Hemodynamic Instability, Intraoperative Complications, Paraganglioma, Pheochromocytoma
Conditions
Keywords
Pheochromocytoma, Paraganglioma, Hemodynamic Instability, Predictive Model, Artificial Intelligence, Continuous Glucose Monitoring, Prospective Cohort
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Peking Union Medical College Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 99 Years
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years. * Diagnosed with pheochromocytoma/paraganglioma by imaging and laboratory tests and scheduled for elective resection. * Planned intraoperative continuous invasive arterial pressure monitoring. * Willing and able to provide written informed consent. * Able to comply with preoperative CGM monitoring, AI agent assessment, and postoperative follow-up.
Exclusion criteria
* Intraoperative hemodynamic data missing ≥20%. * Postoperative histopathology excludes PPGL diagnosis. * Cardiac paraganglioma or metastatic PPGL. * Severe cardiac disease (e.g., severe valvular disease, severe heart failure) that could independently cause intraoperative HDI. * Pregnancy or breastfeeding. * Mental illness, cognitive impairment, or communication barriers that prevent compliance with study procedures. * Refusal to undergo CGM monitoring or AI agent assessment. * Major illness (e.g., acute myocardial infarction, stroke, severe infection) within 3 months prior to surgery. * Severe hepatic or renal insufficiency (Child-Pugh class C, eGFR \<30 ml/min/1.73 m²).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive Model Discrimination (AUROC) | From preoperative assessment up to end of surgery | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Real-time Warning Tool Accuracy - Heart Rate (MAPE) | Intraoperative period | 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: % |
| Real-time Warning Tool Accuracy - Systolic Blood Pressure (MAPE) | Intraoperative period | 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: % |
| Real-time Warning Tool Accuracy - Diastolic Blood Pressure (MAPE) | Intraoperative period | 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: % |
| Predictive Model Calibration | From preoperative assessment up to end of surgery | 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) |
| Predictive Model Clinical Utility (DCA) | From preoperative assessment up to end of surgery | 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) |
| Real-time Warning Tool Accuracy - Mean Arterial Pressure (MAPE) | Time Frame: Intraoperative period | 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: % |
Contacts
CONTACTYE MA, MD
STUDY_CHAIRLE SHEN, MD, PhD
Peking Union Medical College Hospital
Outcome results
None listed