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

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.

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.

Of 150,000 generated states, 127,162 passed broad physiological criteria and 66,269 met strict hypoxemia/hemodynamic criteria. After balancing, 26,400 virtual patients were analyzed (5280 per mechanism; 19,800 training and 6600 held-out testing). SpO₂ alone achieved 26.1% accuracy (macro-AUROC 0.580); the primary bedside model achieved 56.1% accuracy (95% CI 55.0-57.4%) and macro-AUROC 0.845 (95% CI 0.840-0.851). Elevated PVR was the most readily recovered phenotype. Glenn/SVC pressure and SVC saturation provided the largest incremental contributions, while broader measurement-error assumptions substantially reduced performance.

Within this mechanistic simulation, multivariable bedside patterns allowed partial recovery of physiological states encoded by the model. Because phenotypes and their observable consequences were generated from prespecified assumptions, the reported performance quantifies internal recoverability under those assumptions and should not be interpreted as diagnostic accuracy or as independent evidence of bedside information content in patients. The model identifies testable physiologic signatures and areas of overlap for prospective clinical validation.