Comparative Assessment of In Vivo and In Silico Evaluation of Automated Fluid Resuscitation Controllers

Physiologic closed-loop controlled (PCLC) medical devices have the potential to enhance therapeutic precision and effectiveness. However, their complexity introduces potential failure modes that may pose risks to patients if not properly evaluated. This study assesses the effectiveness of a mathematical model of the cardiovascular system in predicting PCLC performance metrics through in vivo and in silico comparisons of blood pressure responses to automated fluid infusion.

A closed-loop control system for regulating mean arterial pressure (MAP) was implemented using a custom Java-based software application on a tablet. The system operated in two control speed modes, 'slow' and 'fast.' The study was conducted in an animal laboratory using thirteen swine subjects (two were excluded due to hardware-related issues). Following intubation, anesthesia, and splenectomy, hemorrhage was induced until MAP reached 45 mmHg, followed by fluid resuscitation using the closed-loop controller targeting 70 mmHg. Arterial blood pressure waveforms were continuously recorded, and cardiac output and hematocrit measurements were taken every 15 min. The same control algorithm and speed modes were applied to in silico subjects generated with a mathematical model simulating cardiovascular responses to fluid perturbation while the same protocol was simulated.

The mathematical model effectively predicted key PCLC performance metrics, including rise time, %overshoot, settling time, and divergence. The simulated results closely matched experimental data and captured differences between slow and fast control speeds.

The ability of the mathematical model to predict PCLC performance metrics demonstrates its value in supporting the development and evaluation of automated fluid resuscitation controllers.