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Chalumuri YR, Arabidarrehdor G, Tivay A, Sampson CM, Khan M, Kinsky M, Kramer GC, Hahn JO, Scully CG, Bighamian R. A Lumped-Parameter Model of the Cardiovascular System Response for Evaluating Automated Fluid Resuscitation Systems. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2024; 12:62511-62525. [PMID: 38872754 PMCID: PMC11170980 DOI: 10.1109/access.2024.3395008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
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.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ghazal Arabidarrehdor
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ali Tivay
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Catherine M Sampson
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Muzna Khan
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Michael Kinsky
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - George C Kramer
- Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555, USA
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA
| | - Christopher G Scully
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, Silver Spring, MD 20993, USA
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Conflicting interactions in multiple closed-loop controlled critical care Treatments: A hemorrhage resuscitation-intravenous propofol sedation case study. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103268] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Parvinian B, Bighamian R, Scully CG, Hahn JO, Pathmanathan P. Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation. Front Physiol 2021; 12:705222. [PMID: 34603074 PMCID: PMC8481867 DOI: 10.3389/fphys.2021.705222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/23/2021] [Indexed: 11/30/2022] Open
Abstract
Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. In silico studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical in silico testing. In this work, the credibility of a subject-specific mathematical model of blood volume kinetics intended to predict blood volume response to hemorrhage and fluid resuscitation during fluid therapy was evaluated. A workflow was used in which: (i) the foundational properties of the mathematical model such as structural identifiability were evaluated; (ii) practical identifiability was evaluated both pre- and post-calibration, with the pre-calibration results used to determine an optimal splitting of experimental data into calibration and validation datasets; (iii) uncertainty in model parameters and the experimental uncertainty were quantified for each subject; and (iv) the uncertainty was propagated through the blood volume kinetics model and its predictive capability was evaluated via validation tests. The mathematical model was found to be structurally identifiable. Pre-calibration identifiability analysis led to splitting the 180 min of time series data per subject into 50 and 130 min calibration and validation windows, respectively. The average root mean squared error of the mathematical model was 12.6% using the calibration window of (0 min, 50 min). Practical identifiability was established post-calibration after fixing one of the parameters to a nominal value. In the validation tests, 82 and 75% of the subject-specific mathematical models were able to correctly predict blood volume response when predictive capability was evaluated at 180 min and at the time when amount of infused fluid equals fluid loss.
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Affiliation(s)
- Bahram Parvinian
- Department of Mechanical Engineering, University of Maryland College Park, College Park, MD, United States
| | - Ramin Bighamian
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
| | - Christopher George Scully
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland College Park, College Park, MD, United States
| | - Pras Pathmanathan
- Office of Science and Engineering Laboratories, Food and Drug Administration, Silver Spring, MD, United States
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Jin X, Bighamian R, Hahn JO. Development and In Silico Evaluation of a Model-Based Closed-Loop Fluid Resuscitation Control Algorithm. IEEE Trans Biomed Eng 2018; 66:1905-1914. [PMID: 30452347 DOI: 10.1109/tbme.2018.2880927] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
OBJECTIVE To develop and evaluate in silico a model-based closed-loop fluid resuscitation control algorithm via blood volume feedback. METHODS Model-based adaptive control algorithm for fluid resuscitation was developed by leveraging a low-order lumped-parameter blood volume dynamics model, and then in silico evaluated based on a detailed mechanistic model of circulatory physiology. The algorithm operates in two steps: (1) the blood volume dynamics model is individualized based on the patient's fractional blood volume response to an initial fluid bolus via system identification; and (2) an adaptive control law built on the individualized blood volume dynamics model regulates the blood volume of the patient. RESULTS The algorithm was able to track the blood volume set point as well as accurately estimate and monitor the patient's absolute blood volume level. The algorithm significantly outperformed a population-based proportional-integral-derivative control. CONCLUSION Model-based development of closed-loop fluid resuscitation control algorithm may enable regulation of blood volume and monitoring of absolute blood volume level. SIGNIFICANCE Model-based closed-loop fluid resuscitation algorithm may offer opportunities for standardized and patient-tailored therapy and reduction of clinician workload.
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