Abstract
Randomized trials comparing high-flow nasal cannula (HFNC) and non-invasive positive pressure ventilation (NIV) for acute respiratory failure (ARF) offer population-level guidance but often fail to capture individual variability in treatment response. In this retrospective study, we identified intensive care units (ICU) patients at risk of invasive mechanical ventilation (IMV) using a previously published risk prediction model. Patients who first received HFNC or NIV after crossing the high-risk threshold formed the early treatment cohort. We developed a deep counterfactual model that integrates representation learning, conditional normalizing flows, and confounder adjustment to estimate individualized treatment effects (ITEs) between HFNC and NIV. Treatment concordance, defined as alignment between the model's recommendation and the treatment actually administered, was assessed using multivariate logistic regression. At UC San Diego Health (UCSD), concordant treatment was associated with significantly reduced odds of IMV (odds ratio [OR] = 0.661 for NIV; 0.677 for HFNC) and mortality or hospice discharge (OR = 0.679 for NIV; 0.749 for HFNC). At UC Irvine Health (UCI), concordant treatment was also linked to improved outcomes, particularly for mortality or hospice discharge (OR = 0.092 for NIV; 0.088 for HFNC). These findings highlight the value of individualized, model-guided respiratory support strategies in improving outcomes for critically ill patients.