Predicting Post-Induction Hypotension in Elderly Patients Using Baroreflex Sensitivity and Machine Learning
Official title: Development of a Baroreflex Sensitivity-Based Multifactorial Machine Learning Model for Predicting Post-Induction Hypotension in Elderly Patients
Run by Peking Union Medical College Hospital · NCT07618416 · Data updated 2026-11-12
Researchers are testing a machine learning approach that uses baroreflex sensitivity and other preoperative data to predict the risk of post-induction hypotension in people over 65 undergoing elective non-cardiac surgery.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 65 Years and up |
| Sex | All |
| Enrollment | 500 (estimated) |
| Sponsor | Peking Union Medical College Hospital Academic / other |
| Locations | 1 site (Beijing, China) |
| Status | Recruiting |
| Started | June 1, 2026 |
| Main results expected | September 30, 2027 |
| Study ends | December 31, 2027 |
| Registry record updated | June 1, 2026 |
| Trial ID | NCT07618416 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults over 65 scheduled for elective non-cardiac surgery under general anesthesia with ASA I–III.
✕Probably not a fit
Patients with severe peripheral vascular disease, secondary hypertension, tremor-related recording issues, inability to measure upper limb blood pressure, pre-existing cardiac arrhythmias affecting BRS, or significant cognitive/psychiatric impairment may derive less or no benefit.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This prospective, single-center cohort study will combine preoperative dynamic baroreflex sensitivity metrics—measured with continuous non-invasive arterial pressure—and static clinical features to build a predictive model for post-induction hypotension in elderly patients. Multiple algorithms, including Logistic Regression, Random Forest, gradient boosting methods, XGBoost, LightGBM, and LSTM, will be explored and validated with cross-validation. The study aims to improve individualized risk stratification to support intraoperative management under general anesthesia. Enrollment targets 500 participants aged over 65 with ASA I–III undergoing elective non-cardiac surgery. Data will integrate preoperative autonomic indices, frailty assessments, intraoperative events, and 30-day postoperative outcomes.
Why it matters
Potential benefit: If successful, the model could help clinicians anticipate PIH risk and tailor anesthesia management to reduce hypotension-related complications.
How similar studies have gone: Similar approaches using dynamic autonomic measures have shown promise, but applying preoperative baroreflex sensitivity within ML pipelines for PIH prediction is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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 Criteria: * 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's running
-
Peking Union Medical College Hospital
Beijing, China, China Recruiting
Study contacts
- Study coordinator: Quexuan Cui, Dr.
- Email: Cuiqx_garfield@126.com
- Phone: +8613520921711
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.