Machine Learning Model Based on Baroreflex Sensitivity for Predicting Post-Induction Hypotension in Elderly Patients
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Post Induction Hypotension. Basic parameters: from 65 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development of a Baroreflex Sensitivity-Based Multifactorial Machine Learning Model for Predicting Post-Induction Hypotension in Elderly Patients
Overview
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.
Detailed description
Aging significantly alters cardiovascular autonomic function, characterized by elevated sympathetic and decreased parasympathetic tone, rendering elderly patients highly vulnerable to post-induction hypotension (PIH). While existing machine learning models heavily rely on static data (e.g., baseline blood pressure, demographics, medication history), they lack real-time dynamic regulatory inputs, limiting their predictive performance in individualized care.
This single-center, prospective cohort study aims to bridge this gap by introducing preoperative BRS parameters-calculated via the continuous non-invasive arterial pressure (CNAP) method-into machine learning frameworks. A total of 500 patients aged over 65 years scheduled for elective non-cardiac surgery will be enrolled. Preoperative data, including autonomic indices, frailty assessments, and static clinical factors, will be mapped alongside intraoperative events and 30-day postoperative complications. Multiple machine learning algorithms (Logistic Regression, Random Forest, GBDT, XGBoost, LightGBM, and LSTM) will be leveraged and optimized using cross-validation to construct a robust clinical decision-support pipeline.
Primary outcome measures
- Incidence of Post-Induction Hypotension (PIH) [Time frame: From immediately after anesthesia induction up to 20 minutes post-induction or before surgical incision.]
Secondary outcome measures (2)
- 1. Early Intraoperative Hypotension Rate [Time frame: From surgical incision to the end of the operation.]
- Postoperative Complication [Time frame: Up to 30 days post-surgery]
Eligibility 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Peking Union Medical College Hospital — Beijing
Identifiers
NCT: NCT07618416 · 2025-PUMCH-A-119 · 2025-PUMCH-A-119