Early Indication of Decompensated Heart Failure in Patients on Home-Telemonitoring: A Comparison of Prediction Algorithms Based on Daily Weight and Noninvasive Transthoracic Bio-impedance.
Illapha Cuba GyllenstenAlberto G BonomiKevin M GoodeHarald ReiterJoerg HabethaOliver AmftJohn G F ClelandPublished in: JMIR medical informatics (2016)
NITTI measurements decrease before decompensations, and combined with trend algorithms, improve the detection of HF decompensation over current guideline rules; however, many alerts are not associated with clinically overt decompensation.
Keyphrases
- heart failure
- machine learning
- ejection fraction
- end stage renal disease
- physical activity
- newly diagnosed
- chronic kidney disease
- deep learning
- healthcare
- body mass index
- atrial fibrillation
- left ventricular
- weight loss
- peritoneal dialysis
- patient reported outcomes
- computed tomography
- weight gain
- magnetic resonance
- magnetic resonance imaging
- liver failure
- loop mediated isothermal amplification
- quantum dots
- cardiac resynchronization therapy
- sensitive detection