Technological integration in predicting hypoxemia risk for improved surgical outcomes in Type A aortic dissection

Abstract

Background:

Postoperative hypoxemia is a severe complication in patients undergoing surgery for acute Type A aortic dissection (AAD), with significant impacts on recovery and clinical outcomes. Technological advancements in risk assessment models offer opportunities for early intervention and optimized care.

Objective:

To develop and validate a technology-driven predictive model for hypoxemia based on clinical and intraoperative risk factors, enhancing postoperative management strategies.

Methods:

A retrospective cohort of 242 patients was analyzed, including 77 with hypoxemia (PaO₂/FiO₂ ≤ 200 mmHg) and 165 without. Key clinical variables, intraoperative factors, and postoperative outcomes were examined. Spearman correlation analysis and receiver operating characteristic (ROC) curve analysis were conducted to identify and validate predictive markers.

Results:

Prolonged time from symptom onset to surgery (>48 h), aortic cross-clamp time, and deep hypothermic circulatory arrest time (DHCA) emerged as the most significant predictors (all p < 0.001). DHCA time demonstrated the highest sensitivity (0.961) and area under the curve (AUC = 0.891). Additional significant predictors included intraoperative blood product use and prolonged mechanical ventilation, with cumulative predictive value for hypoxemia risk.

Conclusion:

The integration of clinical variables into a technology-enhanced prediction model provides robust early warnings of postoperative hypoxemia risk. Implementing timely surgical interventions and refined intraoperative management can minimize adverse respiratory outcomes, improving recovery in AAD patients.