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A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis

Nature Biomedical Engineering volume 6, pages 1134–1147 (2022)Cite this article

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Abstract

Preclinical models of aortic stenosis can induce left ventricular pressure overload and coarsely control the severity of aortic constriction. However, they do not recapitulate the haemodynamics and flow patterns associated with the disease. Here we report the development of a customizable soft robotic aortic sleeve that can mimic the haemodynamics and biomechanics of aortic stenosis. By allowing for the adjustment of actuation patterns and blood-flow dynamics, the robotic sleeve recapitulates clinically relevant haemodynamics in a porcine model of aortic stenosis, as we show via in vivo echocardiography and catheterization studies, and a combination of in vitro and computational analyses. Using in vivo and in vitro magnetic resonance imaging, we also quantified the four-dimensional blood-flow velocity profiles associated with the disease and with bicommissural and unicommissural defects re-created by the robotic sleeve. The design of the sleeve, which can be adjusted on the basis of computed tomography data, allows for the design of patient-specific devices that may guide clinical decisions and improve the management and treatment of patients with aortic stenosis.

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Fig. 1: Concept and design of the soft robotic aortic sleeve.
Fig. 2: In vitro and in silico characterization of the biomimetic soft robotic aortic sleeve.
Fig. 3: In vivo haemodynamics of quasi-static and dynamic aortic constriction.
Fig. 4: Tunability of aortic constriction profile by varying the actuation scheme of the biomimetic soft robotic aortic sleeve and in vivo MRI haemodynamics.
Fig. 5: CT-driven design of the soft robotic aortic sleeve to recapitulate patient-specific morphologies of AS.

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Data availability

All data supporting the findings of this study are available within the article and its Supplementary Information. The raw and analysed data generated during the study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

We acknowledge funding from the Harvard-Massachusetts Institute of Technology Health Sciences and Technology programme, the SITA Foundation Award from the Institute for Medical Engineering and Science, the MathWorks Engineering Fellowship Fund, the Fulbright-Turkey Fellowship, the Hassenfeld Research Scholarship, the Massachusetts General Hospital SPARK Award, and grants R01HL151704, R01HL135242 and R01HL159010 from the National Institutes of Health (NIH) National Heart Lung and Blood Institute (NHLBI). We thank N. Jin for providing the Siemens WIP for the 4D flow MRI sequence used in this study, and BioHues Digital for creating the illustrations in Figs. 1a and 3a.

Author information

Author notes

  1. These authors contributed equally: Luca Rosalia, Caglar Ozturk.

Authors and Affiliations

  1. Health Sciences and Technology Program, Harvard - Massachusetts Institute of Technology, Cambridge, MA, USA

    Luca Rosalia, Efrat M. Goffer, Elazer R. Edelman, Ellen T. Roche & Christopher T. Nguyen

  2. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Luca Rosalia, Caglar Ozturk, Yiling Fan, Manisha Singh, Debkalpa Goswami, Efrat M. Goffer, Elazer R. Edelman & Ellen T. Roche

  3. Cardiovascular Research Center, Massachusetts General Hospital, Charlestown, MA, USA

    Luca Rosalia, Jaume Coll-Font, Yiling Fan, Shi Chen, Robert A. Eder, Jo H. Kim, Salva Yurista, Benjamin P. Bonner, Anna N. Foster, Marcello Panagia, Jose L. Guerrero & Christopher T. Nguyen

  4. A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA

    Luca Rosalia, Jaume Coll-Font, Yiling Fan, Shi Chen, Robert A. Eder, Jo H. Kim, Salva Yurista, Benjamin P. Bonner, Anna N. Foster & Christopher T. Nguyen

  5. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Yiling Fan & Ellen T. Roche

  6. Cardiac Ultrasound Laboratory, Massachusetts General Hospital, Boston, MA, USA

    Yasufumi Nagata & Robert A. Levine

  7. Department of Medicine, Harvard Medical School, Boston, MA, USA

    Yasufumi Nagata, Robert A. Levine & Christopher T. Nguyen

  8. Corrigan Minehan Heart Center, Massachusetts General Hospital, Boston, MA, USA

    Adam Mauskapf

  9. Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA

    Elazer R. Edelman

  10. Cardiovascular Medicine Section, Department of Medicine, Boston University Medical Center, Boston, MA, USA

    Marcello Panagia

  11. Cardiovascular Innovation Research Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA

    Christopher T. Nguyen

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Contributions

L.R., C.O., M.P., E.T.R. and C.T.N. conceived the hypothesis and designed the experiments. L.R., C.O., J.C.-F., Y.F., Y.N., M.S., D.G., A.M., S.C., R.A.E., E.M.G., J.H.K., S.Y., B.P.B., A.N.F., R.A.L., E.R.E. and J.L.G performed the experiments. L.R., C.O., E.T.R. and C.T.N. analysed the results and wrote the manuscript. L.R. and C.O. contributed equally to this work. E.T.R. and C.T.N. equally supervised this research.

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Correspondence to Ellen T. Roche or Christopher T. Nguyen.

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Nature Biomedical Engineering thanks Lyes Kadem, Amanda Randles and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Sleeve tensioning and representative hemodynamic data in vitro and in silico under quasi-static and dynamic conditions.

a, Illustration of an adequately and loosely implanted sleeve, showing the sleeve-to-aorta diameter ratio for classification. Ratios smaller than 1 would result in over-tensioning, and thus in aortic pre-constriction. Ratios greater than 1.8 would cause under-tensioning. b, Representative in vivo peak aortic flow velocity on MRI at baseline (BL), intermediate (Int: 3 mL), and full (4 mL) constrictions for the loose, intermediate, and adequately-pretensioned sleeve. c, Representative in vitro LVP and AoP under quasi-static actuation conditions. d, Representative LVP tracing in vivo measured under quasi-static actuation conditions. e, Representative LVP and AoP tracings in vitro with dynamic actuation. f, Representative LVP tracing in vivo with dynamic actuation. The ON and OFF marks indicate the intervals during which the sleeve was inflated or deflated respectively, following the start of the triggering.

Source data

Extended Data Fig. 2 In vivo MRI aortic flow streamlines and turbulence kinetic energy (TKE).

a, Aortic flow streamlines for the stenosis (AS), bicommissural (Bi), and unicommissural (Uni) constriction profiles. b, TKE map of the aorta of a longitudinal cross-section of the aorta and for the same constriction profiles. c, Distribution of elements across ranges of TKE within the aortic domain. d, TKE line plots along an aortic diameter at the sleeve plane.

Source data

Extended Data Fig. 3 CFD hemodynamics and TKE.

a, Aortic flow streamlines for the stenosis, bicommissural, and unicommissural constriction profiles, with details of transverse planes at the ascending aorta (P1), aortic arch (P2), and descending aorta (P3). b, 2D velocity vector maps of a longitudinal cross-section of the aorta for the three constriction profiles. c, TKE map of the same longitudinal cross-section for the three constriction profiles. d, Distribution of elements across ranges of TKE within the aortic CFD domain. e, TKE line plots along an aortic diameter at the sleeve plane.

Source data

Extended Data Fig. 4 In vivo hemodynamic studies under dynamic actuation.

a–f, Clinical metrics of AS obtained via echocardiography and LV catheterization. These include the (a) iEOA, (b) ΔPmax, (c) ΔPmean, (d) vmax, (e) ELI, (f) ZVA. Error bars, s.d., n = 5 for each data point, with 5 consecutive measurements taken for 1 animal. g, 2D velocity vector maps of the aorta with corresponding flow cross-sectional planes at the ascending aorta (P1), aortic arch (P2), and descending aorta (P3). h, Corresponding TKE map of the aorta.

Source data

Extended Data Fig. 5 Global and local hemodynamics in the presence or absence of a healthy valve in series with the aortic sleeve in vitro.

(a) EOA, (b) ΔPmax, (c) ΔPmean, (d) LVPmax calculated at baseline (valve only; BL) and at 8 psi under dynamic actuation with (valve + sleeve) and without (sleeve only) a valve proximal to the sleeve. Error bars, s.d., n = 15 actuation cycles for each data point. e, LV PV loops at BL and 10 psi for the same groups. f, LV PV loops at BL, and for quasi-static and dynamic actuation. g-h, Longitudinal and cross-sectional 2D velocity vectors (g) before and (h) during actuation of the soft robotic aortic sleeve (sleeve only) and during actuation with a porcine valve inserted proximally to the sleeve (valve + sleeve). Results illustrate the cross-sectional geometry of the mock aortic vessel at the sleeve plane both prior to and during actuation for the two groups (sleeve only and valve + sleeve). Arrows indicate the direction of flow. Scale bar, 1.0 cm.

Source data

Supplementary information

Supplementary Information

Supplementary notes, figures, tables and references.

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Supplementary video 1

Quasi-static and dynamic actuation of the biomimetic soft robotic sleeve in a mock circulatory loop.

Supplementary video 2

Magnetic resonance imaging of the biomimetic soft robotic sleeve under dynamic actuation for various actuation profiles in a mock circulatory loop.

Supplementary video 3

Echocardiography and magnetic resonance imaging of the biomimetic soft robotic sleeve under quasi-static and dynamic actuation in vivo.

Supplementary video 4

Computational fluid-dynamics modelling for the prediction of aortic flow owing to dynamic actuation of the biomimetic soft robotic sleeve.

Supplementary data

Source data for Supplementary Figs. 3b–d.

Source data

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Rosalia, L., Ozturk, C., Coll-Font, J. et al. A soft robotic sleeve mimicking the haemodynamics and biomechanics of left ventricular pressure overload and aortic stenosis. Nat. Biomed. Eng 6, 1134–1147 (2022). https://doi.org/10.1038/s41551-022-00937-8

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