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.

Heart Failure (HF) is a common reason for hospitalization. Admissions might be prevented by early detection of and intervention for decompensation. Conventionally, changes in weight, a possible measure of fluid accumulation, have been used to detect deterioration. Transthoracic impedance may be a more sensitive and accurate measure of fluid accumulation.In this study, we review previously proposed predictive algorithms using body weight and noninvasive transthoracic bio-impedance (NITTI) to predict HF decompensations.We monitored 91 patients with chronic HF for an average of 10 months using a weight scale and a wearable bio-impedance vest. Three algorithms were tested using either simple rule-of-thumb differences (RoT), moving averages (MACD), or cumulative sums (CUSUM).Algorithms using NITTI in the 2 weeks preceding decompensation predicted events (P<.001); however, using weight alone did not. Cross-validation showed that NITTI improved sensitivity of all algorithms tested and that trend algorithms provided the best performance for either measurement (Weight-MACD: 33%, NITTI-CUSUM: 60%) in contrast to the simpler rules-of-thumb (Weight-RoT: 20%, NITTI-RoT: 33%) as proposed in HF guidelines.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

A method to adapt thoracic impedance based on chest geometry and composition to assess congestion in heart failure patients

Multi-frequency trans-thoracic bioimpedance (TTI) could be used to track fluid changes and congestion of the lungs, however, patient specific characteristics may impact the measurements. We investigated the effects of thoracic geometry and composition on measurements of TTI and developed an equation to calculate a personalized fluid index. Simulations of TTI measurements for varying levels of chest circumference, fat and muscle proportion were used to derive parameters for a model predicting expected values of TTI. This model was then adapted to measurements from a control group of 36 healthy volunteers to predict TTI and lung fluids (fluid index). Twenty heart failure (HF) patients treated for acute HF were then used to compare the changes in the personalized fluid index to symptoms of HF and predicted TTI to measurements at hospital discharge. All the derived body characteristics affected the TTI measurements in healthy volunteers and together the model predicted the measured TTI with 8.9% mean absolute error. In HF patients the estimated TTI correlated well with the discharged TTI (r = 0.73, p <0.001) and the personalized fluid index followed changes in symptom levels during treatment. However, 37% (n= 7) of the patients were discharged well below the model expected value. Accounting for chest geometry and composition might help in interpreting TTI measurements

Removing respiratory artefacts from transthoracic bioimpedance spectroscopy measurements

Transthoracic impedance spectroscopy (TIS) measurements from wearable textile electrodes provide a tool to remotely and non-invasively monitor patient health. However, breathing and cardiac processes inevitably affect TIS measurements, since they are sensitive to changes in geometry and air or fluid volumes in the thorax. This study aimed at investigating the effect of respiration on Cole parameters extracted from TIS measurements and developing a method to suppress artifacts. TIS data were collected from 10 participants at 16 frequencies (range: 10 kHz - 1 MHz) using a textile electrode system (Philips Technologie Gmbh). Simultaneously, breathing volumes and frequency were logged using an electronic spirometer augmented with data from a breathing belt. The effect of respiration on TIS measurements was studied at paced (10 and 16 bpm) deep and shallow breathing. These measurements were repeated for each subject in three different postures (lying down, reclining and sitting). Cole parameter estimation was improved by assessing the tidal expiration point thus removing breathing artifacts. This leads to lower intra-subject variability between sessions and a need for less measurements points to accurately assess the spectra. Future work should explore algorithmic artifacts compensation models using breathing and posture or patient contextual information to improve ambulatory transthoracic impedance measurements