Multiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical Practice
Leonardo Calò, Valter Bianchi, Donatella Ferraioli, Luca Santini, Antonio Dello Russo, Cosimo Carriere, Vincenzo Ezio Santobuono, Chiara Andreoli, Carmelo La Greca, Giuseppe Arena, A Talarico, Ennio Pisanò, Amato Santoro, Massimo Giammaria, Matteo Ziacchi, Miguel Viscusi, Ermenegildo De Ruvo, Monica Campari, Sergio Valsecchi, Antonio D’Onofrio, Monia Minati, Claudia Tota, Annamaria Martino, Vincenzo Tavoletta, Michele Manzo, Fabrizio Ammirati, Karim Mahfouz, Carlo Colaiaco, Federico Guerra, Anna Zorzin Fantasia, Vivian Lerner Amato, G Savarese, Dario Pellegrini, L. Pimpinicchio, Domenico Pecora, Cecilia Bartoli, V. Borrello, M. T. Ratti, F De Rosa, Felipe Antunes Quirino, C Tomaselli, Elisa Marino, C. Baiocchi, Oreste De Vivo, Bernardo Baccani, Claudia Amellone, Maria Teresa Lucciola, Andrea Angeletti, J Frisoni, M Brignoli, Alessandro Costa, Antonio Pangallo, F. Benedetto, Patrizia Pepi, Daniele Nicolis, Barbara Petracci, Giovanna Giubilato, L Carbonardi, Daniele Porcelli, Bizhan Romani, Lorenzo Maria Zuccaro
Abstract
Background: The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events. Methods: The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th–75th percentile: 6–16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold. Results: The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55–70.38], P <0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14–0.99], P =0.047). No differences in event rates were observed between in-office and remote alert management. Conclusions: This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02275637.