supervised this work: Nima Aghaeepour, Josef Neu.

Authors and Affiliations

  1. University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA

    Daniel R. Gipson, Diomel de la Cruz & Josef Neu

  2. Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA

    Alan L. Chang & Nima Aghaeepour

  3. Nationwide Children’s Hospital, The Ohio State University College of Medicine, Department of Pediatrics, Division of Neonatology, Columbus, OH, USA

    Allison C. Lure

  4. University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA

    Allison C. Lure, Sonia A. Mehta, Taylor Gowen & Erin Shumans

  5. University of California, Irvine Medical Center, Department of Pediatrics, Division of Neonatology, Irvine, CA, USA

    Sonia A. Mehta

  6. University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, USA

    Taylor Gowen

  7. Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA

    David Stevenson

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  1. Daniel R. Gipson
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Contributions

D.G., A.L., N.A., and J.N. contributed to conception and design of the study. D.G., A.L., S.M., T.G., and E.S. contributed significantly to the acquisition of data. A.C. and N.A. performed data analysis, used machine learning for hierarchal clustering, and performed statistical analysis. D.G., A.C., N.A., D.S., D.C., and J.N. were involved with drafting, revising, and final approval of the article.

Corresponding author

Correspondence to Daniel R. Gipson.

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Competing interests

Dr. Aghaeepour is a member of the Scientific Advisory Boards of January AI, Parallel Bio, Celine Therapeutics, and WellSim Biomedical Technologies, is a paid consultant for MaraBio Systems, and is Cofounder and the Chief Scientist of Takeoff AI. Dr. Neu serves on the Scientific Advisory Boards of Astarte, Medela, and Arla Foods; serves on the Global Scientific Council for the Nestle Nutrition Institute; serves as a consultant for Glycome and Siolta Therapeutics; and has a research grant through Infant Bacterial Therapeutics. The remaining authors have no conflicts of interest to be reported.

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Patient consent was not required to perform this study.

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Gipson, D.R., Chang, A.L., Lure, A.C. et al. Reassessing acquired neonatal intestinal diseases using unsupervised machine learning. Pediatr Res (2024). https://doi.org/10.1038/s41390-024-03074-x

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