PheochromocytomaNCT07516275
Prospective Cohort Study of Pheochromocytoma/Paraganglioma
An observational study of Pheochromocytoma and Paraganglioma, run by Peking Union Medical College Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 99 years
- Conditions the study is for
- Pheochromocytoma, Paraganglioma, Hemodynamic Instability
- How many people
- 200 participants
- Where
- No sites listed yet
- Study dates
- Apr 2026to Feb 2028
- How long will I take part?
- 6 Weeks
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 99 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients diagnosed with pheochromocytoma or paraganglioma scheduled for elective surgical resection at Peking Union Medical College Hospital.
Who may be able to join
- 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.
Who cannot join
- 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.
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- 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).
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Validation and Extension Study of the Predictive Model for Intraoperative Hemodynamic Instability in Pheochromocytoma/Paraganglioma Resection - a Single-center, Prospective, Observational Cohort Study
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.
Read the 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.
- Keywords
- Pheochromocytoma, Paraganglioma, Hemodynamic Instability, Predictive Model, Artificial Intelligence, Continuous Glucose Monitoring, Prospective Cohort
- Medical subject headings
- Pheochromocytoma, Paraganglioma, Intraoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| PPGL Resection Cohort | 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: From preoperative assessment up to end of surgery
Secondary outcomes (6)
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: %
Measured: 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: %
Measured: 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: %
Measured: 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)
Measured: 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)
Measured: 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: %
Measured: Time Frame: Intraoperative period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Apr 2026
- Study startApr 2026 (estimated)
- Primary completionFeb 2028 (estimated)
- Study completionFeb 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PheochromocytomaStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
- Target duration
- 6 Weeks
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- LE SHEN, MD, PhD, Study chair, Peking Union Medical College Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07516275
- Sponsor's protocol ID
- PUMCH-PPGL-P1.0
- Status last verified
- Apr 2026
- First submitted
- 24 Mar 2026
- Last updated
- 7 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation and Extension Study of the Predictive Model for Intraoperative Hemodynamic Instability in Pheochromocytoma/Paraganglioma Resection - a Single-center, Prospective, Observational Cohort Study
- ClinicalTrials.gov ID
- NCT07516275
- Acronym / sponsor ID
- PUMCH-PPGL-P1.0
- Keywords
- Pheochromocytoma, Paraganglioma, Hemodynamic Instability, Predictive Model, Artificial Intelligence, Continuous Glucose Monitoring, Prospective Cohort
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Study start
- Apr 2026 (estimated)
- Primary completion
- Feb 2028 (estimated)
- Study completion
- Feb 2028 (estimated)
- First posted
- 7 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Weeks
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| PPGL Resection Cohort | – | –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. |
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients diagnosed with pheochromocytoma or paraganglioma scheduled for elective surgical resection at Peking Union Medical College Hospital.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (9 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predictive Model Discrimination (AUROC)
Time frame: 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).
6 secondary outcomes
Real-time Warning Tool Accuracy - Heart Rate (MAPE)
Time frame: Intraoperative period
Real-time Warning Tool Accuracy - Systolic Blood Pressure (MAPE)
Time frame: Intraoperative period
Real-time Warning Tool Accuracy - Diastolic Blood Pressure (MAPE)
Time frame: Intraoperative period
Predictive Model Calibration
Time frame: From preoperative assessment up to end of surgery
Predictive Model Clinical Utility (DCA)
Time frame: From preoperative assessment up to end of surgery
Real-time Warning Tool Accuracy - Mean Arterial Pressure (MAPE)
Time frame: Time Frame: Intraoperative period
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Apr 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
LE SHEN, MD, PhD study chair
Peking Union Medical College Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Pheochromocytoma and Paraganglioma, run by Peking Union Medical College Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 7 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact YE MA, MD (+86 18801015226, maye_thu16@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive Model Discrimination (AUROC) (time frame: From preoperative assessment up to end of surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Apr 2026.