Development and validation of a predictive model for predicting cardiovascular morbidity in patients after pheochromocytoma surgery

Objective Although surgical resection is the primary treatment method for pheochromocytoma, it carries a high risk of morbidity, especially cardiovascular‐related morbidity. There are no models for predicting cardiovascular morbidity after pheochromocytoma surgery. Thus, we developed and validated a model for the preoperative prediction of cardiovascular morbidity after pheochromocytoma surgery. Design The development cohort consisted of 262 patients who underwent unilateral laparoscopic or open pheochromocytoma surgery at our centre between 1 January 2007 and 31 December 2016. Patient's clinicopathologic data were recorded. The LASSO regression was used for data dimension reduction and feature selection; then, multivariable logistic regression analysis was used to develop the prediction model. An independent cohort consisting of 112 consecutive patients from 1 January 2017 and 31 December 2018 was used for validation. The performance of this prediction model was assessed with respect to discrimination, calibration and clinical usefulness. Results The predictors in this prediction model included body mass index, history of coronary heart disease, tumour size, intraoperative hemodynamic instability and use of crystal/colloid fluids preoperatively. In the validation cohort, the model showed good discrimination with an AUROC of 0.869 (95% CI, 0.797, 0.940) and good calibration (unreliability test, P = .852). Decision curve analysis demonstrated that the model was also clinically useful. Conclusion This study presented a good nomogram that could facilitate the preoperative individualized prediction of cardiovascular morbidity after pheochromocytoma surgery, which may help improve perioperative strategy and good treatment outcomes.