Machine Learning Model Based on Baroreflex Sensitivity for Predicting Post-Induction Hypotension in Elderly Patients
Recruiting now
Conditions studied: Post Induction Hypotension
In brief
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.
Key facts
- Study ID
- NCT07618416
- Run by
- Peking Union Medical College Hospital
- People needed
- 500
- Starts
- 2026-06-01
- Expected to finish
- 2027-12-31
- Last updated by the study team
- 2026-06-01
Who can join
Age: 65 and older. Sex: any. Healthy volunteers: not accepted.
You may qualify if…
- 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.
You may not qualify if…
- 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.
Where it is running
- Peking Union Medical College Hospital — Beijing, China, China (enrolling)
Full record on ClinicalTrials.gov
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