| Autor: |
Chalumuri YR; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA., Arabidarrehdor G; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA., Tivay A; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA., Sampson CM; Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA., Khan M; Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA., Kinsky M; Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA., Kramer GC; Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA., Hahn JO; Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA., Scully CG; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA., Bighamian R; Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA. |
| Abstrakt: |
Physiological closed-loop controlled (PCLC) medical devices, such as those designed for blood pressure regulation, can be tested for safety and efficacy in real-world clinical settings. However, relying solely on limited animal and clinical studies may not capture the diverse range of physiological conditions. Credible mathematical models can complement these studies by allowing the testing of the device against simulated patient scenarios. This research involves the development and validation of a low-order lumped-parameter mathematical model of the cardiovascular system's response to fluid perturbation. The model takes rates of hemorrhage and fluid infusion as inputs and provides hematocrit and blood volume, heart rate, stroke volume, cardiac output and mean arterial blood pressure as outputs. The model was calibrated using data from 27 sheep subjects, and its predictive capability was evaluated through a leave-one-out cross-validation procedure, followed by independent validation using 12 swine subjects. Our findings showed small model calibration error against the training dataset, with the normalized root-mean-square error (NRMSE) less than 10% across all variables. The mathematical model and virtual patient cohort generation tool demonstrated a high level of predictive capability and successfully generated a sufficient number of subjects that closely resembled the test dataset. The average NRMSE for the best virtual subject, across two distinct samples of virtual subjects, was below 12.7% and 11.9% for the leave-one-out cross-validation and independent validation dataset. These findings suggest that the model and virtual cohort generator are suitable for simulating patient populations under fluid perturbation, indicating their potential value in PCLC medical device evaluation. |
