An observational study in Pheochromocytoma, Paraganglioma and Hemodynamic Instability, sponsored by Peking Union Medical College Hospital. Not yet recruiting. Open to participants aged 18 Years to 99 Years. Per ClinicalTrials.gov, last updated 2026-04-07.
Sponsored by Peking Union Medical College Hospital · Observational
The study will enroll patients scheduled for PPGL removal surgery at Peking Union Medical College Hospital. Before surgery, researchers will use a 6-variable model to predict the patient's risk of experiencing severe blood pressure swings during the operation. During surgery, a real-time early warning tool will be tested for its ability to accurately predict blood pressure changes 60 seconds in advance. The study will also explore the value of continuous glucose monitoring (CGM) in understanding blood pressure fluctuations and evaluate the performance of an artificial intelligence (AI) agent for preoperative anesthesia assessment, comparing its accuracy, consistency, and efficiency against that of human anesthesiologists.
Participation involves no changes to the patient's standard surgical or medical care. It includes collecting clinical data, wearing a CGM sensor from the day before to the day after surgery, and having the preoperative assessment performed by both the AI agent and anesthesiologists.
Background: Resection of pheochromocytoma/paraganglioma (PPGL) carries a high risk of intraoperative hemodynamic instability (HDI) due to catecholamine release. Existing predictive models have limitations, and novel tools require prospective validation. This study aims to address this gap.
Objective: The primary objectives are to: 1) Prospectively validate a previously developed 6-variable model for predicting severe HDI subtypes; 2) Assess the accuracy and timeliness of a real-time intraoperative early warning tool for HDI events; 3) Explore the association between continuous glucose monitoring (CGM) metrics and intraoperative HDI; and 4) Evaluate the clinical utility of an AI-based preoperative anesthesia assessment agent against human assessors.
Methods: This is a single-center, prospective, observational cohort study at Peking Union Medical College Hospital. Eligible patients (≥18 years) scheduled for elective PPGL resection will be enrolled.
Prediction Model Validation: Preoperative data (symptoms, hemoglobin, tumor functional status, epinephrine/norepinephrine elevation multiples, phenoxybenzamine dose) will be used to predict the HDI subtype (mild vs. severe). The predicted subtype will be compared against the actual subtype determined by post-hoc K-means clustering of intraoperative hemodynamic data (24 metrics).
Real-time Warning Tool Validation: The tool will be used intraoperatively to predict vital signs (SBP, DBP, MAP, HR) 60 seconds ahead based on the preceding 200 seconds of data. Its predictions will be compared against actual monitored values, and its sensitivity/specificity for predicting hypertensive, hypotensive, and tachycardic events will be calculated.
CGM Exploration: Patients will wear a CGM sensor from the day before surgery to one day after. Metrics like mean glucose, glycemic variability, and time in hypoglycemia/hyperglycemia will be analyzed for their association with intraoperative HDI outcomes using multivariable regression.
AI Agent Evaluation: Each patient will undergo paired preoperative assessments: one by the AI agent and one by a junior anesthesiologist (≤5 years experience). A senior anesthesiologist (≥10 years experience) will provide the reference standard for risk stratification, tumor functionality, and preparation adequacy. Accuracy, inter-rater agreement (Kappa), and assessment time will be compared between the AI and junior anesthesiologist.
Outcomes: Primary outcomes include the Area Under the ROC Curve (AUROC) for the prediction model, the Mean Absolute Percentage Error (MAPE) for the warning tool, the occurrence of intraoperative HDI for the CGM analysis, and the accuracy of the AI agent's risk stratification. Sample sizes have been calculated for each sub-study, with a total target enrollment of approximately 202 participants to meet all objectives.
149 studies on the registry are indexed under Pheochromocytoma; 56 are open to participants now.
This study's planned enrollment of 200 is close to the median of 186 across 54 observational studies indexed under Pheochromocytoma.
Browse Pheochromocytoma studies →Peking Union Medical College Hospital is the lead sponsor of 1,115 studies on the registry; 463 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients diagnosed with pheochromocytoma or paraganglioma scheduled for elective surgical resection at Peking Union Medical College Hospital.
Exclusion Criteria:
Patients diagnosed with pheochromocytoma or paraganglioma who are scheduled for elective surgical resection. All participants will undergo the same study procedures: preoperative data collection, CGM monitoring, AI and human preoperative assessment, intraoperative application of the real-time warning tool, and postoperative follow-up. No intervention is applied; this is purely observational.
Predictive Model Discrimination (AUROC)
Discriminative ability of the preoperative 6-variable model for predicting severe intraoperative hemodynamic instability (HDI) subtype, assessed by the Area Under the Receiver Operating Characteristic Curve (AUROC).
Time frame: From preoperative assessment up to end of surgery
Real-time Warning Tool Accuracy - Heart Rate (MAPE)
Accuracy of the intraoperative early warning tool for heart rate, measured by the Mean Absolute Percentage Error (MAPE) between its predicted and actual monitored heart rate values. Unit of Measure: %
Time frame: Intraoperative period
Real-time Warning Tool Accuracy - Systolic Blood Pressure (MAPE)
Accuracy of the intraoperative early warning tool for blood pressure parameters, measured by the Mean Absolute Percentage Error (MAPE) between its predicted and actual monitored values for systolic blood pressure (SBP). Unit of Measure: %
Time frame: Intraoperative period
Real-time Warning Tool Accuracy - Diastolic Blood Pressure (MAPE)
Accuracy of the intraoperative early warning tool for blood pressure parameters, measured by the Mean Absolute Percentage Error (MAPE) between its predicted and actual monitored values for diastolic blood pressure (DBP). Unit of Measure: %
Time frame: Intraoperative period
Predictive Model Calibration
Calibration of the preoperative 6-variable predictive model, assessed by the Hosmer-Lemeshow goodness-of-fit test (p-value), calibration curve, and Brier score. Unit of Measure: p-value (dimensionless), Brier score (dimensionless)
Time frame: From preoperative assessment up to end of surgery
Predictive Model Clinical Utility (DCA)
Clinical utility of the preoperative model, assessed by Decision Curve Analysis (DCA) to evaluate net clinical benefit at different threshold probabilities. Unit of Measure: Net benefit (dimensionless probability)
Time frame: From preoperative assessment up to end of surgery
Real-time Warning Tool Accuracy - Mean Arterial Pressure (MAPE)
Accuracy of the intraoperative early warning tool for blood pressure parameters, measured by the Mean Absolute Percentage Error (MAPE) between its predicted and actual monitored values for mean arterial pressure (MAP). Unit of Measure: %
Time frame: Time Frame: Intraoperative period
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Plan to share: No
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Peking Union Medical College Hospital