Nomogram for predicting severe morbidity after pheochromocytoma surgery

Area of Science:

• Endocrinology
• Surgical Oncology
• Medical Informatics

Background:

• Pheochromocytoma surgery, while curative, carries significant risks of severe morbidity.
• Current clinical practice lacks a validated tool for predicting these surgical complications.
• Developing a predictive model is crucial for optimizing patient care and outcomes.

Purpose of the Study:

• To develop and validate a nomogram for predicting severe morbidity following pheochromocytoma surgery.
• To identify key clinical and surgical factors associated with severe postoperative complications.

Main Methods:

• A predictive model was developed using data from 262 patients undergoing pheochromocytoma surgery (2007-2016).
• Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariable logistic regression were employed.
• Model performance was validated in an independent cohort of 128 patients (2017-2018).

Main Results:

• The final model incorporated predictors including sex, BMI, comorbidities (coronary heart disease, arrhythmia), tumor size, hemodynamic instability, and surgical duration.
• The nomogram demonstrated good discrimination (AUROC 0.818) and calibration in the validation cohort.
• Decision curve analysis confirmed the clinical utility of the developed predictive model.

Conclusions:

• A validated nomogram is now available for individualized prediction of severe morbidity after pheochromocytoma surgery.
• This tool can assist clinicians in refining perioperative strategies and potentially improving treatment outcomes.
• The nomogram facilitates personalized risk assessment for patients undergoing pheochromocytoma resection.