A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study

John P. Boehmer; Ramesh Hariharan; Fausto G. Devecchi; Andrew L. Smith; Giulio Molon; Alessandro Capucci; Qi An; Viktoria Averina; Craig M. Stolen; Pramodsingh H. Thakur; Julie A. Thompson; Ramesh Wariar; Yi Zhang; Jagmeet P. Singh

doi:10.1016/j.jchf.2016.12.011

A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study

John P. Boehmer, Ramesh Hariharan, Fausto G. Devecchi, Andrew L. Smith, Giulio Molon, Alessandro Capucci, Qi An, Viktoria Averina, Craig M. Stolen, Pramodsingh H. Thakur, Julie A. Thompson, Ramesh Wariar, Yi Zhang, Jagmeet P. Singh

Research output: Contribution to journal › Article › peer-review

264 Scopus citations

Abstract

Objectives The aim of this study was to develop and validate a device-based diagnostic algorithm to predict heart failure (HF) events. Background HF involves costly hospitalizations with adverse impact on patient outcomes. The authors hypothesized that an algorithm combining a diverse set of implanted device-based sensors chosen to target HF pathophysiology could detect worsening HF. Methods The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study enrolled patients with investigational chronic ambulatory data collection via implanted cardiac resynchronization therapy defibrillators. HF events (HFEs), defined as HF admissions or unscheduled visits with intravenous treatment, were independently adjudicated. The development cohort of patients was used to construct a composite index and alert algorithm (HeartLogic) combining heart sounds, respiration, thoracic impedance, heart rate, and activity; the test cohort was sequestered for independent validation. The 2 coprimary endpoints were sensitivity to detect HFE >40% and unexplained alert rate <2 alerts per patient-year. Results Overall, 900 patients (development cohort, n = 500; test cohort, n = 400) were followed for up to 1 year. Coprimary endpoints were evaluated using 320 patient-years of follow-up data and 50 HFEs in the test cohort (72% men; mean age 66.8 ± 10.3 years; New York Heart Association functional class at enrollment: 69% in class II, 25% in class III; mean left ventricular ejection fraction 30.0 ± 11.4%). Both endpoints were significantly exceeded, with sensitivity of 70% (95% confidence interval [CI]: 55.4% to 82.1%) and an unexplained alert rate of 1.47 per patient-year (95% CI: 1.32 to 1.65). The median lead time before HFE was 34.0 days (interquartile range: 19.0 to 66.3 days). Conclusions The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation.

Original language	English (US)
Pages (from-to)	216-225
Number of pages	10
Journal	JACC: Heart Failure
Volume	5
Issue number	3
DOIs	https://doi.org/10.1016/j.jchf.2016.12.011
State	Published - Mar 1 2017

All Science Journal Classification (ASJC) codes

Cardiology and Cardiovascular Medicine

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10.1016/j.jchf.2016.12.011

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Boehmer, J. P., Hariharan, R., Devecchi, F. G., Smith, A. L., Molon, G., Capucci, A., An, Q., Averina, V., Stolen, C. M., Thakur, P. H., Thompson, J. A., Wariar, R., Zhang, Y., & Singh, J. P. (2017). A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study. JACC: Heart Failure, 5(3), 216-225. https://doi.org/10.1016/j.jchf.2016.12.011

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title = "A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study",

abstract = "Objectives The aim of this study was to develop and validate a device-based diagnostic algorithm to predict heart failure (HF) events. Background HF involves costly hospitalizations with adverse impact on patient outcomes. The authors hypothesized that an algorithm combining a diverse set of implanted device-based sensors chosen to target HF pathophysiology could detect worsening HF. Methods The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study enrolled patients with investigational chronic ambulatory data collection via implanted cardiac resynchronization therapy defibrillators. HF events (HFEs), defined as HF admissions or unscheduled visits with intravenous treatment, were independently adjudicated. The development cohort of patients was used to construct a composite index and alert algorithm (HeartLogic) combining heart sounds, respiration, thoracic impedance, heart rate, and activity; the test cohort was sequestered for independent validation. The 2 coprimary endpoints were sensitivity to detect HFE >40% and unexplained alert rate <2 alerts per patient-year. Results Overall, 900 patients (development cohort, n = 500; test cohort, n = 400) were followed for up to 1 year. Coprimary endpoints were evaluated using 320 patient-years of follow-up data and 50 HFEs in the test cohort (72% men; mean age 66.8 ± 10.3 years; New York Heart Association functional class at enrollment: 69% in class II, 25% in class III; mean left ventricular ejection fraction 30.0 ± 11.4%). Both endpoints were significantly exceeded, with sensitivity of 70% (95% confidence interval [CI]: 55.4% to 82.1%) and an unexplained alert rate of 1.47 per patient-year (95% CI: 1.32 to 1.65). The median lead time before HFE was 34.0 days (interquartile range: 19.0 to 66.3 days). Conclusions The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation.",

author = "Boehmer, {John P.} and Ramesh Hariharan and Devecchi, {Fausto G.} and Smith, {Andrew L.} and Giulio Molon and Alessandro Capucci and Qi An and Viktoria Averina and Stolen, {Craig M.} and Thakur, {Pramodsingh H.} and Thompson, {Julie A.} and Ramesh Wariar and Yi Zhang and Singh, {Jagmeet P.}",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors",

year = "2017",

month = mar,

day = "1",

doi = "10.1016/j.jchf.2016.12.011",

language = "English (US)",

volume = "5",

pages = "216--225",

journal = "JACC: Heart Failure",

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Boehmer, JP, Hariharan, R, Devecchi, FG, Smith, AL, Molon, G, Capucci, A, An, Q, Averina, V, Stolen, CM, Thakur, PH, Thompson, JA, Wariar, R, Zhang, Y & Singh, JP 2017, 'A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study', JACC: Heart Failure, vol. 5, no. 3, pp. 216-225. https://doi.org/10.1016/j.jchf.2016.12.011

TY - JOUR

T1 - A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices

T2 - Results From the MultiSENSE Study

AU - Boehmer, John P.

AU - Hariharan, Ramesh

AU - Devecchi, Fausto G.

AU - Smith, Andrew L.

AU - Molon, Giulio

AU - Capucci, Alessandro

AU - An, Qi

AU - Averina, Viktoria

AU - Stolen, Craig M.

AU - Thakur, Pramodsingh H.

AU - Thompson, Julie A.

AU - Wariar, Ramesh

AU - Zhang, Yi

AU - Singh, Jagmeet P.

PY - 2017/3/1

Y1 - 2017/3/1

N2 - Objectives The aim of this study was to develop and validate a device-based diagnostic algorithm to predict heart failure (HF) events. Background HF involves costly hospitalizations with adverse impact on patient outcomes. The authors hypothesized that an algorithm combining a diverse set of implanted device-based sensors chosen to target HF pathophysiology could detect worsening HF. Methods The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study enrolled patients with investigational chronic ambulatory data collection via implanted cardiac resynchronization therapy defibrillators. HF events (HFEs), defined as HF admissions or unscheduled visits with intravenous treatment, were independently adjudicated. The development cohort of patients was used to construct a composite index and alert algorithm (HeartLogic) combining heart sounds, respiration, thoracic impedance, heart rate, and activity; the test cohort was sequestered for independent validation. The 2 coprimary endpoints were sensitivity to detect HFE >40% and unexplained alert rate <2 alerts per patient-year. Results Overall, 900 patients (development cohort, n = 500; test cohort, n = 400) were followed for up to 1 year. Coprimary endpoints were evaluated using 320 patient-years of follow-up data and 50 HFEs in the test cohort (72% men; mean age 66.8 ± 10.3 years; New York Heart Association functional class at enrollment: 69% in class II, 25% in class III; mean left ventricular ejection fraction 30.0 ± 11.4%). Both endpoints were significantly exceeded, with sensitivity of 70% (95% confidence interval [CI]: 55.4% to 82.1%) and an unexplained alert rate of 1.47 per patient-year (95% CI: 1.32 to 1.65). The median lead time before HFE was 34.0 days (interquartile range: 19.0 to 66.3 days). Conclusions The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation.

AB - Objectives The aim of this study was to develop and validate a device-based diagnostic algorithm to predict heart failure (HF) events. Background HF involves costly hospitalizations with adverse impact on patient outcomes. The authors hypothesized that an algorithm combining a diverse set of implanted device-based sensors chosen to target HF pathophysiology could detect worsening HF. Methods The MultiSENSE (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients) study enrolled patients with investigational chronic ambulatory data collection via implanted cardiac resynchronization therapy defibrillators. HF events (HFEs), defined as HF admissions or unscheduled visits with intravenous treatment, were independently adjudicated. The development cohort of patients was used to construct a composite index and alert algorithm (HeartLogic) combining heart sounds, respiration, thoracic impedance, heart rate, and activity; the test cohort was sequestered for independent validation. The 2 coprimary endpoints were sensitivity to detect HFE >40% and unexplained alert rate <2 alerts per patient-year. Results Overall, 900 patients (development cohort, n = 500; test cohort, n = 400) were followed for up to 1 year. Coprimary endpoints were evaluated using 320 patient-years of follow-up data and 50 HFEs in the test cohort (72% men; mean age 66.8 ± 10.3 years; New York Heart Association functional class at enrollment: 69% in class II, 25% in class III; mean left ventricular ejection fraction 30.0 ± 11.4%). Both endpoints were significantly exceeded, with sensitivity of 70% (95% confidence interval [CI]: 55.4% to 82.1%) and an unexplained alert rate of 1.47 per patient-year (95% CI: 1.32 to 1.65). The median lead time before HFE was 34.0 days (interquartile range: 19.0 to 66.3 days). Conclusions The HeartLogic multisensor index and alert algorithm provides a sensitive and timely predictor of impending HF decompensation.

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ER -

A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study

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