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

ABSTRACT: 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, ?) 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.

SUBMITTER: Reljin N

PROVIDER: S-EPMC7484776 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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

Reljin Natasa N Posada-Quintero Hugo F HF Eaton-Robb Caitlin C Binici Sophia S Ensom Emily E Ding Eric E Hayes Anna A Riistama Jarno J Darling Chad C McManus David D Chon Ki H KH

JMIR medical informatics 20200827 8

<h4>Background</h4>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.<h4>Objective</h4>We aimed to evaluate the performance of ...[more]

PMID: 32852277

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Project description:BACKGROUND:Recurrent hospital admissions are common among patients admitted for acute decompensated heart failure (ADHF), but identification of patients at risk for rehospitalization remains challenging. Contemporary heart failure (HF) management programs have shown modest ability to reduce readmissions, partly because they monitor signs or symptoms of HF worsening that appear late during decompensation. Detecting early stages of HF decompensation might allow for immediate application of effective HF therapies, thereby potentially reducing HF readmissions. One of the earliest indicators of HF decompensation is intrathoracic fluid accumulation, which can be assessed using transthoracic bioimpedance. OBJECTIVE:The SENTINEL-HF study is a prospective observational study designed to test a novel, wearable HF monitoring system as a predictor of HF decompensation among patients discharged after hospitalization for ADHF. METHODS:SENTINEL-HF tests the hypothesis that a decline in transthoracic bioimpedance, as assessed daily with the Philips fluid accumulation vest (FAV) and transmitted using a mobile phone, is associated with HF worsening and rehospitalization. According to pre-specified power calculations, 180 patients admitted with ADHF are enrolled. Participants transmit daily self-assessments using the FAV-mobile phone dyad for 45 days post-discharge. The primary predictor is the deviation of transthoracic bioimpedance for 3 consecutive days from a patient-specific normal variability range. The ADHF detection algorithm is evaluated in relation with a composite outcome of HF readmission, diuretic up-titration, and self-reported HF worsening (Kansas City Cardiomyopathy Questionnaire) during a 90-day follow-up period. Here, we provide the details and rationale of SENTINEL-HF. RESULTS:Enrollment in the SENTINEL-HF study is complete and the 90-days follow-up is currently under way. Once data collection is complete, the study dataset will be used to evaluate our ADHF detection algorithm and the results submitted for publication. CONCLUSION:SENTINEL-HF emerged from our long-term vision that advanced home monitoring technology can improve the management of chronic HF by extending clinical care into patients' homes. Monitoring transthoracic bioimpedance with the FAV may identify patients at risk of recurrent HF decompensation and enable timely preventive measures. TRIAL REGISTRATION:Clinicaltrials.gov NCT01877369: https://clinicaltrials.gov/ct2/show/NCT01877369 (Archived by WebCite at http://www.webcitation.org/6bDYl0dGy).

Project description:BackgroundHeart rate variability (HRV) and pulse rate variability are indices of autonomic cardiac modulation. Increased pericardial fat is associated with worse cardiovascular outcomes. We hypothesized that progressive increases in pericardial fat volume and inflammation prospectively dampen HRV in hypercholesterolemic pigs.MethodsWT (wild type) or PCSK9 (proprotein convertase subtilisin-like/kexin type-9) gain-of-function Ossabaw mini-pigs were studied in vivo before and after 3 and 6 months of a normal diet (WT-normal diet, n=4; PCSK9-normal diet, n=6) or high-fat diet (HFD; WT-HFD, n=3; PCSK9-HFD, n=6). The arterial pulse waveform was obtained from an arterial telemetry transmitter to analyze HRV indices, including SD (SD of all pulse-to-pulse intervals over a single 5-minute period), root mean square of successive differences, proportion >50 ms of normal-to-normal R-R intervals, and the calculated ratio of low-to-high frequency distributions (low-frequency power/high-frequency power). Pericardial fat volumes were evaluated using multidetector computed tomography and its inflammation by gene expression of TNF (tumor necrosis factor)-α. Plasma lipid panel and norepinephrine level were also measured.ResultsAt diet completion, hypercholesterolemic PCSK9-HFD had significantly (P<0.05 versus baseline) depressed HRV (SD of all pulse-to-pulse intervals over a single 5-minute period, root mean square of successive differences, proportion >50 ms, high-frequency power, low-frequency power), and both HFD groups had higher sympathovagal balance (SD of all pulse-to-pulse intervals over a single 5-minute period/root mean square of successive differences, low-frequency power/high-frequency power) compared with normal diet. Pericardial fat volumes and LDL (low-density lipoprotein) cholesterol concentrations correlated inversely with HRV and directly with sympathovagal balance, while sympathovagal balance correlated directly with plasma norepinephrine. Pericardial fat TNF-α expression was upregulated in PCSK9-HFD, colocalized with nerve fibers, and correlated inversely with root mean square of successive differences and proportion >50 ms.ConclusionsProgressive pericardial fat expansion and inflammation are associated with a fall in HRV in Ossabaw mini-pigs, implying aggravated autonomic imbalance. Hence, pericardial fat accumulation is associated with alterations in HRV and the autonomic nervous system. Visual Overview: A visual overview is available for this article.

Project description:Heart rate variability (HRV) is the variation of cardiac inter-beat intervals over time resulting largely from the interplay between the sympathetic and parasympathetic branches of the autonomic nervous system. Individual differences in HRV are associated with emotion regulation, personality, psychopathology, cardiovascular health, and mortality. Previous studies have shown significant heritability of HRV measures. Here we extend genetic research on HRV by investigating sex differences in genetic underpinnings of HRV, the degree of genetic overlap among different measurement domains of HRV, and phenotypic and genetic relationships between HRV and the resting heart rate (HR). We performed electrocardiogram (ECG) recordings in a large population-representative sample of young adult twins (n=1060 individuals) and computed HRV measures from three domains: time, frequency, and nonlinear dynamics. Genetic and environmental influences on HRV measures were estimated using linear structural equation modeling of twin data. The results showed that variability of HRV and HR measures can be accounted for by additive genetic and non-shared environmental influences (AE model), with no evidence for significant shared environmental effects. Heritability estimates ranged from 47 to 64%, with little difference across HRV measurement domains. Genetic influences did not differ between genders for most variables except the square root of the mean squared differences between successive R-R intervals (RMSSD, higher heritability in males) and the ratio of low to high frequency power (LF/HF, distinct genetic factors operating in males and females). The results indicate high phenotypic and especially genetic correlations between HRV measures from different domains, suggesting that >90% of genetic influences are shared across measures. Finally, about 40% of genetic variance in HRV was shared with HR. In conclusion, both HR and HRV measures are highly heritable traits in the general population of young adults, with high degree of genetic overlap across different measurement domains.

Dataset Information

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

Publications

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

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