Literature DB >> 21549292

Hemodynamic factors associated with acute decompensated heart failure: part 2--use in automated detection.

Philip B Adamson1, Michael R Zile, Yong K Cho, Tom D Bennett, Robert C Bourge, Mark F Aaron, Juan M Aranda, William T Abraham, Fred J Kueffer, Robert T Taepke.   

Abstract

BACKGROUND: The purpose of this study was to develop an automated surveillance system, using pressure-based hemodynamic factors that would detect which patients were making the transition from compensated to decompensated heart failure before they developed worsening symptoms and required acute medical care. METHODS AND
RESULTS: Intracardiac pressures in 274 patients with heart failure were measured using an implantable hemodynamic monitor (IHM) and were analyzed in a retrospective manner. An automated pressure change detection (PCD) algorithm was developed using the cumulative sum method. The performance characteristics of the PCD algorithm were defined in all patients who developed a heart failure-related event (HFRE); patients without HFRE served as controls. Optimal PCD threshold values were chosen using a receiver operator curve analysis. Each of the pressures measured with the IHM were evaluated using the PCD analysis. All had sensitivities ≥80% and false-positive rates <4.7/patient-year; however, estimated pulmonary artery diastolic pressure (ePAD) had the best performance. An ePAD based on the optimized PCD threshold of 6.0 yielded a sensitivity of 83% and a false-positive rate of 4.1/patient-year for detecting patients making the transition from compensated to decompensated heart failure. These performance characteristics were not significantly different for patients with an ejection fraction > vs. <50%, estimated glomerular filtration rate > vs. <60 mL/min/1.73 m(2), or age > vs. <60 years.
CONCLUSIONS: The automated PCD algorithm had high sensitivity and acceptable false-positive rates in detecting the development of decompensated heart failure before the patient developed worsening symptoms and required acute medical care. These data support the development of a prospective study to examine the utility of adding an automated PCD algorithm to IHM-based management strategies to prevent decompensated heart failure.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21549292     DOI: 10.1016/j.cardfail.2011.01.011

Source DB:  PubMed          Journal:  J Card Fail        ISSN: 1071-9164            Impact factor:   5.712


  11 in total

1.  Customized laboratory TLR4 and TLR2 detection method from peripheral human blood for early detection of doxorubicin-induced cardiotoxicity.

Authors:  A L Pop-Moldovan; N-M Trofenciuc; D A Dărăbanţiu; C Precup; H Branea; R Christodorescu; M Puşchiţă
Journal:  Cancer Gene Ther       Date:  2017-03-03       Impact factor: 5.987

2.  Association of Ambulatory Hemodynamic Monitoring of Heart Failure With Clinical Outcomes in a Concurrent Matched Cohort Analysis.

Authors:  Jacob Abraham; Rupinder Bharmi; Orvar Jonsson; Guilherme H Oliveira; Andre Artis; Ali Valika; Robert Capodilupo; Philip B Adamson; Gregory Roberts; Nirav Dalal; Akshay S Desai; Raymond L Benza
Journal:  JAMA Cardiol       Date:  2019-06-01       Impact factor: 14.676

Review 3.  Remote Patient Monitoring in Heart Failure: Factors for Clinical Efficacy.

Authors:  Ankit Bhatia; Thomas M Maddox
Journal:  Int J Heart Fail       Date:  2020-11-30

4.  Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients With Heart Failure: A Feasibility Study.

Authors:  Md Mobashir Hasan Shandhi; Joanna Fan; J Alex Heller; Mozziyar Etemadi; Liviu Klein; Omer T Inan
Journal:  IEEE Trans Biomed Eng       Date:  2022-07-20       Impact factor: 4.756

5.  Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance.

Authors:  Illapha Cuba Gyllensten; Alberto G Bonomi; Kevin M Goode; Harald Reiter; Joerg Habetha; Oliver Amft; John Gf Cleland
Journal:  JMIR Med Inform       Date:  2016-02-18

6.  Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions.

Authors:  Illapha Cuba Gyllensten; Amanda Crundall-Goode; Ronald M Aarts; Kevin M Goode
Journal:  BMC Med Inform Decis Mak       Date:  2017-01-17       Impact factor: 2.796

Review 7.  Does Hemodynamic-Guided Heart Failure Management Reduce Hospitalization? A Systematic Review.

Authors:  Abdul M Minhas; Saba Ahmed; Muhammad S Khan; Kaneez Fatima; Muhammad N Anwar; Jonathan Constantin
Journal:  Cureus       Date:  2017-04-13

Review 8.  Nitrates for acute heart failure syndromes.

Authors:  Abel Wakai; Aileen McCabe; Rachel Kidney; Steven C Brooks; Rawle A Seupaul; Deborah B Diercks; Nigel Salter; Gregory J Fermann; Caroline Pospisil
Journal:  Cochrane Database Syst Rev       Date:  2013-08-06

9.  Remote Hemodynamic-Guided Therapy of Patients With Recurrent Heart Failure Following Cardiac Resynchronization Therapy.

Authors:  Niraj Varma; Robert C Bourge; Lynne Warner Stevenson; Maria Rosa Costanzo; David Shavelle; Philip B Adamson; Greg Ginn; John Henderson; William T Abraham
Journal:  J Am Heart Assoc       Date:  2021-02-25       Impact factor: 5.501

10.  Intracardiac impedance to track cardiac volume status during cardiac resynchronization therapy - The quest for a heart failure sensor.

Authors:  Niraj Varma
Journal:  Indian Pacing Electrophysiol J       Date:  2021 Jul-Aug
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