Nomogram for Predicting Intraoperative Hemodynamic Instability in Patients With Normotensive Pheochromocytoma

Abstract

Context: Intraoperative hemodynamic instability (HI) deteriorates surgical outcomes of patients with normotensive pheochromocytoma (NP).

Objective: To characterize the hemodynamics of NP and develop and externally validate a prediction model for intraoperative HI.

Methods: Data on 117 patients with NP (derivation cohort) and 40 patients with normotensive adrenal myelolipoma (NAM) who underwent laparoscopic adrenalectomy from January 2011 to November 2021 were retrospectively collected. Data on 22 patients with NP (independent validation cohort) were collected from another hospital during the same period. The hemodynamic characteristics of patients with NP and NAM were compared. Machine learning models were used to identify risk factors associated with HI. The final model was visualized via a nomogram.

Results: Forty-eight (41%) out of 117 patients experienced HI, which was significantly more than that for NAM. A multivariate logistic regression including age, tumor size, fasting plasma glucose, and preoperative systolic blood pressure showed good discrimination measured by area under curve (0.8286; 95% CI 0.6875-0.9696 and 0.7667; 95% CI 0.5386-0.9947) for predicting HI in internal and independent validation cohorts, respectively. The sensitivities and positive predictive values were 0.6667 and 0.7692 for the internal and 0.9167 and 0.6111 for the independent validations, respectively. The final model was visualized via a nomogram and yielded net benefits across a wide range of risk thresholds in decision curve analysis.

Conclusion: Patients with NP experienced HI during laparoscopic adrenalectomy. The nomogram can be used for individualized prediction of intraoperative HI in patients with NP.

Keywords: hemodynamic instability; intraoperative; machine learning; nomogram; normotensive; pheochromocytoma.