A nomogram for predicting intra-operative conversion to endotracheal intubation during non-intubated spontaneous ventilation anesthesia in pulmonary resection: development of a risk prediction model in hypoxic and high-risk patients - Lin, Tao; Zhang, Bing; Chen, Lei; Mei, Jialin; Zhu, Yongyue; Gao, Fei; Dong, Jihao; Bao, Yang; Li, Gaofeng (Frontiers in Medicine, 2025)
BackgroundNon-intubated spontaneous ventilation anesthesia (NISVA) avoids complications associated with endotracheal intubation in pulmonary resection. However, intraoperative conversion to endotracheal intubation (IETI) occurs in significant numbers of patients. This study aimed to develop and validate a predictive model for IETI risk during NISVA -based pulmonary resection.MethodsThis retrospective cohort study included 244 patients undergoing pulmonary resection under NISVA from January 2019 to December 2024. Patients were randomly divided into training (n = 170) and validation (n = 74) sets. Independent risk factors for IETI were identified using LASSO regression and multivariate logistic regression. A nomogram prediction model was constructed and validated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).ResultsThe IETI incidence was 45.49% (111/244). Five independent risk factors were identified: preoperative hypoxemia (OR = 2.973, 95% CI: 1.249–7.340), surgical site (lower lobe) (OR = 2.462, 95% CI: 1.055–5.827), Type of surgery (lobectomy) (OR = 3.600, 95% CI: 1.575–8.559), difficult airway (OR = 4.708, 95% CI: 1.984–11.87), and surgical duration ≥ 3 h (OR = 11.81, 95% CI: 4.617–33.96). The nomogram demonstrated excellent discrimination with AUCs of 0.889 (training) and 0.880 (validation). Calibration curves showed good agreement between predicted and observed probabilities. DCA indicated clinical utility across threshold probabilities of 5–85%.ConclusionThis novel nomogram accurately predicts IETI risk during NISVA -based pulmonary resection, enabling individualized preoperative assessment and optimization of anesthesia strategies. The model shows potential for improving surgical safety and patient outcomes in non-intubated thoracic surgery.