Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study.

BACKGROUND: Accumulation of excess body fluid and autonomic dysregulation are clinically important characteristics of acute decompensated heart failure. We hypothesized that transthoracic bioimpedance, a noninvasive, simple method for measuring fluid retention in lungs, and heart rate variability, an assessment of autonomic function, can be used for detection of fluid accumulation in patients with acute decompensated heart failure. OBJECTIVE: We aimed to evaluate the performance of transthoracic bioimpedance and heart rate variability parameters obtained using a fluid accumulation vest with carbon black-polydimethylsiloxane dry electrodes in a prospective clinical study (System for Heart Failure Identification Using an External Lung Fluid Device; SHIELD). METHODS: We computed 15 parameters: 8 were calculated from the model to fit Cole-Cole plots from transthoracic bioimpedance measurements (extracellular, intracellular, intracellular-extracellular difference, and intracellular-extracellular parallel circuit resistances as well as fitting error, resonance frequency, tissue heterogeneity, and cellular membrane capacitance), and 7 were based on linear (mean heart rate, low-frequency components of heart rate variability, high-frequency components of heart rate variability, normalized low-frequency components of heart rate variability, normalized high-frequency components of heart rate variability) and nonlinear (principal dynamic mode index of sympathetic function, and principal dynamic mode index of parasympathetic function) analysis of heart rate variability. We compared the values of these parameters between 3 participant data sets: control (n=32, patients who did not have heart failure), baseline (n=23, patients with acute decompensated heart failure taken at the time of admittance to the hospital), and discharge (n=17, patients with acute decompensated heart failure taken at the time of discharge from hospital). We used several machine learning approaches to classify participants with fluid accumulation (baseline) and without fluid accumulation (control and discharge), termed with fluid and without fluid groups, respectively. RESULTS: Among the 15 parameters, 3 transthoracic bioimpedance (extracellular resistance, R0; difference in extracellular-intracellular resistance, R0 - R∞, and tissue heterogeneity, α) and 3 heart rate variability (high-frequency, normalized low-frequency, and normalized high-frequency components) parameters were found to be the most discriminatory between groups (patients with and patients without heart failure). R0 and R0 - R∞ had significantly lower values for patients with heart failure than for those without heart failure (R0: P=.006; R0 - R∞: P=.001), indicating that a higher volume of fluids accumulated in the lungs of patients with heart failure. A cubic support vector machine model using the 5 parameters achieved an accuracy of 92% for with fluid and without fluid group classification. The transthoracic bioimpedance parameters were related to intra- and extracellular fluid, whereas the heart rate variability parameters were mostly related to sympathetic activation. CONCLUSIONS: This is useful, for instance, for an in-home diagnostic wearable to detect fluid accumulation. Results suggest that fluid accumulation, and subsequently acute decompensated heart failure detection, could be performed using transthoracic bioimpedance and heart rate variability measurements acquired with a wearable vest.

Patient Preferences for Point-of-Care Testing: Survey Validation and Results.

BACKGROUND: Studies of current opinion of our community members for the characteristics, mode, and location of use, use cases, and overall enthusiasm for point-of-care testing (POCT) diagnosis and management tools are needed. STUDY DESIGN AND METHODS: Qualitative research methods were used to develop, refine, and evaluate hardcopy and electronic versions of a 45-item English language survey. The accuracy of the instrument was measured by recorded structured interview, and its precision was measured by comparison to its administration to a group of uncompensated volunteers. MAIN FINDINGS AND RESULTS: Comparison of survey and structured interview data demonstrated high levels of accuracy. Highly concordant with significant levels of correlation and of direct association indicated favorable precision. Ninety-three percent of respondents believed that POCT could improve their care, and 56% identified having a POCT in their home as a top priority. Accuracy, insurance coverage, immediacy of results, and ease of use were identified as the most important characteristics of a POCT. CONCLUSIONS: Community members strongly support the development of accurate, in-home devices that produce immediate results that can be used to diagnose, manage, and encourage their adherence to treatments for their medical conditions.

Point-of-care technologies in heart, lung, blood and sleep disorders from the Center for Advancing Point-of-Care Technologies.

Recent advancements in point-of-care technologies have transformed care for patients with heart, lung, blood, and sleep disorders by providing rapid, cost-effective, and accessible solutions to challenges in the detection and management of many health conditions. However, major barriers exist throughout the technology development process that inhibit the actualization of many promising and potentially successful ideas. The Center for Advancing Point of Care Technologies has established a system for supporting further innovation in this field and bridging the gap between initial idea conception and implementation. We highlight current and emerging point-of-care technologies throughout the development spectrum and emphasize the need for a needs-driven model of health technology development that involve appropriate stakeholders in the process.