Bioimpedance and New‐Onset Heart Failure: A Longitudinal Study of &gt;500 000 Individuals From the General Population

Lindholm, Daniel; Fukaya, Eri; Leeper, Nicholas J.; Ingelsson, Erik

doi:10.1161/jaha.118.008970

Cited by 32 publications

(13 citation statements)

References 22 publications

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“…7,28,31 Bioimpedance is defined as the ability of tissue to impede electric current, and is related to the amount of fluid accumulation in body tissue. 36 Combined with the recent finding that leg bioimpedance is associated with risk for heart failure 37 , these novel predictors suggest a link between high-volume venous reflux, increased hydrostatic pressure, and resulting venous hypertension.…”

Section: Discussionmentioning

confidence: 94%

Clinical and Genetic Determinants of Varicose Veins

et al. 2018

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Background: Varicose veins are a common problem with no approved medical therapies. While it is believed that varicose vein pathogenesis is multifactorial, there is a limited understanding of the genetic and environmental factors that contribute to their formation. Large-scale studies of risk factors for varicose veins may highlight important aspects of pathophysiology and identify groups at increased risk for disease. Methods: We applied machine learning to agnostically search for risk factors of varicose veins in 493,519 individuals in the UK Biobank. Predictors were further studied using univariable and multivariable Cox regression analysis (2,441 incident events). A genome-wide association study (GWAS) of varicose veins was also performed among 337,536 unrelated individuals (9,577 cases) of white British descent, followed by eQTL and pathway analyses. Because height emerged as a new candidate risk factor, we performed Mendelian randomization analyses to assess a potential causal role for height in varicose vein development. Results: Machine learning confirmed several known (age, sex, obesity, pregnancy, history of deep vein thrombosis) and identified several new risk factors for varicose vein disease, including height. After adjusting for traditional risk factors in Cox regression, greater height remained independently associated with varicose veins (HR for upper vs. lower quartile: 1·74; 95% CI: 1·51–2·01, P<0·0001). A GWAS identified 30 new genome-wide significant loci, identifying pathways involved in vascular development and skeletal/limb biology. Mendelian randomization analysis provided evidence that increased height is causally related to varicose veins (Inverse-variance weighted: OR=1·26; P=2·07×10−16). Conclusions: Using data from nearly half a million individuals, we present a comprehensive genetic and epidemiological study of varicose veins. We identified novel clinical and genetic risk factors which provide pathophysiological insights and could help future improvements of treatment of varicose vein disease.

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Section: Discussionmentioning

confidence: 94%

Clinical and Genetic Determinants of Varicose Veins

et al. 2018

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“…In patients with HF, use of the Edema Guard Monitor (CardioSet Medical) combined with a symptom diary was associated with increases in self-behaviour score for 30 days after hospital discharge 49 . In an analysis of more than 500,000 individuals in the UK Biobank, a machine learning model revealed that leg bioimpedance was inversely associated with HF incidence 50 . Numerous innovative and non-invasive tools can be used to detect leg impedance, such as sock-based sensors 51 .…”

Section: Innovative Biosensors For Hf Detectionmentioning

confidence: 99%

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

et al. 2020

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Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. T he se tools are increasingly recognized by regulatory a g e n c ies a n d p r o f e s s ional m e d ic al s oc ie ti es, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

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“…We also demonstrated that a predictive algorithm analyzing daily bioimpedance measures achieved good performance for predicting recurrent acute decompensated heart failure [ 12 ]. Lindholm et al [ 22 ] also performed a longitudinal study including over 500,000 participants and determined that leg bioimpedance was inversely correlated with new-onset heart failure and that by combining the leg bioimpedance with clinical parameters such as age, sex, and history of myocardial infarction, accurate prediction of heart failure could be achieved. In another study [ 23 ] on participants with congenital heart disease, bioelectrical impedance correlated with heart failure severity.…”

Section: Resultsmentioning

confidence: 99%

“…Our group has shown that transthoracic bioimpedance can be measured daily with fluid accumulation vests using conventional electrodes, and a predictive algorithm analyzing daily bioimpedance parameters showed reasonable performance in predicting recurrent heart failure events, including hospitalization, diuretic uptitration, and worsening heart failure symptoms [ 12 ]. Lindholm et al [ 22 ] determined that leg bioimpedance was inversely correlated with heart failure incidence, and by combining leg bioimpedance with demographic information, they obtained accurate heart failure predictions. Sato et al [ 23 ] evaluated parameters from bioelectrical impedance analysis in participants with congenital heart disease and determined that the edema index obtained from bioelectrical impedance analysis could also be a marker for heart failure severity.…”

Section: Introductionmentioning

confidence: 99%

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

Reljin¹,

Posada-Quintero²,

Eaton-Robb³

et al. 2020

JMIR Med Inform

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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.

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Bioimpedance and New‐Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population

Cited by 32 publications

References 22 publications

Clinical and Genetic Determinants of Varicose Veins

Clinical and Genetic Determinants of Varicose Veins

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

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

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