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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Related Experiment Videos

Multimodal Machine Learning Model Predicting Postoperative Delirium Based on Heart Rate Variability: A Prospective

Yuling Tang1, Yuanhui Liu1, Jiayi Tang1

  • 1From the Department of Anesthesiology, The Affiliated Hospital, Southwest Medical University, Luzhou, China.

Anesthesia and Analgesia
|July 13, 2026
PubMed
Summary

Integrating heart rate variability with clinical and electrocardiogram data significantly improves prediction of postoperative delirium. This multimodal approach offers enhanced personalized risk assessment for patients undergoing general anesthesia.

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Area of Science:

  • Anesthesiology and Perioperative Medicine
  • Cardiology and Electrocardiography
  • Artificial Intelligence in Healthcare

Background:

  • Postoperative delirium (POD) is a frequent and severe complication following general anesthesia, posing a significant challenge in perioperative care.
  • Current predictive models for POD often rely solely on clinical variables, exhibiting limited accuracy.
  • There is a need for improved methods to accurately predict POD risk, especially incorporating physiological data.

Purpose of the Study:

  • To assess the added predictive value of heart rate variability (HRV) parameters for postoperative delirium.
  • To develop and validate an interpretable multimodal predictive model for POD.
  • To enhance personalized risk assessment for patients undergoing general anesthesia.

Main Methods:

  • A prospective observational study included 1418 patients undergoing general anesthesia.
  • Seventy-three features, including ECG abnormalities and HRV indicators (time, frequency, nonlinear domains), were extracted.
  • Machine learning models were developed using feature selection techniques (LASSO, Boruta, random forests) and validated externally.

Main Results:

  • Postoperative delirium occurred in 18% of patients.
  • The combined clinical-electrocardiogram-HRV model achieved the highest predictive performance (AUC = 0.728), surpassing clinical-only (AUC = 0.673) and ECG-only (AUC = 0.679) models.
  • Key predictors included arrhythmias, operative time, ECG abnormalities, age, ASA classification, and HRV entropy; external validation confirmed robust performance (AUC = 0.836).

Conclusions:

  • Integrating HRV parameters with clinical and electrocardiogram data significantly improves the prediction of postoperative delirium.
  • The developed multimodal model offers enhanced personalized risk assessment for POD.
  • This approach advances the capability for proactive management of POD in perioperative settings.