Determining an Optimal Oxygen Saturation Target Range Based on Neonatal Maturity: Demonstration of a Decision Tree Analytic.

Thomas E Bachman, Narayan P Iyer, Christopher J L Newth, Robert LeMoyne

Abstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Thomas E BachmanFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.

Narayan P IyerChildren's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA 90027, USA.

Christopher J L NewthChildren's Hospital Los Angeles, University of Southern California Keck School of Medicine, Los Angeles, CA 90027, USA.ORCID 0000-0002-5705-5227

Robert LeMoyneFaculty of Biomedical Engineering, Czech Technical University in Prague, 272 01 Kladno, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The utility of decision tree machine learning in exploring the interactions among the SpO

methodsThis observational study used 3 years of paired age-SpO

resultsIn total, 7500 paired data points within the potential target range envelope were analyzed. The two lowest target ranges were associated with the highest risk, and the ranges of 91-95% and 92-96% were associated with the lowest risk. There were shifts in the risk associated with PMA. All the target ranges showed the lowest risk at ≥42 weeks PMA. The lowest risk for preterm infants was within a target range of 92-96% with a PMA of ≤34 weeks.

conclusionsThis study demonstrates the utility of decision tree analytics. These results suggest that SpO

Indexed as

decision tree classificationhyperoxemic-riskhypoxemic-riskmachine learningneonataloxygen saturation targeting

Identifiers

PMID37958208

DOI10.3390/diagnostics13213312

PMCPMC10648394

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