Nomogram prediction model for delayed extubation in patients undergoing robotic-assisted radical prostatectomy

Delayed extubation after anesthesia can lead to adverse clinical outcomes. We aimed to identify the risk factors for delayed extubation in patients undergoing robot-assisted radical prostatectomy and to develop a visualized nomogram prediction model for clinical use. A total of 624 patients were enrolled and divided into training group, validation group, and temporal validation group. The training group was utilized to develop a nomogram, whereas the validation group and temporal validation group were used to assess its performance. LASSO regression was employed to refine variables and select predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the model was internally validated using calibration and receiver operating characteristic curves. Additionally, decision curve analysis and clinical impact curves were used to assess the clinical utility of the model. Patients who underwent robot-assisted radical prostatectomy between January 2022 and April 2024 were included and divided into a training group (n = 389), a validation group (n = 98), and a temporal validation group (n = 137). Logistic regression identified cerebral infarction, pulmonary disease, coronary heart disease, age, and intraoperative hypotension as independent predictors of delayed extubation. A nomogram constructed based on these factors demonstrated excellent predictive performance, with area under the curve values of 0.763 (95% CI: 0.717–0.810) in the training group, 0.811 (95% CI: 0.726–0.897) in the validation group, and 0.769 (95% CI: 0.689–0.848) in the temporal validation group. Across all three groups, the model demonstrated a good fit, as indicated by a non-significant P-value from the Hosmer–Lemeshow test, and the calibration curves indicated a strong alignment between the predictions and actual observations. Furthermore, decision curve analysis and clinical impact curve demonstrated the clinical efficiency and benefits of the prediction model. This study identified key risk factors for delayed extubation and established an effective predictive nomogram with high discriminative power and clinical applicability for predicting the risk of extubation delay in patients undergoing robot-assisted radical prostatectomy. The Medical Ethics Committee of Nanjing Drum Tower Hospital granted ethical approval for this research(grant number: 2024–742-01).