Children
Machine Learning to Distinguish Physiologic Causes of Hypoxemia After Bidirectional Glenn Using Bedside Markers
By Fabio Savorgnan, Paola Pilla, Sarah Visokay and 3 others.
Abstract
Background
Hypoxemia after bidirectional Glenn palliation can result from distinct physiological mechanisms that may produce similar arterial oxygen saturation. We evaluated whether routinely available bedside markers could recover physiological states encoded in a mechanistic virtual-patient model.
Methods
A steady-state zero-dimensional closed-loop model generated five causes of Glenn hypoxemia: elevated pulmonary vascular resistance, veno-venous collateral shunting, pulmonary gas-exchange impairment, increased metabolic demand, and ventricular dysfunction. Hidden physiological variables were withheld from the classifier; inputs were SpO2, heart rate, mean arterial pressure, hemoglobin, Glenn/SVC pressure, and SVC oxygen saturation. Because bedside patterns arise downstream from prespecified equations and phenotype perturbations, classification was interpreted as internal mechanistic recoverability rather than clinical diagnostic performance.