Development of a Baroreflex Sensitivity-Based Multifactorial Machine Learning Model for Predicting Post-Induction Hypotension in Elderly Patients
Status: Recruiting
Location: See location...
Study Type: Observational
SUMMARY
The purpose of this study is to develop a high-performance machine learning model combining dynamic baroreflex sensitivity (BRS) metrics and multi-dimensional static clinical features to predict the risk of post-induction hypotension (PIH) in elderly patients undergoing elective non-cardiac surgery under general anesthesia.
Eligibility
Participation Requirements
Sex: All
Minimum Age: 65
Healthy Volunteers: f
View:
• Aged over 65 years;
• Scheduled for elective non-cardiac surgery;
• American Society of Anesthesiologists (ASA) physical status classification I-III;
• Planned for general anesthesia with endotracheal intubation;
• Patient and legal guardians are capable of understanding the study protocol and willing to provide written informed consent.
Locations
Other Locations
China
Peking Union Medical College Hospital
RECRUITING
Beijing
Contact Information
Primary
Quexuan Cui, Dr.
Cuiqx_garfield@126.com
+8613520921711
Time Frame
Start Date: 2026-06-01
Estimated Completion Date: 2027-12-31
Participants
Target number of participants: 500
Treatments
Elderly Surgical Patients
Patients aged over 65 years who are undergoing elective non-cardiac surgery under general anesthesia with endotracheal intubation. All patients will receive continuous non-invasive hemodynamic monitoring prior to anesthesia induction to calculate baseline BRS parameters.
Related Therapeutic Areas
Sponsors
Leads: Peking Union Medical College Hospital