Use of the cardiopulmonary coupling index based on refined composite multiscale entropy for prognostication of acute

Zhi-Jie Mao1, Wei-Wei Wen2, Yi-Chen Han1

  • 1The Key Laboratory of Cardiovascular Disease of Wenzhou, Department of Cardiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Insights

Cardiopulmonary coupling (CPC) based on RCMSE effectively predicts complications and death in acute type A aortic dissection (ATAAD) patients. This novel approach aids in risk stratification for better patient outcomes.

Area of Science:

  • Cardiology
  • Thoracic Surgery
  • Biomedical Engineering

Background:

  • The cardiopulmonary system exhibits nonlinear regulation.
  • The relationship between cardiopulmonary coupling (CPC) and postoperative risk in acute type A aortic dissection (ATAAD) remains unstudied.
  • Accurate risk stratification is crucial for managing ATAAD patients.

Purpose of the Study:

  • To assess the predictive value of cardiopulmonary coupling (CPC) using RCMSE for complications and mortality in ATAAD patients.
  • To investigate CPC-RCMSE as a novel biomarker for risk stratification in ATAAD.

Main Methods:

  • A prospective, single-center cohort study enrolled 39 ATAAD patients.
  • Cardiopulmonary coupling (CPC) based on RCMSE was analyzed.
  • Outcomes included in-hospital complications and 2-year all-cause readmission or death.

Main Results:

  • CPC-RCMSE demonstrated strong predictive power for in-hospital complications (AUC=0.853) and 2-year mortality/readmission (AUC=0.731).
  • CPC-RCMSE was identified as an independent predictor of in-hospital complications after adjusting for clinical factors.
  • 41.0% of patients experienced in-hospital complications, and 38.5% faced adverse outcomes within 2 years.

Conclusions:

  • Cardiopulmonary coupling (CPC) based on RCMSE is a valuable independent predictor for in-hospital complications in ATAAD.
  • CPC-RCMSE also predicts 2-year all-cause readmission or death in ATAAD patients.
  • This metric offers a promising tool for enhanced risk stratification in ATAAD management.
Abstract

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