Abstract
PURPOSE: Although resection is the primary treatment strategy for pheochromocytoma, surgery is associated with a high risk of morbidity. At present, there is no nomogram for prediction of severe morbidity after pheochromocytoma surgery, thus the aim of the present study was to develop and validate a nomogram for prediction of severe morbidity after pheochromocytoma surgery. METHODS: The development cohort consisted of 262 patients who underwent unilateral laparoscopic or open pheochromocytoma surgery at our center between 1 January 2007 and 31 December 2016. The patients' clinicopathological characters were recorded. The least absolute shrinkage and selection operator (LASSO) binary logistic regression model was used for data dimension reduction and feature selection, then multivariable logistic regression analysis was used to develop the predictive model. An independent validation cohort consisted of 128 consecutive patients from 1 January 2017 and 31 December 2018. The performance of the predictive model was assessed in regards to discrimination, calibration, and clinical usefulness. RESULTS: Predictors of this model included sex, BMI, coronary heart disease, arrhythmia, tumor size, intraoperative hemodynamic instability, and surgical duration. For the validation cohort, the model showed good discrimination with an AUROC of 0.818 (95% CI, 0.745, 0.891) and good calibration (Unreliability test, P = 0.440). Decision curve analysis demonstrated that the model was also clinically useful. CONCLUSIONS: A nomogram was developed to facilitate the individualized prediction of severe morbidity after pheochromocytoma surgery and may help to improve the perioperative strategy and treatment outcome.