Author + information
- Received November 7, 2016
- Revision received December 10, 2016
- Accepted December 14, 2016
- Published online February 27, 2017.
- John P. Boehmer, MDa,∗ (, )
- Ramesh Hariharan, MDb,
- Fausto G. Devecchi, MDc,
- Andrew L. Smith, MDd,
- Giulio Molon, MDe,
- Alessandro Capucci, MDf,
- Qi An, PhDg,
- Viktoria Averina, PhDg,
- Craig M. Stolen, PhDg,
- Pramodsingh H. Thakur, PhDg,
- Julie A. Thompson, PhDg,
- Ramesh Wariar, PhDg,
- Yi Zhang, PhDg and
- Jagmeet P. Singh, MD, DPhilh
- aPenn State Hershey Medical Center, Hershey, Pennsylvania
- bUniversity of Texas Physicians, EP Heart, Houston, Texas
- cCardiac Arrhythmia Service, Lutheran Health Network, Fort Wayne, Indiana
- dEmory University, Atlanta, Georgia
- eCardiology Department, Sacro Cuore Hospital, Negrar, Italy
- fUniversità Politecnica Delle March, Ancona, Italy
- gBoston Scientific, St. Paul, Minnesota
- hMassachusetts General Hospital Heart Center, Boston, Massachusetts
- ↵∗Address for correspondence:
Dr. John P. Boehmer, Heart Failure Program, The Pennsylvania State University College of Medicine, The Penn State Hershey Medical Center, 500 University Drive, H047, Hershey, Pennsylvania 17033.
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. (Evaluation of Multisensor Data in Heart Failure Patients With Implanted Devices [MultiSENSE]; NCT01128166)
- cardiac devices
- cardiac resynchronization therapy
- heart failure
- remote monitoring
This study was funded by Boston Scientific. Dr. Boehmer is a consultant for Boston Scientific. Dr. Hariharan has received consultation fees from Biotronik, Boston Scientific, Medtronic, and St. Jude Medical. Dr. Molon is a consultant for Boston Scientific, Medtronic, Sorin, and St. Jude Medical. Dr. Capucci has received honoraria from Abbott, Bayer, Boehringer Ingelheim, Boston Scientific, Pfizer, and Sorin Italia. Drs. An, Averina, Stolen, Thakur, Thompson, Wariar, and Zhang are employees of Boston Scientific. Dr. Singh is a consultant for BackBeat, Biotronik, Boston Scientific, Impulse Dynamics, LivaNova, Medtronic, Respicardia, and St. Jude Medical. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received November 7, 2016.
- Revision received December 10, 2016.
- Accepted December 14, 2016.
- 2017 The Authors