title: Noninvasive predictor of HeartMate XVE pump failure by neural network and waveform analysis.
abstract: Patients increasingly require longer durations of left ventricular assist device (LVAD) therapy. Despite a recent trend toward continuous flow VADs, the HeartMate XVE is still commonly used, but its longevity remains a significant limitation. Existing surveillance methods of pump failure often give inconclusive results. XVE electrical current waveforms were collected regularly (2001-2008) and sorted into quartiles according to number of days until pump failure (Q1, 0-34; Q2, 34-160; Q3, 160-300; and Q4, 300-390 days). Thoratec waveform files were converted into text files. The 10-second electrical current, voltage waveform was identified and isolated for analysis. Waveforms were analyzed by principal component analysis (PCA) and with a fast Fourier transform. Quartiles were compared with analysis of variance (ANOVA). Waveforms (n = 454) were collected for 21 patients with failed pumps. An artificial neural network was used to predict pump failure within 30 days from the waveform characteristics identified though signal processing.
authors.affiliation: Utah Artificial Heart Program, Intermountain Medical Center part of Intermountain Healthcare, Murray, Utah, USA. nathan.mason@imal.org
authors.forename: Nathanael O
authors.initials: NO
authors.lastname: Mason
authors.forename: Corey J
authors.initials: CJ
authors.lastname: Bishop
authors.forename: Abdallah G
authors.initials: AG
authors.lastname: Kfoury
authors.forename: Robert L
authors.initials: RL
authors.lastname: Lux
authors.forename: Caleb
authors.initials: C
authors.lastname: Crawford
authors.forename: Benjamin D
authors.initials: BD
authors.lastname: Horne
authors.forename: Sandi
authors.initials: S
authors.lastname: Stoker
authors.forename: Stephen E
authors.initials: SE
authors.lastname: Clayson
authors.forename: Brad Y
authors.initials: BY
authors.lastname: Rasmusson
authors.forename: Bruce B
authors.initials: BB
authors.lastname: Reid
mesh_terms: D004868:Equipment Failure; D019544:Equipment Failure Analysis / Q000379:methods*; D005260:Female; D006353:Heart-Assist Devices; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D025341:Principal Component Analysis