Liviu Klein

Heart failure (HF) is an escalating public health problem, with few effective methods for home monitoring. In HF management, the important clinical factors to monitor include symptoms, fluid status, cardiac output, and blood pressure - based on these factors, inotrope and diuretic dosages are adjusted day-by-day to control the disorder and improve the patient's status towards a successful discharge. Previously, the ballistocardiogram (BCG) measured on a weighing scale has been shown to be capable of detecting changes in cardiac output and contractility for healthy subjects. In this study, we investigated whether the BCG and electrocardiogram (ECG) signals measured on a wireless modified scale could accurately track the clinical status of HF patients during their hospital stay. Using logistic regression, we found that the root-mean-square (RMS) power of the BCG provided a good fit for clinical status, as determined based on clinical measurements and symptoms, for the 85 patient days studied from 10 patients (p <; 0.01). These results provide a promising foundation for future studies aimed at using the BCG / ECG scale at home to track HF patient status remotely.

The recent resurgence of ballistocardiogram (BCG) measurement and interpretation technologies has led to a wide range of powerful tools available for unobtrusively assessing mechanical aspects of cardiovascular health at home. Researchers have demonstrated a multitude of modern BCG measurement modalities, including beds, chairs, weighing scales, and wearable approaches. However, many modalities produce significant variations in the morphology of the measured BCG, creating confusion in the analysis and interpretation of the signals. This paper creates a framework for comparing wearable BCG measurements to whole body measurements - such as taken with a weighing scale system - to eventually allow the same analysis and interpretation tools that have been developed for whole body systems to be applied in the future to wearable systems. To the best of our knowledge, it represents the first attempt to morphologically compare vertical acceleration recordings measured on different locations on the torso to whole body displacements measured by BCG instrumentation.

Each year, there are over one million hospitalizations for heart failure in the United States, with a similar number in Western Europe. Although these patients respond to initial therapies, they have very high short and intermediate term (2-6 months) mortality and readmission rates, while the healthcare system incurs substantial costs. Several risk prediction models that can accurately identify high-risk patients have been developed using data from clinical trials, large registries or administrative databases. Use of multi-variable risk models at the time of hospital admission or discharge offers better risk stratification and should be encouraged, as it allows for appropriate allocation of existing resources and development of clinical trials testing new treatment strategies for patients admitted with heart failure.