UNLABELLED: The utility of decision tree machine learning in exploring the interactions among the SpO2 target range, neonatal maturity, and oxemic-risk is demonstrated. METHODS: This observational study used 3 years of paired age-SpO2-PaO2 data from a neonatal ICU. The CHAID decision tree method was used to explore the interaction of postmenstrual age (PMA) on the risk of extreme arterial oxygen levels at six different potential SpO2 target ranges (88-92%, 89-93%, 90-94%, 91-95%, 92-96% and 93-97%). Risk was calculated using a severity-weighted average of arterial oxygen outside the normal range for neonates (50-80 mmHg). RESULTS: In 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. CONCLUSIONS: This study demonstrates the utility of decision tree analytics. These results suggest that SpO2 target ranges that are different from typical range might reduce morbidity and mortality. Further research, including prospective randomized trials, is warranted.

• Klíčová slova
• decision tree classification, hyperoxemic-risk, hypoxemic-risk, machine learning, neonatal, oxygen saturation targeting,
• Publikační typ
• časopisecké články MeSH