RecruitingNCT07618416
Machine Learning Model Based on Baroreflex Sensitivity for Predicting Post-Induction Hypotension in Elderly Patients
Development of a Baroreflex Sensitivity-Based Multifactorial Machine Learning Model for Predicting Post-Induction Hypotension in Elderly Patients
Sponsor
Peking Union Medical College Hospital
Enrollment
500 participants
Start Date
Jun 1, 2026
Study Type
OBSERVATIONAL
Conditions
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
Min Age: 65 Years
Inclusion Criteria5
- 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.
Exclusion Criteria6
- Severe peripheral vascular diseases;
- Secondary hypertension;
- Presence of physical tremors (e.g., Parkinson's disease) preventing stable recording;
- Inability to accurately measure upper limb blood pressure;
- Pre-existing cardiac arrhythmias (e.g., atrial fibrillation) that render BRS;
- Psychiatric disorders or cognitive impairments hindering basic cooperation.
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Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07618416