Machine Learning to Distinguish Physiologic Causes of Hypoxemia After Bidirectional Glenn Using Bedside Markers

Background: Hypoxemia after bidirectional Glenn palliation can result from several distinct physiologic mechanisms that may produce similar arterial oxygen saturation. We evaluated whether routinely available bedside markers can help distinguish these mechanisms 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. The underlying physiologic variables responsible for each mechanism were hidden from the classifier. Machine-learning models used only bedside-observable variables: SpO₂, heart rate, mean arterial pressure, hemoglobin, Glenn/SVC pressure, and SVC oxygen saturation. Multinomial logistic regression was evaluated in a held-out test cohort, with random forest as a nonlinear comparator.Results: Among 150,000 generated states, 70,529 met prespecified hypoxemia and physiologic criteria; after class balancing, 26,905 virtual patients were analyzed. SpO₂ alone provided limited mechanistic discrimination (accuracy 26.8%; macro-AUROC 0.584). The multivariable bedside model improved accuracy to 59.1% (95% CI 57.9%–60.2%) and macro-AUROC to 0.865 (95% CI 0.860–0.871). Elevated pulmonary vascular resistance was most readily identified, with Glenn/SVC pressure providing the largest incremental contribution to classification. Veno venous collateral shunting and pulmonary gas-exchange impairment remained the principal areas of overlap. When compensatory systemic vasoconstriction was incorporated into ventricular dysfunction, systemic flow could fall despite preserved mean arterial pressure, reducing the discriminatory value of blood pressure alone.Conclusions: Bedside interpretation of Glenn hypoxemia should extend beyond arterial oxygen saturation. In this mechanistic model, Glenn/SVC pressure and venous oxygen saturation substantially improved distinction among physiologic causes of hypoxemia, while preserved arterial pressure did not reliably indicate preserved systemic flow. These findings support a physiology-based, multivariable approach to evaluating Glenn hypoxemia and provide specific hypotheses for prospective clinical validation.