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Decoding a Hidden Energy State Based on Marked Point Process Cortisol Secretory Events During Cardiac Surgery.

Saman Khazaei1, Rose T Faghih1

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This study decodes a hidden cortisol-related energy state during coronary artery bypass grafting surgery. Results show a higher energy state post-surgery, offering insights for patient care and treatment planning.

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Biomedical signal processingestimationstate-space methods

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Area of Science:

  • Endocrinology
  • Computational Biology
  • Surgical Physiology

Background:

  • Cortisol plays a key role in regulating energy metabolism during physiological stress, such as surgery.
  • Understanding cortisol dynamics is crucial for managing patient health during inflammatory conditions like post-surgical recovery.

Purpose of the Study:

  • To decode a hidden cortisol-related energy state from patient cortisol profiles during coronary artery bypass grafting (CABG) surgery.
  • To assess the clinical relevance and reliability of a computational approach for tracking individual energy states.

Main Methods:

  • Utilized a Bayesian state estimation approach within an expectation-maximization framework.
  • Estimated energy state using inferred cortisol secretory events and a high-frequency (1-minute resolution) reconstructed cortisol profile.
  • Reconstructed cortisol profile derived from deconvolution of 10-minute sampled cortisol data.

Main Results:

  • Identified a significantly higher energy state in the post-surgery phase compared to the surgery phase across all 10 patients.
  • The developed decoder demonstrated reliability in revealing clinically relevant information regarding energy state fluctuations.

Conclusions:

  • Tracking patient-specific, cortisol-related energy states during surgery is feasible and provides valuable insights.
  • This approach can inform the design of surgical interventions and personalized treatment plans for improved patient outcomes.