scholarly journals Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study

10.2196/19227 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e19227
Author(s):  
Markus Mach ◽  
Victoria Watzal ◽  
Waseem Hasan ◽  
Martin Andreas ◽  
Bernhard Winkler ◽  
...  
Keyword(s):  
Heart Rate ◽  
Aortic Valve ◽  
Hospital Mortality ◽  
Health Monitoring ◽  
Monitoring Device ◽  
Proof Of Concept ◽  
Concept Study ◽  
Transcatheter Aortic Valve ◽  
Frailty Assessment ◽  
Fitness Tracker

Background While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. Objective The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. Methods In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. Results The overall preprocedural stress level (P=.02), minutes of high stress per day (P=.02), minutes of rest per day (P=.045), and daily heart rate maximum (P=.048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; P=.048; GSC-6MWT AUC 0.671, CI 0.487-0.855; P=.42; EFS-C AUC 0.636, CI 0.254-1.000; P=.44). Conclusions This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments.

scholarly journals Fitness-Tracker Assisted Frailty-Assessment Before Transcatheter Aortic Valve Implantation: Proof-of-Concept Study (Preprint)

2020 ◽  
Author(s):  
Markus Mach ◽  
Victoria Watzal ◽  
Waseem Hasan ◽  
Martin Andreas ◽  
Bernhard Winkler ◽  
...  
Keyword(s):  
Heart Rate ◽  
Aortic Valve ◽  
Hospital Mortality ◽  
Health Monitoring ◽  
Monitoring Device ◽  
Proof Of Concept ◽  
Concept Study ◽  
Transcatheter Aortic Valve ◽  
Frailty Assessment ◽  
Fitness Tracker

BACKGROUND While transcatheter aortic valve replacement (TAVR) has revolutionized the treatment of aortic valve stenosis, wearable health-monitoring devices are gradually transforming digital patient care. OBJECTIVE The aim of this study was to develop a simple, efficient, and economical method for preprocedural frailty assessment based on parameters measured by a wearable health-monitoring device. METHODS In this prospective study, we analyzed data of 50 consecutive patients with mean (SD) age of 77.5 (5.1) years and a median (IQR) European system for cardiac operative risk evaluation (EuroSCORE) II of 3.3 (4.1) undergoing either transfemoral or transapical TAVR between 2017 and 2018. Every patient was fitted with a wrist-worn health-monitoring device (Garmin Vivosmart 3) for 1 week prior to the procedure. Twenty different parameters were measured, and threshold levels for the 3 most predictive categories (ie, step count, heart rate, and preprocedural stress) were calculated. Patients were assigned 1 point per category for exceeding the cut-off value and were then classified into 4 stages (no, borderline, moderate, and severe frailty). Furthermore, the FItness-tracker assisted Frailty-Assessment Score (FIFA score) was compared with the scores of the preprocedural gait speed category derived from the 6-minute walk test (GSC-6MWT) and the Edmonton Frail Scale classification (EFS-C). The primary study endpoint was hospital mortality. RESULTS The overall preprocedural stress level (<i>P=.</i>02), minutes of high stress per day (<i>P=.</i>02), minutes of rest per day (<i>P=.</i>045), and daily heart rate maximum (<i>P=.</i>048) as single parameters were the strongest predictors of hospital mortality. When comparing the different frailty scores, the FIFA score demonstrated the greatest predictive power for hospital mortality (FIFA area under the curve [AUC] 0.844, CI 0.656-1.000; <i>P=.</i>048; GSC-6MWT AUC 0.671, CI 0.487-0.855; <i>P=.</i>42; EFS-C AUC 0.636, CI 0.254-1.000; <i>P=.</i>44). CONCLUSIONS This proof-of-concept study demonstrates the strong predictive performance of the FIFA score compared to that of the conventional frailty assessments.

Valve-in-Valve Implantation Using a Novel Supravalvular Transcatheter Aortic Valve: Proof of Concept

2009 ◽  
Vol 88 (6) ◽  
pp. 1864-1869 ◽  
Author(s):  
Ali N. Azadani ◽  
Nicolas Jaussaud ◽  
Peter B. Matthews ◽  
Liang Ge ◽  
T. Sloane Guy ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Proof Of Concept ◽  
Transcatheter Aortic Valve ◽  
Valve In Valve ◽  
Valve Implantation

Bleeding Complications Drive In-Hospital Mortality of Patients with Atrial Fibrillation after Transcatheter Aortic Valve Replacement

2020 ◽  
Vol 120 (11) ◽  
pp. 1580-1586 ◽  
Author(s):  
Achim Lother ◽  
Klaus Kaier ◽  
Ingo Ahrens ◽  
Wolfgang Bothe ◽  
Dennis Wolf ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Risk Factor ◽  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Hospital Mortality ◽  
Valve Replacement ◽  
Transcatheter Aortic Valve Replacement ◽  
Bleeding Complications ◽  
Hospital Outcome ◽  
Transcatheter Aortic Valve

Abstract Background Atrial fibrillation (AF) is a risk factor for poor postoperative outcome after transfemoral transcatheter aortic valve replacement (TF-TAVR). The present study analyses the outcomes after TF-TAVR in patients with or without AF and identifies independent predictors for in-hospital mortality in clinical practice. Methods and Results Among all 57,050 patients undergoing isolated TF-TAVR between 2008 and 2016 in Germany, 44.2% of patients (n = 25,309) had AF. Patients with AF were at higher risk for unfavorable in-hospital outcome after TAVR. Including all baseline characteristics for a risk-adjusted comparison, AF was an independent risk factor for in-hospital mortality after TAVR. Among patients with AF, EuroSCORE, New York Heart Association classification class, or renal disease had only moderate effects on mortality, while the occurrence of postprocedural stroke or moderate to major bleeding substantially increased in-hospital mortality (odds ratio [OR] 3.35, 95% confidence interval [CI] 2.61–4.30, p < 0.001 and OR 3.12, 95% CI 2.68–3.62, p < 0.001). However, the strongest independent predictor for in-hospital mortality among patients with AF was severe bleeding (OR 18.00, 95% CI 15.22–21.30, p < 0.001). Conclusion The present study demonstrates that the incidence of bleeding defines the in-hospital outcome of patients with AF after TF-TAVR. Thus, the periprocedural phase demands particular care in bleeding prevention.

scholarly journals 403 Simulation of Altered Blood Flow in Bicuspid Aortic Valve Disease: A Proof of Concept Study

2020 ◽  
Vol 29 ◽  
pp. S219
Author(s):  
L. Holmes ◽  
G. Black ◽  
R. Jeremy ◽  
R. Cordina ◽  
D. Celermajer ◽  
...  
Keyword(s):  
Blood Flow ◽  
Aortic Valve ◽  
Bicuspid Aortic Valve ◽  
Aortic Valve Disease ◽  
Valve Disease ◽  
Proof Of Concept ◽  
Concept Study ◽  
Altered Blood

Morphometric and traditional frailty assessment in transcatheter aortic valve implantation

2020 ◽  
Vol 21 (10) ◽  
pp. 779-786 ◽  
Author(s):  
Mohammed A. Waduud ◽  
Marilena Giannoudi ◽  
Michael Drozd ◽  
Penelope P.J. Sucharitkul ◽  
Thomas A. Slater ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Transcatheter Aortic Valve Implantation ◽  
Transcatheter Aortic Valve ◽  
Frailty Assessment ◽  
Aortic Valve Implantation ◽  
Valve Implantation

Modifiable Predictors of In-Hospital Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement

2018 ◽  
Vol 356 (2) ◽  
pp. 135-140 ◽  
Author(s):  
Oluwaseun A. Akinseye ◽  
Muhammad Shahreyar ◽  
Chioma C. Nwagbara ◽  
Mannu Nayyar ◽  
Salem A. Salem ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Aortic Valve Replacement ◽  
Hospital Mortality ◽  
Valve Replacement ◽  
Transcatheter Aortic Valve Replacement ◽  
Transcatheter Aortic Valve

scholarly journals Intranasal oxytocin increases heart-rate variability in men at clinical high risk for psychosis: a proof-of-concept study

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel Martins ◽  
Cathy Davies ◽  
Andrea De Micheli ◽  
Dominic Oliver ◽  
Alicja Krawczun-Rygmaczewska ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
High Risk ◽  
Clinical High Risk ◽  
Proof Of Concept ◽  
Concept Study

scholarly journals A Proof of Concept Study of Using Machine-Learning in Artificial Aortic Valve Design: From Leaflet Design to Stress Analysis

2019 ◽  
Vol 6 (4) ◽  
pp. 104 ◽  
Author(s):  
Liang Liang ◽  
Bill Sun
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Aortic Valve ◽  
Stress Analysis ◽  
Heart Valves ◽  
Deep Neural Networks ◽  
Design Parameters ◽  
Attractive Alternative ◽  
Proof Of Concept ◽  
Concept Study

Artificial heart valves, used to replace diseased human heart valves, are life-saving medical devices. Currently, at the device development stage, new artificial valves are primarily assessed through time-consuming and expensive benchtop tests or animal implantation studies. Computational stress analysis using the finite element (FE) method presents an attractive alternative to physical testing. However, FE computational analysis requires a complex process of numeric modeling and simulation, as well as in-depth engineering expertise. In this proof of concept study, our objective was to develop machine learning (ML) techniques that can estimate the stress and deformation of a transcatheter aortic valve (TAV) from a given set of TAV leaflet design parameters. Two deep neural networks were developed and compared: the autoencoder-based ML-models and the direct ML-models. The ML-models were evaluated through Monte Carlo cross validation. From the results, both proposed deep neural networks could accurately estimate the deformed geometry of the TAV leaflets and the associated stress distributions within a second, with the direct ML-models (ML-model-d) having slightly larger errors. In conclusion, although this is a proof-of-concept study, the proposed ML approaches have demonstrated great potential to serve as a fast and reliable tool for future TAV design.

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