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Mulder MP, Broomé M, Donker DW, Westerhof BE. Distinct morphologies of arterial waveforms reveal preload-, contractility-, and afterload-deficient hemodynamic instability: An in silico simulation study. Physiol Rep 2022; 10:e15242. [PMID: 35412023 PMCID: PMC9004248 DOI: 10.14814/phy2.15242] [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: 11/08/2021] [Revised: 02/18/2022] [Accepted: 03/05/2022] [Indexed: 11/24/2022] Open
Abstract
Hemodynamic instability is frequently present in critically ill patients, primarily caused by a decreased preload, contractility, and/or afterload. We hypothesized that peripheral arterial blood pressure waveforms allow to differentiate between these underlying causes. In this in-silico experimental study, a computational cardiovascular model was used to simulate hemodynamic instability by decreasing blood volume, left ventricular contractility or systemic vascular resistance, and additionally adaptive and compensatory mechanisms. From the arterial pressure waveforms, 45 features describing the morphology were discerned and a sensitivity analysis and principal component analysis were performed, to quantitatively investigate their discriminative power. During hemodynamic instability, the arterial waveform morphology changed distinctively, for example, the slope of the systolic upstroke having a sensitivity of 2.02 for reduced preload, 0.80 for reduced contractility, and -0.02 for reduced afterload. It was possible to differentiate between the three underlying causes based on the derived features, as demonstrated by the first two principal components explaining 99% of the variance in waveforms. The features with a high correlation coefficient (>0.25) to these principal components are describing the systolic up- and downstroke, and the anacrotic and dicrotic notches of the waveforms. In this study, characteristic peripheral arterial waveform morphologies were identified that allow differentiation between deficits in preload, contractility, and afterload causing hemodynamic instability. These findings are confined to an in silico simulation and warrant further experimental and clinical research in order to prove clinical usability in daily practice.
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Affiliation(s)
- Marijn P Mulder
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, Enschede, The Netherlands
| | - Michael Broomé
- Anesthesia and Intensive Care, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.,ECMO Department, Karolinska University Hospital, Stockholm, Sweden
| | - Dirk W Donker
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, Enschede, The Netherlands.,Intensive Care Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Berend E Westerhof
- Cardiovascular and Respiratory Physiology, TechMed Centre, University of Twente, Enschede, The Netherlands.,Department of Pulmonary Medicine, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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2
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Chalumuri YR, Kimball JP, Mousavi A, Zia JS, Rolfes C, Parreira JD, Inan OT, Hahn JO. Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals. SENSORS (BASEL, SWITZERLAND) 2022; 22:1336. [PMID: 35214238 PMCID: PMC8963055 DOI: 10.3390/s22041336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 01/14/2022] [Accepted: 01/25/2022] [Indexed: 12/15/2022]
Abstract
This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.
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Affiliation(s)
- Yekanth Ram Chalumuri
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jacob P. Kimball
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Azin Mousavi
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Jonathan S. Zia
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Christopher Rolfes
- Global Center for Medical Innovation, Translational Training and Testing Laboratories, Inc. (T3 Labs), Atlanta, GA 30313, USA;
| | - Jesse D. Parreira
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
| | - Omer T. Inan
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA; (J.P.K.); (J.S.Z.); (O.T.I.)
| | - Jin-Oh Hahn
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA; (A.M.); (J.D.P.)
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van der Ster BJP, Kim YS, Westerhof BE, van Lieshout JJ. Central Hypovolemia Detection During Environmental Stress-A Role for Artificial Intelligence? Front Physiol 2021; 12:784413. [PMID: 34975538 PMCID: PMC8715014 DOI: 10.3389/fphys.2021.784413] [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: 09/27/2021] [Accepted: 11/18/2021] [Indexed: 11/19/2022] Open
Abstract
The first step to exercise is preceded by the required assumption of the upright body position, which itself involves physical activity. The gravitational displacement of blood from the chest to the lower parts of the body elicits a fall in central blood volume (CBV), which corresponds to the fraction of thoracic blood volume directly available to the left ventricle. The reduction in CBV and stroke volume (SV) in response to postural stress, post-exercise, or to blood loss results in reduced left ventricular filling, which may manifest as orthostatic intolerance. When termination of exercise removes the leg muscle pump function, CBV is no longer maintained. The resulting imbalance between a reduced cardiac output (CO) and a still enhanced peripheral vascular conductance may provoke post-exercise hypotension (PEH). Instruments that quantify CBV are not readily available and to express which magnitude of the CBV in a healthy subject should remains difficult. In the physiological laboratory, the CBV can be modified by making use of postural stressors, such as lower body "negative" or sub-atmospheric pressure (LBNP) or passive head-up tilt (HUT), while quantifying relevant biomedical parameters of blood flow and oxygenation. Several approaches, such as wearable sensors and advanced machine-learning techniques, have been followed in an attempt to improve methodologies for better prediction of outcomes and to guide treatment in civil patients and on the battlefield. In the recent decade, efforts have been made to develop algorithms and apply artificial intelligence (AI) in the field of hemodynamic monitoring. Advances in quantifying and monitoring CBV during environmental stress from exercise to hemorrhage and understanding the analogy between postural stress and central hypovolemia during anesthesia offer great relevance for healthy subjects and clinical populations.
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Affiliation(s)
- Björn J. P. van der Ster
- Department of Internal Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Department of Anesthesiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Laboratory for Clinical Cardiovascular Physiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Yu-Sok Kim
- Laboratory for Clinical Cardiovascular Physiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Department of Internal Medicine, Medisch Centrum Leeuwarden, Leeuwarden, Netherlands
| | - Berend E. Westerhof
- Laboratory for Clinical Cardiovascular Physiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Department of Pulmonary Medicine, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, Netherlands
| | - Johannes J. van Lieshout
- Department of Internal Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Laboratory for Clinical Cardiovascular Physiology, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Medical Research Council Versus Arthritis Centre for Musculoskeletal Ageing Research, Division of Physiology, Pharmacology and Neuroscience, School of Life Sciences, The Medical School, University of Nottingham Medical School, Queen's Medical Centre, Nottingham, United Kingdom
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Convertino VA, Koons NJ, Suresh MR. Physiology of Human Hemorrhage and Compensation. Compr Physiol 2021; 11:1531-1574. [PMID: 33577122 DOI: 10.1002/cphy.c200016] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Hemorrhage is a leading cause of death following traumatic injuries in the United States. Much of the previous work in assessing the physiology and pathophysiology underlying blood loss has focused on descriptive measures of hemodynamic responses such as blood pressure, cardiac output, stroke volume, heart rate, and vascular resistance as indicators of changes in organ perfusion. More recent work has shifted the focus toward understanding mechanisms of compensation for reduced systemic delivery and cellular utilization of oxygen as a more comprehensive approach to understanding the complex physiologic changes that occur following and during blood loss. In this article, we begin with applying dimensional analysis for comparison of animal models, and progress to descriptions of various physiological consequences of hemorrhage. We then introduce the complementary side of compensation by detailing the complexity and integration of various compensatory mechanisms that are activated from the initiation of hemorrhage and serve to maintain adequate vital organ perfusion and hemodynamic stability in the scenario of reduced systemic delivery of oxygen until the onset of hemodynamic decompensation. New data are introduced that challenge legacy concepts related to mechanisms that underlie baroreflex functions and provide novel insights into the measurement of the integrated response of compensation to central hypovolemia known as the compensatory reserve. The impact of demographic and environmental factors on tolerance to hemorrhage is also reviewed. Finally, we describe how understanding the physiology of compensation can be translated to applications for early assessment of the clinical status and accurate triage of hypovolemic and hypotensive patients. © 2021 American Physiological Society. Compr Physiol 11:1531-1574, 2021.
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Affiliation(s)
- Victor A Convertino
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
| | - Natalie J Koons
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
| | - Mithun R Suresh
- Battlefield Healthy & Trauma Center for Human Integrative Physiology, United States Army Institute of Surgical Research, JBSA San Antonio, Texas, USA
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Convertino VA, Schauer SG, Weitzel EK, Cardin S, Stackle ME, Talley MJ, Sawka MN, Inan OT. Wearable Sensors Incorporating Compensatory Reserve Measurement for Advancing Physiological Monitoring in Critically Injured Trauma Patients. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6413. [PMID: 33182638 PMCID: PMC7697670 DOI: 10.3390/s20226413] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/02/2020] [Accepted: 11/04/2020] [Indexed: 12/21/2022]
Abstract
Vital signs historically served as the primary method to triage patients and resources for trauma and emergency care, but have failed to provide clinically-meaningful predictive information about patient clinical status. In this review, a framework is presented that focuses on potential wearable sensor technologies that can harness necessary electronic physiological signal integration with a current state-of-the-art predictive machine-learning algorithm that provides early clinical assessment of hypovolemia status to impact patient outcome. The ability to study the physiology of hemorrhage using a human model of progressive central hypovolemia led to the development of a novel machine-learning algorithm known as the compensatory reserve measurement (CRM). Greater sensitivity, specificity, and diagnostic accuracy to detect hemorrhage and onset of decompensated shock has been demonstrated by the CRM when compared to all standard vital signs and hemodynamic variables. The development of CRM revealed that continuous measurements of changes in arterial waveform features represented the most integrated signal of physiological compensation for conditions of reduced systemic oxygen delivery. In this review, detailed analysis of sensor technologies that include photoplethysmography, tonometry, ultrasound-based blood pressure, and cardiogenic vibration are identified as potential candidates for harnessing arterial waveform analog features required for real-time calculation of CRM. The integration of wearable sensors with the CRM algorithm provides a potentially powerful medical monitoring advancement to save civilian and military lives in emergency medical settings.
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Affiliation(s)
- Victor A. Convertino
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
| | - Steven G. Schauer
- Battlefield Health & Trauma Center for Human Integrative Physiology, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
| | - Erik K. Weitzel
- Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA;
- Brooke Army Medical Center, JBSA Fort Sam Houston, San Antonio, TX 78234, USA
- 59th Medical Wing, JBSA Lackland, San Antonio, TX 78236, USA
| | - Sylvain Cardin
- Navy Medical Research Unit, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Mark E. Stackle
- Commander, US Army Institute of Surgical Research, JBSA Fort Sam Houston, San Antonio, TX 78234, USA;
| | - Michael J. Talley
- Commanding General, US Army Medical Research and Development Command, Fort Detrick, Frederick, MD 21702, USA;
| | - Michael N. Sawka
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
| | - Omer T. Inan
- Georgia Institute of Technology, Atlanta, GA 30332, USA; (M.N.S.); (O.T.I.)
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van der Ster BJP, Westerhof BE, Stok WJ, van Lieshout JJ. Detecting central hypovolemia in simulated hypovolemic shock by automated feature extraction with principal component analysis. Physiol Rep 2019; 6:e13895. [PMID: 30488597 PMCID: PMC6429974 DOI: 10.14814/phy2.13895] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/10/2018] [Accepted: 09/11/2018] [Indexed: 11/24/2022] Open
Abstract
Assessment of the volume status by blood pressure (BP) monitoring is difficult, since baroreflex control of BP makes it insensitive to blood loss up to about one liter. We hypothesized that a machine learning model recognizes the progression of central hypovolemia toward presyncope by extracting information of the noninvasive blood pressure waveform parametrized through principal component analysis. This was tested in healthy volunteers exposed to simulated hemorrhage by lower body negative pressure (LBNP). Fifty‐six healthy volunteers were subjected to progressive central hypovolemia. A support vector machine was trained on the blood pressure waveform. Three classes of progressive stages of hypovolemia were defined. The model was optimized for the number of principal components and regularization parameter for penalizing misclassification (cost): C. Model performance was expressed as accuracy, mean squared error (MSE), and kappa statistic (inter‐rater agreement). Forty‐six subjects developed presyncope of which 41 showed an increase in model classification severity from baseline to presyncope. In five of the remaining nine subjects (1 was excluded) it stagnated. Classification of samples during baseline and end‐stage LBNP had the highest accuracy (95% and 50%, respectively). Baseline and first stage of LBNP demonstrated the lowest MSE (0.01 respectively 0.32). Model MSE and accuracy did not improve for C values exceeding 0.01. Adding more than five principal components did not further improve accuracy or MSE. Increment in kappa halted after 10 principal components had been added. Automated feature extraction of the blood pressure waveform allows modeling of progressive hypovolemia with a support vector machine. The model distinguishes classes between baseline and presyncope.
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Affiliation(s)
- Björn J P van der Ster
- Department of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Department of Medical Biology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Laboratory for Clinical Cardiovascular Physiology, Center for Heart Failure Research, Academic Medical Center, Amsterdam, the Netherlands
| | - Berend E Westerhof
- Department of Medical Biology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Laboratory for Clinical Cardiovascular Physiology, Center for Heart Failure Research, Academic Medical Center, Amsterdam, the Netherlands.,Department of Pulmonary Diseases, Amsterdam Cardiovascular Sciences, VU University Medical Center, Amsterdam, the Netherlands
| | - Wim J Stok
- Department of Medical Biology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Laboratory for Clinical Cardiovascular Physiology, Center for Heart Failure Research, Academic Medical Center, Amsterdam, the Netherlands
| | - Johannes J van Lieshout
- Department of Internal Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Department of Medical Biology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands.,Laboratory for Clinical Cardiovascular Physiology, Center for Heart Failure Research, Academic Medical Center, Amsterdam, the Netherlands.,MRC/Arthritis Research, UK Centre for Musculoskeletal Ageing Research, School of Life Sciences, the Medical School, University of Nottingham Medical School, Queen's Medical Centre, Nottingham, United Kingdom
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