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Combining Home Monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations: results from the SELENE HF study.

Antonio D'OnofrioFrancesco SolimeneLeonardo CalòValeria CalviMiguel ViscusiDonato MelissanoVitantonio RussoAntonio RapacciuoloAndrea CampanaFabrizio CaravatiPaolo BonfantiGabriele ZanottoEdoardo GrondaAntonello VadoVittorio CalzolariGiovanni Luca BottoMassimo ZecchinLuca BontempiDaniele GiacopelliAlessio GargaroLuigi Padeletti
Published in: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology (2021)
With the developed algorithm, two-thirds of first post-implant HF hospitalizations could be predicted timely with only 0.7 false alerts per patient-year.
Keyphrases
  • heart failure
  • case report
  • acute heart failure
  • healthcare
  • machine learning
  • cardiac resynchronization therapy
  • deep learning
  • left ventricular
  • atrial fibrillation