Prediction of Tracheal Intubation in Patients with Pierre Robin Sequence: Development of A Machine Learning-Based Model

Patients with Pierre Robin sequence (PRS) frequently require multiple attempts for airway stabilization. However, there is currently no standardized algorithm to identify anatomical landmarks that predict the difficulty of tracheal intubation and its clinical impact. This study aimed to determine predictive factors associated with multiple attempts at airway stabilization in PRS patients undergoing mandibular distraction osteogenesis (MDO) and to evaluate their influence on clinical outcomes. A derivation cohort comprising 348 PRS patients who underwent MDO January 2007 and June 2023 was included; An external validation cohort of 87 PRS patients at the same period was analyzed. Machine learning and multivariate regression analyses were employed to examine the relationship between tracheal intubation outcomes and anatomical measurements. Five variables were independently associated with increased risk: Mandibular angle, tongue length, mouth opening, palatopharyngeal flow velocity, and traction length of machine disconnection. The XGBoost model demonstrated superior performance, achieving an area under curve (AUC) of 0.952 after parameter tuning. Decision curve analysis revealed a threshold probability of 84%. Similar results were observed in the external validation cohort (AUC = 0.943). These five-factor model could effectively identify the risk of adverse events in PRS patients undergoing MDO, providing a valuable tool for clinical decision-making.