1
|
Ueta H, Kodera S, Sugimoto S, Hirata A. Projection of future heat-related morbidity in three metropolitan prefectures of Japan based on large ensemble simulations of climate change under 2 °C global warming scenarios. Environ Res 2024; 247:118202. [PMID: 38224937 DOI: 10.1016/j.envres.2024.118202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 01/17/2024]
Abstract
Recently, global warming has become a prominent topic, including its impacts on human health. The number of heat illness cases requiring ambulance transport has been strongly linked to increasing temperature and the frequency of heat waves. Thus, a potential increase in the number of cases in the future is a concern for medical resource management. In this study, we estimated the number of heat illness cases in three prefectures of Japan under 2 °C global warming scenarios, approximately corresponding to the 2040s. Based on the population composition, a regression model was used to estimate the number of heat illness cases with an input parameter of time-dependent meteorological ambient temperature or computed thermophysiological response of test subjects in large-scale computation. We generated 504 weather patterns using 2 °C global warming scenarios. The large-scale computational results show that daily amount of sweating increased twice and the core temperature increased by maximum 0.168 °C, suggesting significant heat strain. According to the regression model, the estimated number of heat illness cases in the 2040s of the three prefectures was 1.90 (95%CI: 1.35-2.38) times higher than that in the 2010s. These computational results suggest the need to manage ambulance services and medical resource allocation, including intervention for public awareness of heat illnesses. This issue will be important in other aging societies in near future.
Collapse
Affiliation(s)
- Haruto Ueta
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, 466-8555, Japan
| | - Shiori Sugimoto
- Japan Agency for Marine-Earth Science and Technology, Yokohama, 236-0001, Japan
| | - Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan; Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya, 466-8555, Japan.
| |
Collapse
|
2
|
Wang Y, Stroh JN, Hripcsak G, Low Wang CC, Bennett TD, Wrobel J, Der Nigoghossian C, Mueller SW, Claassen J, Albers DJ. A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation. J Biomed Inform 2023; 148:104547. [PMID: 37984547 PMCID: PMC10802138 DOI: 10.1016/j.jbi.2023.104547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023]
Abstract
OBJECTIVE Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR). METHODS A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation. RESULTS The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%. CONCLUSION The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.
Collapse
Affiliation(s)
- Yanran Wang
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, 3rd Floor, Mail Stop B119, Aurora, CO 80045, United States of America; Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America.
| | - J N Stroh
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America; Department of Biomedical Engineering, University of Colorado, 12705 East Montview Boulevard, Suite 100, Aurora, CO 80045, United States of America
| | - George Hripcsak
- Biomedical Informatics, Columbia University, 622 W. 168th Street, PH20, New York, NY 10032, United States of America
| | - Cecilia C Low Wang
- Division of Endocrinology, Metabolism and Diabetes, Department of Medicine, University of Colorado School of Medicine, 12801 East 17th Avenue, 7103, Aurora, CO 80045, United States of America
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America
| | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd, NE Atlanta, GA 30322, United States of America
| | - Caroline Der Nigoghossian
- Columbia University School of Nursing, 560 West 168th Street, New York, NY 10032, United States of America
| | - Scott W Mueller
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 East Montview Boulevard, Aurora, CO 80045, United States of America
| | - Jan Claassen
- The Neurological Institute of New York, Columbia University Irving Medical Center, 710 West 168th Street, New York NY 10032, United States of America
| | - D J Albers
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, 3rd Floor, Mail Stop B119, Aurora, CO 80045, United States of America; Department of Biomedical Informatics, University of Colorado School of Medicine, Anschutz Health Sciences Building, 1890 N. Revere Court, Mailstop F600, Aurora, CO 80045, United States of America; Department of Biomedical Engineering, University of Colorado, 12705 East Montview Boulevard, Suite 100, Aurora, CO 80045, United States of America; Biomedical Informatics, Columbia University, 622 W. 168th Street, PH20, New York, NY 10032, United States of America
| |
Collapse
|
3
|
Hobbs N, Samadi S, Rashid M, Shahidehpour A, Askari MR, Park M, Quinn L, Cinar A. A physical activity-intensity driven glycemic model for type 1 diabetes. Comput Methods Programs Biomed 2022; 226:107153. [PMID: 36183639 DOI: 10.1016/j.cmpb.2022.107153] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 06/21/2022] [Accepted: 09/21/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVE The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered. METHODS Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed. RESULTS The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002). CONCLUSIONS The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.
Collapse
Affiliation(s)
- Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Andrew Shahidehpour
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA
| | - Minsun Park
- College of Nursing, University of Illinois at Chicago. Chicago, IL, USA
| | - Laurie Quinn
- College of Nursing, University of Illinois at Chicago. Chicago, IL, USA
| | - Ali Cinar
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA; Department of Chemical and Biological Engineering, Illinois Institute of Technology, Perlstein Hall, Suite 127, 10 W. 33rd St., Chicago, IL 60616, USA.
| |
Collapse
|
4
|
Candia-Rivera D. Brain-heart interactions in the neurobiology of consciousness. Curr Res Neurobiol 2022; 3:100050. [PMID: 36685762 PMCID: PMC9846460 DOI: 10.1016/j.crneur.2022.100050] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 07/23/2022] [Accepted: 07/27/2022] [Indexed: 01/25/2023] Open
Abstract
Recent experimental evidence on patients with disorders of consciousness revealed that observing brain-heart interactions helps to detect residual consciousness, even in patients with absence of behavioral signs of consciousness. Those findings support hypotheses suggesting that visceral activity is involved in the neurobiology of consciousness, and sum to the existing evidence in healthy participants in which the neural responses to heartbeats reveal perceptual and self-consciousness. More evidence obtained through mathematical modeling of physiological dynamics revealed that emotion processing is prompted by an initial modulation from ascending vagal inputs to the brain, followed by sustained bidirectional brain-heart interactions. Those findings support long-lasting hypotheses on the causal role of bodily activity in emotions, feelings, and potentially consciousness. In this paper, the theoretical landscape on the potential role of heartbeats in cognition and consciousness is reviewed, as well as the experimental evidence supporting these hypotheses. I advocate for methodological developments on the estimation of brain-heart interactions to uncover the role of cardiac inputs in the origin, levels, and contents of consciousness. The ongoing evidence depicts interactions further than the cortical responses evoked by each heartbeat, suggesting the potential presence of non-linear, complex, and bidirectional communication between brain and heartbeat dynamics. Further developments on methodologies to analyze brain-heart interactions may contribute to a better understanding of the physiological dynamics involved in homeostatic-allostatic control, cognitive functions, and consciousness.
Collapse
|
5
|
Cheng L, Qiu Y, Schmidt BJ, Wei GW. Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure. J Pharmacokinet Pharmacodyn 2021; 49:39-50. [PMID: 34637069 PMCID: PMC8837528 DOI: 10.1007/s10928-021-09785-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.
Collapse
Affiliation(s)
- Limei Cheng
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA.
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Brian J Schmidt
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.,Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
| |
Collapse
|
6
|
Uludag K, Havlicek M. Determining laminar neuronal activity from BOLD fMRI using a generative model. Prog Neurobiol 2021; 207:102055. [PMID: 33930519 DOI: 10.1016/j.pneurobio.2021.102055] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 03/12/2021] [Accepted: 04/20/2021] [Indexed: 11/17/2022]
Abstract
Laminar fMRI using the BOLD contrast enables the non-invasive investigation of mesoscopic functional circuits in the human brain. However, the laminar neuronal activity is spatiotemporally biased in the observed cortical depth profiles of the BOLD signal. In this study, we propose a generative fMRI signal model, comprehensively covering the relationship between cortical depth-dependent changes in excitatory and inhibitory neuronal activity with the sampling of the BOLD signal with finite voxels. The generative model allowed us to investigate pertinent questions regarding the accuracy of the laminar BOLD signal relative to the neuronal activity, and we found that: a) condition differences in laminar BOLD signals may be more reflective of neuronal activity than single condition BOLD signal depth profiles; b) angular dependence of the BOLD signal induces significant signal variability, which can mask underlying activity profiles; c) even if only three neuronal depths are of interest, more BOLD signal depths should be considered in the analysis. In addition, we recommend that the laminar BOLD data should be displayed using the centroid method to appreciate its spatial distribution in the original resolution. Finally, we showed that Bayesian model inversion of the generative model can improve sensitivity and specificity of assessing depth-dependent neuronal changes both for steady-state and dynamically.
Collapse
Affiliation(s)
- Kamil Uludag
- Techna Institute & Koerner Scientist in MR Imaging, University Health Network, Toronto, Canada; Center for Neuroscience Imaging Research, Institute for Basic Science & Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
| | - Martin Havlicek
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| |
Collapse
|
7
|
Chang YC, Cracchiolo M, Ahmed U, Mughrabi I, Gabalski A, Daytz A, Rieth L, Becker L, Datta-Chaudhuri T, Al-Abed Y, Zanos TP, Zanos S. Quantitative estimation of nerve fiber engagement by vagus nerve stimulation using physiological markers. Brain Stimul 2020; 13:1617-1630. [PMID: 32956868 DOI: 10.1016/j.brs.2020.09.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 07/31/2020] [Accepted: 09/04/2020] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Cervical vagus nerve stimulation (VNS) is an emerging bioelectronic treatment for brain, metabolic, cardiovascular and immune disorders. Its desired and off-target effects are mediated by different nerve fiber populations and knowledge of their engagement could guide calibration and monitoring of VNS therapies. OBJECTIVE Stimulus-evoked compound action potentials (eCAPs) directly provide fiber engagement information but are currently not feasible in humans. A method to estimate fiber engagement through common, noninvasive physiological readouts could be used in place of eCAP measurements. METHODS In anesthetized rats, we recorded eCAPs while registering acute physiological response markers to VNS: cervical electromyography (EMG), changes in heart rate (ΔHR) and breathing interval (ΔBI). Quantitative models were established to capture the relationship between A-, B- and C-fiber type activation and those markers, and to quantitatively estimate fiber activation from physiological markers and stimulation parameters. RESULTS In bivariate analyses, we found that EMG correlates with A-fiber, ΔHR with B-fiber and ΔBI with C-fiber activation, in agreement with known physiological functions of the vagus. We compiled multivariate models for quantitative estimation of fiber engagement from these markers and stimulation parameters. Finally, we compiled frequency gain models that allow estimation of fiber engagement at a wide range of VNS frequencies. Our models, after calibration in humans, could provide noninvasive estimation of fiber engagement in current and future therapeutic applications of VNS.
Collapse
Affiliation(s)
- Yao-Chuan Chang
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Marina Cracchiolo
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA; The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, 56127, Italy
| | - Umair Ahmed
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Ibrahim Mughrabi
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Arielle Gabalski
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Anna Daytz
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Loren Rieth
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Lance Becker
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Timir Datta-Chaudhuri
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Yousef Al-Abed
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Theodoros P Zanos
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA
| | - Stavros Zanos
- Institute of Bioelectronic Medicine, Feinstein Institutes for Medical Research, Manhasset, NY, 11030, USA.
| |
Collapse
|
8
|
Drescher U, Koschate J, Thieschäfer L, Schneider S, Hoffmann U. Temporal dissociation between muscle and pulmonary oxygen uptake kinetics: influences of perfusion dynamics and arteriovenous oxygen concentration differences in muscles and lungs. Eur J Appl Physiol 2018; 118:1845-56. [PMID: 29934765 DOI: 10.1007/s00421-018-3916-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 06/08/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE The aim of the study was to test whether or not the arteriovenous oxygen concentration difference (avDO2) kinetics at the pulmonary (avDO2pulm) and muscle (avDO2musc) levels is significantly different during dynamic exercise. METHODS A re-analysis involving six publications dealing with kinetic analysis was utilized with an overall sample size of 69 participants. All studies comprised an identical pseudorandom binary sequence work rate (WR) protocol-WR changes between 30 and 80 W-to analyze the kinetic responses of pulmonary ([Formula: see text]) and muscle ([Formula: see text]) oxygen uptake kinetics as well as those of avDO2pulm and avDO2musc. RESULTS A significant difference between [Formula: see text] (0.395 ± 0.079) and [Formula: see text] kinetics (0.330 ± 0.078) was observed (p < 0.001), where the variables showed a significant relationship (rSP = 0.744, p < 0.001). There were no significant differences between avDO2musc (0.446 ± 0.077) and avDO2pulm kinetics (0.451 ± 0.075), which are highly correlated (r = 0.929, p < 0.001). CONCLUSION It is suggested that neither avDO2pulm nor avDO2musc kinetic responses seem to be responsible for the differences between estimated [Formula: see text] and measured [Formula: see text] kinetics. Obviously, the conflation of avDO2 and perfusion ([Formula: see text] ) at different points in time and at different physiological levels drive potential differences in [Formula: see text] and [Formula: see text] kinetics. Therefore, [Formula: see text] should, in general, be considered whenever oxygen uptake kinetics are analyzed or discussed.
Collapse
|
9
|
Ngo C, Dahlmanns S, Vollmer T, Misgeld B, Leonhardt S. An object-oriented computational model to study cardiopulmonary hemodynamic interactions in humans. Comput Methods Programs Biomed 2018; 159:167-183. [PMID: 29650311 DOI: 10.1016/j.cmpb.2018.03.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 01/02/2018] [Accepted: 03/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE This work introduces an object-oriented computational model to study cardiopulmonary interactions in humans. METHODS Modeling was performed in object-oriented programing language Matlab Simscape, where model components are connected with each other through physical connections. Constitutive and phenomenological equations of model elements are implemented based on their non-linear pressure-volume or pressure-flow relationship. The model includes more than 30 physiological compartments, which belong either to the cardiovascular or respiratory system. The model considers non-linear behaviors of veins, pulmonary capillaries, collapsible airways, alveoli, and the chest wall. Model parameters were derisved based on literature values. Model validation was performed by comparing simulation results with clinical and animal data reported in literature. RESULTS The model is able to provide quantitative values of alveolar, pleural, interstitial, aortic and ventricular pressures, as well as heart and lung volumes during spontaneous breathing and mechanical ventilation. Results of baseline simulation demonstrate the consistency of the assigned parameters. Simulation results during mechanical ventilation with PEEP trials can be directly compared with animal and clinical data given in literature. CONCLUSIONS Object-oriented programming languages can be used to model interconnected systems including model non-linearities. The model provides a useful tool to investigate cardiopulmonary activity during spontaneous breathing and mechanical ventilation.
Collapse
Affiliation(s)
- Chuong Ngo
- Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany.
| | - Stephan Dahlmanns
- Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany
| | - Thomas Vollmer
- Philips Technologie GmbH Innovative Technologies, Pauwelsstr. 17, 52074 Aachen, Germany
| | - Berno Misgeld
- Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany
| | - Steffen Leonhardt
- Chair of Medical Information Technology, Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany
| |
Collapse
|
10
|
Jalalahmadi G, Helguera M, Mix DS, Linte CA. Toward modeling the effects of regional material properties on the wall stress distribution of abdominal aortic aneurysms. Proc SPIE Int Soc Opt Eng 2018; 10578. [PMID: 31213733 DOI: 10.1117/12.2294558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
The overall geometry and different biomechanical parameters of an abdominal aortic aneurysm (AAA), contribute to its severity and risk of rupture, therefore they could be used to track its progression. Previous and ongoing research efforts have resorted to using uniform material properties to model the behavior of AAA. However, it has been recently illustrated that different regions of the AAA wall exhibit different behavior due to the effect of the biological activities in the metalloproteinase matrix that makes up the wall at the aneurysm site. In this work, we introduce a non-invasive patient-specific regional material property model to help us better understand and investigate the AAA wall stress distribution, peak wall stress (PWS) severity, and potential rupture risk. Our results indicate that the PWS and the overall wall stress distribution predicted using the proposed regional material property model, are higher than those predicted using the traditional homogeneous, hyper-elastic model (p <1.43E-07). Our results also show that to investigate AAA, the overall geometry, presence of intra-luminal thrombus (ILT), and loading condition in a patient specific manner may be critical for capturing the biomechanical complexity of AAAs.
Collapse
Affiliation(s)
- Golnaz Jalalahmadi
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA
| | - María Helguera
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA.,Instituto Tecnológico José Mario Molina Pasquel y Henríquez - Unidad Lagos de Moreno, Jalisco, México
| | - Doran S Mix
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA.,Department of Surgery, Division of Vascular Surgery, University of Rochester Medical Center, Rochester, USA
| | - Cristian A Linte
- Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, USA.,Biomedical Engineering Department, Rochester Institute of Technology, Rochester, USA
| |
Collapse
|
11
|
Le Rolle V, Beuchee A, Praud JP, Samson N, Pladys P, Hernández AI. Recursive identification of an arterial baroreflex model for the evaluation of cardiovascular autonomic modulation. Comput Biol Med 2015; 66:287-94. [PMID: 26453759 DOI: 10.1016/j.compbiomed.2015.09.013] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Revised: 09/07/2015] [Accepted: 09/16/2015] [Indexed: 11/28/2022]
Abstract
The evaluation of the time-varying vagal and sympathetic contributions to heart rate remains a challenging task because the observability of the baroreflex is generally limited and the time-varying properties are difficult to take into account, especially in non-stationnary conditions. The objective is to propose a model-based approach to estimate the autonomic modulation during a pharmacological challenge. A recursive parameter identification method is proposed and applied to a mathematical model of the baroreflex, in order to estimate the time-varying vagal and sympathetic contributions to heart rate modulation during autonomic maneuvers. The model-based method was evaluated with data from five newborn lambs, which were acquired during injection of vasodilator and vasoconstrictor drugs, on normal conditions and under beta-blockers, so as to quantify the effect of the pharmacological sympathetic blockade on the estimated parameters. After parameter identification, results show a close match between experimental and simulated signals for the five lambs, as the mean relative root mean squared error is equal to 0.0026 (± 0.003). The error, between simulated and experimental signals, is significantly reduced compared to a batch identification of parameters. The model-based estimation of vagal and sympathetic contributions were consistent with physiological knowledge and, as expected, it was possible to observe an alteration of the sympathetic response under beta-blockers. The simulated vagal modulation illustrates a response similar to traditional heart rate variability markers during the pharmacological maneuver. The model-based method, proposed in the paper, highlights the advantages of using a recursive identification method for the estimation of vagal and sympathetic modulation.
Collapse
Affiliation(s)
- Virginie Le Rolle
- INSERM, U1099, Rennes F-35000, France; Campus de Beaulieu, Université de Rennes 1, LTSI, 263 Avenue du General Leclerc, CS 74205, 35042 Rennes Cedex, Rennes F-35000, France.
| | - Alain Beuchee
- INSERM, U1099, Rennes F-35000, France; Campus de Beaulieu, Université de Rennes 1, LTSI, 263 Avenue du General Leclerc, CS 74205, 35042 Rennes Cedex, Rennes F-35000, France; CHU Rennes, Pole de pdiatrie mdico-chirurgicale et gntique clinique - Service de pdiatrie, Rennes F-35000, France
| | - Jean-Paul Praud
- Department of Pediatrics, University of Sherbrooke, QC, Canada J1H5N4
| | - Nathalie Samson
- Department of Pediatrics, University of Sherbrooke, QC, Canada J1H5N4
| | - Patrick Pladys
- INSERM, U1099, Rennes F-35000, France; Campus de Beaulieu, Université de Rennes 1, LTSI, 263 Avenue du General Leclerc, CS 74205, 35042 Rennes Cedex, Rennes F-35000, France; CHU Rennes, Pole de pdiatrie mdico-chirurgicale et gntique clinique - Service de pdiatrie, Rennes F-35000, France
| | - Alfredo I Hernández
- INSERM, U1099, Rennes F-35000, France; Campus de Beaulieu, Université de Rennes 1, LTSI, 263 Avenue du General Leclerc, CS 74205, 35042 Rennes Cedex, Rennes F-35000, France
| |
Collapse
|