From a “Circulation: Heart Failure” Journal study (Feb 25, 2020):
The study shows that wearable sensors coupled with machine learning analytics have predictive accuracy comparable to implanted devices.
We demonstrate that machine learning analytics using data from a wearable sensor can accurately predict hospitalization for heart failure exacerbation…at a median time of 6.5 days before the admission.
Heart failure (HF) is a major public health problem affecting >23 million patients worldwide. Hospitalization costs for HF represent 80% of costs attributed to HF care. Thus, accurate and timely detection of worsening HF could allow for interventions aimed at reducing the risk of HF admission.
Several such approaches have been tested. Tracking of daily weight, as recommended by current HF guidelines, did not lead to reduction of the risk of HF hospitalization, most likely because the weight gain is a contemporaneous or lagging indicator rather than a leading event. Interventions based on intrathoracic impedance monitoring also did not result in reduction of readmission risk. The results suggest that physiological parameters other than weight or intrathoracic impedance in isolation may be needed to detect HF decompensation in a timely manner. In fact, 28% reduction of rehospitalization rates has been shown with interventions based on pulmonary artery hemodynamic monitoring. More recently, in the MultiSENSE study (Multisensor Chronic Evaluation in Ambulatory HF Patients), an algorithm based on physiological data from sensors in the implantable cardiac resynchronization therapy defibrillators, was shown to have 70% sensitivity in predicting the risk of HF hospitalization or outpatient visit with intravenous therapies for worsening of HF.