open access

Vol 25, No 2 (2018)
Original articles — Clinical cardiology
Published online: 2017-06-20
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Refined heart failure detection algorithm for improved clinical reliability of OptiVol alerts in CRT-D recipients

Mate Vamos, Noemi Nyolczas, Zsolt Bari, Peter Bogyi, Balazs Muk, Barna Szabo, Bettina Ancsin, Robert G. Kiss, Gabor Z. Duray
DOI: 10.5603/CJ.a2017.0077
·
Pubmed: 28653309
·
Cardiol J 2018;25(2):236-244.

open access

Vol 25, No 2 (2018)
Original articles — Clinical cardiology
Published online: 2017-06-20

Abstract

 Background: The reliability of intrathoracic impedance monitoring for prediction of heart failure (HF) by implantable cardiac devices is controversial. Despite using additional device-based parameters described in the PARTNERS HF study, such as new onset of arrhythmias, abnormal autonomics, low biventricular pacing rate or patient activity level, the predictive power of device diagnostic algorithm is still in doubt. The objective of this study was to compare the device diagnostic algorithm described in the PARTNERS HF study to a newly developed algorithm applying refined diagnostic criteria.

Methods: Fourty two patients were prospectively enrolled who had been implanted with an intrathoracic impedance and remote monitoring capable implantable cardiac defibrillator with a cardiac resychroniza­tion therapy (CRT-D) device in this observational study. If a remote OptiVolTM alert occurred, patients were checked for presence of HF symptoms. A new algorithm was derived from the original PARTNERS HF criteria, considering more sensitive cut-offs and changes of patterns of the device-based parameters.

Results: During an average follow-up of 38 months, 722 remote transmissions were received. From the total of 128 transmissions with OptiVol alerts, 32 (25%) corresponded to true HF events. Upon multivariate discriminant analysis, low patient activity, high nocturnal heart rate, and low CRT pacing (< 90%) proved to be independent predictors of true HF events (all p < 0.01). Incorporating these three refined criteria in a new algorithm, the diagnostic yield of OptiVol was improved by increasing specific­ity from 37.5% to 86.5%, positive predictive value from 34.1% to 69.8% and area under the curve from 0.787 to 0.922 (p < 0.01), without a relevant loss in sensitivity (96.9% vs. 93.8%).

Conclusions: A refined device diagnostic algorithm based on the parameters of low activity level, high nocturnal heart rate, and suboptimal biventricular pacing might improve the clinical reliability of OptiVol alerts.  

Abstract

 Background: The reliability of intrathoracic impedance monitoring for prediction of heart failure (HF) by implantable cardiac devices is controversial. Despite using additional device-based parameters described in the PARTNERS HF study, such as new onset of arrhythmias, abnormal autonomics, low biventricular pacing rate or patient activity level, the predictive power of device diagnostic algorithm is still in doubt. The objective of this study was to compare the device diagnostic algorithm described in the PARTNERS HF study to a newly developed algorithm applying refined diagnostic criteria.

Methods: Fourty two patients were prospectively enrolled who had been implanted with an intrathoracic impedance and remote monitoring capable implantable cardiac defibrillator with a cardiac resychroniza­tion therapy (CRT-D) device in this observational study. If a remote OptiVolTM alert occurred, patients were checked for presence of HF symptoms. A new algorithm was derived from the original PARTNERS HF criteria, considering more sensitive cut-offs and changes of patterns of the device-based parameters.

Results: During an average follow-up of 38 months, 722 remote transmissions were received. From the total of 128 transmissions with OptiVol alerts, 32 (25%) corresponded to true HF events. Upon multivariate discriminant analysis, low patient activity, high nocturnal heart rate, and low CRT pacing (< 90%) proved to be independent predictors of true HF events (all p < 0.01). Incorporating these three refined criteria in a new algorithm, the diagnostic yield of OptiVol was improved by increasing specific­ity from 37.5% to 86.5%, positive predictive value from 34.1% to 69.8% and area under the curve from 0.787 to 0.922 (p < 0.01), without a relevant loss in sensitivity (96.9% vs. 93.8%).

Conclusions: A refined device diagnostic algorithm based on the parameters of low activity level, high nocturnal heart rate, and suboptimal biventricular pacing might improve the clinical reliability of OptiVol alerts.  

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Keywords

congestive heart failure, remote monitoring, intrathoracic impedance monitoring, CRT-D, OptiVol

About this article
Title

Refined heart failure detection algorithm for improved clinical reliability of OptiVol alerts in CRT-D recipients

Journal

Cardiology Journal

Issue

Vol 25, No 2 (2018)

Pages

236-244

Published online

2017-06-20

DOI

10.5603/CJ.a2017.0077

Pubmed

28653309

Bibliographic record

Cardiol J 2018;25(2):236-244.

Keywords

congestive heart failure
remote monitoring
intrathoracic impedance monitoring
CRT-D
OptiVol

Authors

Mate Vamos
Noemi Nyolczas
Zsolt Bari
Peter Bogyi
Balazs Muk
Barna Szabo
Bettina Ancsin
Robert G. Kiss
Gabor Z. Duray

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