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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
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Enhancing post-induction hypotension prediction based on exemplar learning with crossover restart strategy driven

Liufang Sheng1, Shenghui Yu1, Ke Ding2

  • 1The Affiliated People's Hospital, Ningbo University, Ningbo, Zhejiang, 315040, China.

Computer Methods and Programs in Biomedicine
|October 17, 2025
PubMed
Summary

A new machine learning model, bECRIME-SVM, accurately predicts post-induction hypotension (PIH) in elderly patients undergoing surgery. Early PIH prediction using this model can improve patient outcomes and postoperative recovery.

Keywords:
Anesthetic agentsClinical predictorsMachine learningPost-induction hypotensionRime optimization algorithmSupport vector machine

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Area of Science:

  • Anesthesiology and Critical Care Medicine
  • Artificial Intelligence in Healthcare
  • Cardiovascular Medicine

Background:

  • Post-induction hypotension (PIH) is a common complication in patients undergoing general anesthesia, particularly those with cardiovascular conditions or fluid management issues.
  • PIH can lead to critical organ hypoperfusion, increasing risks of prolonged recovery, complications, and mortality.
  • Early prediction of PIH is essential for optimizing patient management and improving surgical outcomes.

Purpose of the Study:

  • To develop and validate a machine learning model for the accurate prediction of post-induction hypotension (PIH) in elderly patients undergoing elective surgery.
  • To identify key clinical features that predict the occurrence of PIH.

Main Methods:

  • A machine learning model, bECRIME-SVM, was developed using data from 440 elderly patients undergoing elective surgery.
  • The model utilized an exemplar learning strategy with a crossover restart strategy within the rime optimization algorithm (ECRIME) for feature selection, followed by support vector machine (SVM) evaluation.
  • Patients were classified into PIH and non-PIH groups based on mean arterial pressure post-induction.

Main Results:

  • The bECRIME-SVM model achieved a prediction accuracy of 84.1% and a specificity of 85.3% for PIH.
  • The ECRIME algorithm demonstrated superior optimization and convergence accuracy compared to other benchmark models.
  • Key predictive features identified include diabetes, alcohol consumption, atropine use, beta-blocker use, total cholesterol, and pre-induction systolic blood pressure.

Conclusions:

  • The bECRIME-SVM model is a valuable tool for accurate clinical prediction of PIH.
  • Identifying significant predictive factors provides crucial insights for early detection and management of PIH.
  • This predictive capability can lead to improved postoperative outcomes for patients receiving general anesthesia.