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).
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).
Authors: Varol Burak Aydemir; Supriya Nagesh; Md Mobashir Hasan Shandhi; Joanna Fan; Liviu Klein; Mozziyar Etemadi; James Alex Heller; Omer T Inan; James M Rehg Journal: IEEE Trans Biomed Eng Date: 2019-08-15 Impact factor: 4.538
Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yu-Feng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Circ Arrhythm Electrophysiol Date: 2021-02-12
Authors: David Duncker; Roman Michalski; Johanna Müller-Leisse; Christos Zormpas; Thorben König; Christian Veltmann Journal: Herzschrittmacherther Elektrophysiol Date: 2017-08-15