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Cheng AL, Liu J, Bravo S, Miller JC, Pahlevan NM. Screening left ventricular systolic dysfunction in children using intrinsic frequencies of carotid pressure waveforms measured by a novel smartphone-based device. Physiol Meas 2023; 44:10.1088/1361-6579/acba7b. [PMID: 36753767 PMCID: PMC11073485 DOI: 10.1088/1361-6579/acba7b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Objective.Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients.Approach. Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%.Main results.The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameterω1was elevated among patients with reduced LVEF.Significance.A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.
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Affiliation(s)
- Andrew L Cheng
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Jing Liu
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
| | - Stephen Bravo
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Jennifer C Miller
- Division of Pediatric Cardiology, Children’s Hospital Los Angeles, Los Angeles, CA, United States of America
| | - Niema M Pahlevan
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States of America
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States of America
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Aghilinejad A, Alavi R, Rogers B, Amlani F, Pahlevan NM. Effects of vessel wall mechanics on non-invasive evaluation of cardiovascular intrinsic frequencies. J Biomech 2021; 129:110852. [PMID: 34775340 DOI: 10.1016/j.jbiomech.2021.110852] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/04/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
Intrinsic Frequency (IF) is a systems-based approach that provides valuable information for hemodynamic monitoring of the left ventricle (LV), the arterial system, and their coupling. Recent clinical studies have demonstrated the clinical significance of this method for prognosis and diagnosis of cardiovascular diseases. In IF analysis, two dominant instantaneous frequencies (ω1 and ω2) are extracted from arterial pressure waveforms. The value of ω1 is related to the dynamics of the LV and the value of ω2 is related to the dynamics of vascular function. This work investigates the effects of vessel wall mechanics on the accuracy and applicability of IFs extracted from vessel wall displacement waveforms compared to IFs extracted from pressure waveforms. In this study, we used a computational approach employing a fluid-structure interaction finite element method for various wall mechanics governed by linearly elastic, hyperelastic, and viscoelastic models. Results show that for vessels with elastic wall behavior, the error between displacement-based and pressure-based IFs is negligible. In the presence of stenosis or aneurysm in elastic arteries, the maximum errors associated with displacement-based IFs is less than 2%. For non-linear elastic and viscoelastic arteries, errors are more pronounced (where the former reaches up to 11% and the latter up to 27%). Our results ultimately suggest that displacement-based computations of ω1 and ω2 are accurate in vessels that exhibit elastic behavior (such as carotid arteries) and are suitable surrogates for pressure-based IFs. This is clinically significant because displacement-based IFs can be measured non-invasively.
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Affiliation(s)
- Arian Aghilinejad
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Rashid Alavi
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Bryson Rogers
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Faisal Amlani
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA
| | - Niema M Pahlevan
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, USA; Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, USA.
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Aghilinejad A, Amlani F, Liu J, Pahlevan NM. Accuracy and applicability of non-invasive evaluation of aortic wave intensity using only pressure waveforms in humans. Physiol Meas 2021; 42. [PMID: 34521071 DOI: 10.1088/1361-6579/ac2671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 09/14/2021] [Indexed: 01/09/2023]
Abstract
Background.Wave intensity (WI) analysis is a well-established method for quantifying the energy carried in arterial waves, providing valuable clinical information about cardiovascular function. The primary drawback of this method is the need for concurrent measurements of both pressure and flow waveforms.Objective. We have for the first time investigated the accuracy of a novel methodology for estimating wave intensity employing only single pressure waveform measurements; we studied both carotid- and radial-based estimations in a large heterogeneous cohort.Approach.Tonometry was performed alongside Doppler ultrasound to acquire measurements of both carotid and radial pressure waveforms as well as aortic flow waveforms in 2640 healthy and diseased participants (1439 female) in the Framingham Heart Study. Patterns consisting of two forward waves (Wf1, Wf2) and one backward wave (Wb1) along with reflection metrics were compared with those obtained from exact WI analysis.Main Results. Carotid-based estimates correlated well for forward peak amplitudes (Wf1,r = 0.85,p < 0.05; Wf2,r = 0.72,p < 0.05) and peak time (Wf1,r = 0.94,p < 0.05; Wf2,r = 0.98,p < 0.05), and radial-based estimates correlated fairly to poorly for amplitudes (Wf1,r = 0.62,p < 0.05; Wf2,r = 0.42,p < 0.05) and peak time (Wf1,r = 0.04,p = 0.10; Wf2,r = 0.75,p < 0.05). In all cases, estimated Wb1 measures were not correlated. Reflection metrics were well correlated for healthy patients (r = 0.67,p < 0.05), moderately correlated for valvular disease (r = 0.59,p < 0.05) and fairly correlated for CVD (r = 0.46,p < 0.05) and heart failure (r = 0.49,p < 0.05).Significance. These findings indicate that pressure-only WI produces accurate results only when forward contributions are of primary interest and only for carotid pressure waveforms. The pressure-only WI estimations of this work provide an important opportunity to further the goal of uncovering clinical insights through wave analysis affordably and non-invasively.
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Affiliation(s)
- Arian Aghilinejad
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Faisal Amlani
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Jing Liu
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America
| | - Niema M Pahlevan
- Department of Aerospace & Mechanical Engineering, University of Southern California, Los Angeles, United States of America.,Division of Cardiovascular Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, United States of America
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Alavi R, Dai W, Amlani F, Rinderknecht DG, Kloner RA, Pahlevan NM. Scalability of cardiovascular intrinsic frequencies: Validations in preclinical models and non-invasive clinical studies. Life Sci 2021; 284:119880. [PMID: 34389404 DOI: 10.1016/j.lfs.2021.119880] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/05/2021] [Accepted: 08/06/2021] [Indexed: 11/25/2022]
Abstract
AIMS Cardiovascular intrinsic frequencies (IFs) are associated with cardiovascular health and disease, separately capturing the systolic and diastolic information contained in a single (uncalibrated) arterial waveform. Previous clinical investigations related to IF have been restricted to studying chronic conditions, and hence its applicability for acute cardiovascular diseases has not been explored. Studies of cardiovascular complications such as acute myocardial infarction are difficult to perform in humans due to the high-risk and invasive nature of such procedures. Although they can be performed in preclinical (animal) models, the corresponding interpretation of IF measures and how they ultimately translate to humans is unknown. Hence, we studied the scalability of IF across species and sensor platforms. MATERIALS AND METHODS Scaled values of the two intrinsic frequencies ω1 and ω2 (corresponding to systolic and diastolic dynamics, respectively) were extracted from carotid waveforms acquired either non-invasively (via tonometry, Vivio or iPhone) in humans or invasively in rabbits and rats. KEY FINDINGS The scaled IF parameters for all species were found to fall within the same physiological ranges carrying similar statistical characteristics, even though body sizes and corresponding heart rates of the species were substantially different. Additionally, results demonstrated that all non-invasive sensor platforms were significantly correlated with each other for scaled IFs, suggesting that such analysis is device-agnostic and can be applied to upcoming wearable technologies. SIGNIFICANCE Ultimately, our results found that IFs are scalable across species, which is particularly valuable for the training of IF-based artificial intelligence systems using both preclinical and clinical data.
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Affiliation(s)
- Rashid Alavi
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wangde Dai
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Cardiovascular Research Institute, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Faisal Amlani
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States
| | | | - Robert A Kloner
- Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Cardiovascular Research Institute, Huntington Medical Research Institutes, Pasadena, CA, United States
| | - Niema M Pahlevan
- Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, United States; Division of Cardiovascular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States; Huntington Medical Research Institutes, Pasadena, CA, United States.
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Mogadam E, Shavelle DM, Giesler GM, Economides C, Lidia SP, Duquette S, Matthews RV, Pahlevan NM. Intrinsic frequency method for instantaneous assessment of left ventricular-arterial coupling after transcatheter aortic valve replacement. Physiol Meas 2020; 41:085002. [DOI: 10.1088/1361-6579/aba67f] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Pahlevan NM, Mazandarani SP. Estimation of Wave Condition Number From Pressure Waveform Alone and Its Changes With Advancing Age in Healthy Women and Men. Front Physiol 2020; 11:313. [PMID: 32328003 PMCID: PMC7161432 DOI: 10.3389/fphys.2020.00313] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 03/19/2020] [Indexed: 11/13/2022] Open
Abstract
Introduction The wave condition number (WCN) is a non-dimensional number that determines the state of arterial wave reflections. WCN is equal to HR × Leff/PWV where HR, Leff, and PWV are the heart rate, effective length, and pulse wave velocity, respectively. It has been shown that a value of WCN = 0.1 indicates the optimum state of arterial wave reflection in which left ventricle workload is minimized. The pressure wave, flow wave, and PWV are all required to compute WCN, which may limit the potential clinical utility of WCN. The aims of this study are as follows: (1) to assess the feasibility of approximating WCN from the pressure waveform alone (WCNPinf), and (2) to provide the proof-of-concept that WCNPinf can capture age related differences in arterial wave reflection among healthy women and men. Methods Previously published retrospective data composed of seventeen patients (age 19–54 years; 34.3 ± 9.6) were used to assess the accuracy of WCNPinf. The exact value of WCN was computed from PWV (measured by foot-to-foot method), HR, and Leff. A quarter wavelength relationship with minimum impedance modulus were used to compute Leff. WCNPinf was calculated using HR and the reflected wave arrival time. Previously published analyses from a healthy subset of the Anglo-Cardiff Collaborative Trial (ACCT) study population were used to investigate if non-invasive WCNPinf captures age related differences in arterial wave reflection among healthy women and men. Results A strong correlation (r = 0.83, p-value <0.0001) between WCNPinf and WCN was observed. The accuracy of WCNPinf was independent from relevant physiological parameters such as PWV, pulse pressure (PP), and HR. Similar changes in WCNPinf with advancing age were observed in both healthy men and healthy women. In young, healthy individuals (women and men) the WCNPinf was around 0.1 (the optimum value), and reduced with aging. Conclusion WCN can be approximated from a single pressure waveform and can capture age related arterial wave reflection alteration. These results are clinically significant since WCN can be extracted from a single non-invasive pressure waveform. Future studies will focus on investigating if WCN is associated with risk for onset of cardiovascular disease events.
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Affiliation(s)
- Niema M Pahlevan
- Department of Aerospace Mechanical Engineering, University of Southern California, Los Angeles, CA, United States.,Division of Cardiovascular Medicine, Department of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Sohrab P Mazandarani
- Department of Economics, Geography, and Political Science, Division of Language, Humanity, and Social Science, Riverside City College, Riverside, CA, United States
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