Y. El Dsouki and I. Condello: J Extra Corpor Technol 2025, 57, 137--146 139
Figure 2. Principal flowchart.
Maintain safe temperature gradients to prevent endothelial damage and monitor for potential hyperthermic conditions.
System alerts to notify staff if rewarming rates exceed safe limits, ensuring immediate corrective action.
5. Final temperature adjustment and weaning
Stabilize core temperature within the target range prior to concluding CPB.
Adjust gas flow and ventilation to transition smoothly to normothermic conditions.
Conduct final checks of all metabolic markers to confirm patient stability before disengagement from the system.
6. Simulation protocol( preliminary validation)
A simulated environment was created using MATLAB to replicate dynamic patient responses during CPB. The algorithm was tested for accuracy, responsiveness, and redundancy during sensor dropout. Initial results showed the system maintained safe temperature gradients 97 % of the time.
Algorithm integration and architecture
The system interfaces directly with standard HCUs using analog / digital communication protocols( e. g., RS232). Sensor data is continuously relayed to a central control unit. Fail-safe protocols include sensor redundancy, alarms for DT breaches, manual override, and logging of alerts for auditing.
Safe temperature gradient protocols
A set of predefined safe ranges for cooling and rewarming gradients is strictly maintained to minimize thermal stress and its associated risks:
Cooling Gradient: Maintains a maximum DT of 10 ° C between the blood and the HCU.
Rewarming Gradient: Limits the DT to 4 ° C between the core and the blood.
Deep hypothermia management
Deep hypothermia protocols are carefully managed to prevent complications associated with extreme temperature changes:
Gradual cooling and rewarming, closely monitoring NIRS( Near-Infrared Spectroscopy), and cerebral saturation to ensure adequate brain protection.
Oxygen and flow adjustments
DO 2 levels and perfusion flows are adjusted based on temperature-dependent metabolic rates, ensuring adequate tissue perfusion across varying temperatures.
Challenges and innovations
The system incorporates advanced predictive modeling to dynamically adjust to patient-specific conditions and realtime changes. It addresses potential risks of over-reliance