Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer.

Background: In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.

Objective: This research is designed to construct and validate a predictive model for assessing the risk of postoperative complications in patients undergoing robotic-assisted radical gastrectomy.

Methods: A retrospective analysis was conducted on 500 GC patients from Gansu Provincial People's Hospital between December 2016 and October 2023. These patients formed the training cohort. An additional 136 patients from the 940th Hospital of Joint Logistic Support Force, the Chinese People's Liberation Army as the external validation cohort. Patients were categorized into groups with and without complications. Data collected included demographic details, laboratory results, CT quantitative body composition analysis, and clinical information. Variable selection was conducted through Lasso regression, succeeded by multivariable logistic regression to pinpoint independent risk factors. These elements facilitated the construction of a nomogram for prediction. The model's performance underwent internal validation via bootstrap techniques and external validation through a validation cohort. The efficacy of the model was quantified by the area under the receiver operating characteristic (ROC) curve (AUC), evaluated for calibration using calibration curves and the Hosmer-Lemeshow test, and assessed for clinical utility through decision curve analysis (DCA).

Results: Of the 500 patients in the training cohort, 65 experienced complications, a rate of 13%. The validation cohort had a similar complication rate of 13.24% (18 out of 136 patients). Independent risk factors identified included tumor diameter (OR = 1.99, 95% CI = 1.07-3.73), TNM stage III (OR = 2.12, 95% CI = 1.03-4.36), ASA class I (OR = 0.26, 95% CI = 0.13-0.53), ASA class III (OR = 4.75, 95% CI = 2.12-10.62), and visceral fat area (VFA) (OR = 2.52, 95% CI = 1.10-5.79). The nomogram demonstrated good discrimination (AUC = 0.81, 95% CI: 0.76-0.87) in internal validation and (AUC = 0.79, 95% CI: 0.67-0.90) in external validation. Both validations confirmed the model's accurate calibration and significant clinical utility, with net benefits observed at probability thresholds ranging from 2 to 79% and 2-71%.

Conclusion: The developed nomogram, based on five independent risk factors-tumor diameter, TNM stage III, ASA class I, ASA class III, and VFA-effectively predicts the risk of complications in patients undergoing robotic-assisted radical gastrectomy, offering a valuable tool for clinical decision-making.