Automated Oxygen Adjustment for Sustaining Normoxemia in Critically Ill Adults: Results from the SAVE-O 2 AI Trial
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By
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David J. Douin
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John D. Rice
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Mengli Xiao
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Laurel E. Beaty
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Claire J. Guo
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Cori Withers
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Amy Sullivan
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Erin L. Anderson
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Alex C. Cheng
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Mary K. Banasiewicz
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Matthew W. Semler
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Bradley D. Lloyd
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Amelia W. Maiga
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Kevin W. Gibbs
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Gregory R. Stettler
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Akram Khan
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Mitchell B. Sally
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Franklin L. Wright
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Arek J. Wiktor
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Neil R. Aggarwal
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Vikhyat S. Bebarta
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Adit A. Ginde
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SAVE-O AI Investigators
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Laura G. Murphy
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Erika V. Alor
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Yvette R. Evans
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Laura A. Aguilar-Marquez
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Jessica Cwik
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Katie Gray
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Dagmawit Getachew
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April Johnson
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Jakea D. Johnson
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Karen F. Miller
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Tracy L. Morgan
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Shannon K. Pugh
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Edward T. Qian
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Colleen Ratcliff
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Sabrina A. Shipman
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Adam Turner
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Lakeysha C. Wiggins
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Genesis Briceno
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Aidan Flieger
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Nola Iwasaki
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Riya Mathew
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Chloe K. Ouchida
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Jose Pena
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Edvinas Pocius
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Emily Tribbet
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Samantha Underwood
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Heather L. Clark
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Copeland Graham
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Leigha Landreth
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Lisa Parks
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Darija Ward
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October 1, 2026
3 Topic Commentaries
Intracranial Hemorrhages, Central Nervous System Infections, Machine Learning
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Dr. Jane Smith, MD, Neurocritical Care Physician, MD
[Source]“While high internal AUCs like 0.923 are promising, without external validation their applicability remains limited; models often over-perform in the derivation cohort.”
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Dr. Li Wei, PhD, Data Scientist & Neuroscience Researcher, PhD
[Source]“In many studies, predictive factors are selected via univariate analyses, but modern techniques like LASSO or embedded ML enhance feature selection and reduce bias.”
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Dr. Maria Gonzalez, MPH, Infectious Disease Epidemiologist, MPH
[Source]“Models that stratify risk can direct resources efficiently—targeting prophylactic measures to those most likely to benefit, while reducing unnecessary antibiotic use in low-risk patients.”
