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

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.