1
|
Numan L, Moazeni M, Oerlemans MI, Aarts E, Van Der Kaaij NP, Asselbergs FW, Van Laake LW. Data-driven monitoring in patients on left ventricular assist device support. Expert Rev Med Devices 2022; 19:677-685. [DOI: 10.1080/17434440.2022.2132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Affiliation(s)
- Lieke Numan
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Mehran Moazeni
- Department of Methodology and Statistics, Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, the Netherlands
| | - Marish I.F.J. Oerlemans
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
| | - Emmeke Aarts
- Department of Methodology and Statistics, Utrecht University, Heidelberglaan 8, 3584 CS, Utrecht, the Netherlands
| | - Niels P. Van Der Kaaij
- Department of Cardiothoracic Surgery, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Folkert W. Asselbergs
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, Gower Street, WC1E 6BT, London, UK
- Health Data Research UK and Institute of Health Informatics, University College London, Gower Street, WC1E 6BT, London, UK
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, the Netherlands
| | - Linda W. Van Laake
- Department of Cardiology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, Gower Street WC1E 6BT, London, UK
| |
Collapse
|
2
|
Mainsah BO, Patel PA, Chen XJ, Olsen C, Collins LM, Karra R. Novel Acoustic Biomarker of Quality of Life in Left Ventricular Assist Device Recipients. J Am Heart Assoc 2021; 10:e018588. [PMID: 33660516 PMCID: PMC8174227 DOI: 10.1161/jaha.120.018588] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/08/2021] [Indexed: 12/18/2022]
Abstract
Background Although technological advances to pump design have improved survival, left ventricular assist device (LVAD) recipients experience variable improvements in quality of life. Methods for optimizing LVAD support to improve quality of life are needed. We investigated whether acoustic signatures obtained from digital stethoscopes can predict patient-centered outcomes in LVAD recipients. Methods and Results We followed precordial sounds over 6 months in 24 LVAD recipients (8 HeartWare HVAD™, 16 HeartMate 3 [HM3]). Subjects recorded their precordial sounds with a digital stethoscope and completed a Kansas City Cardiomyopathy Questionnaire weekly. We developed a novel algorithm to filter LVAD sounds from recordings. Unsupervised clustering of LVAD-mitigated sounds revealed distinct groups of acoustic features. Of 16 HM3 recipients, 6 (38%) had a unique acoustic feature that we have termed the pulse synchronized sound based on its temporal association with the artificial pulse of the HM3. HM3 recipients with the pulse synchronized sound had significantly better Kansas City Cardiomyopathy Questionnaire scores at baseline (median, 89.1 [interquartile range, 86.2-90.4] versus 66.1 [interquartile range, 31.1-73.7]; P=0.03) and over the 6-month study period (marginal mean, 77.6 [95% CI, 66.3-88.9] versus 59.9 [95% CI, 47.9-70.0]; P<0.001). Mechanistically, the pulse synchronized sound shares acoustic features with patient-derived intrinsic sounds. Finally, we developed a machine learning algorithm to automatically detect the pulse synchronized sound within precordial sounds (area under the curve, 0.95, leave-one-subject-out cross-validation). Conclusions We have identified a novel acoustic biomarker associated with better quality of life in HM3 LVAD recipients, which may provide a method for assaying optimized LVAD support.
Collapse
Affiliation(s)
- Boyla O. Mainsah
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNC
| | | | - Xinlin J. Chen
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNC
| | - Cameron Olsen
- Division of CardiologyDepartment of MedicineDuke University Medical CenterDurhamNC
| | - Leslie M. Collins
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNC
| | - Ravi Karra
- Division of CardiologyDepartment of MedicineDuke University Medical CenterDurhamNC
| |
Collapse
|