A Feasibility Study of Non-Invasive Continuous Estimation of Brachial Pressure Derived from Arterial and Venous Lines During Dialysis

Stewart, J R; Stewart, Paul; Walker, Thomas; Eldehini, Tarek; Hörner, Daniela Viramontes; Lucas, Bethany; White, K. Preston; Muggleton, Andy; Selby, Nicholas M; Taal, Martin W

doi:10.31224/osf.io/xpv65

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…HRV signals can be highly dynamic and are further complicated by the effects of haemodialysis [ 14 ]. This can lead to difficulty in matching sampling rates or processing techniques to the signal, with the result that it is nearly impossible to establish which features of the PSD estimate are real and which are artefacts.…”

Section: Resultsmentioning

confidence: 99%

“…This study application is the iTrend (Intelligent Technologies for Renal Dialysis) programme, a long-term collaborative project conducted by a multidisciplinary research team from the Universities of Derby and Nottingham and the Royal Derby Hospital Renal Unit in the UK. The primary goal of the programme is to develop supporting technologies to enable personalised treatment in ESKD [ 14 ]. Adult participants were recruited from the Renal Unit's prevalent dialysis population and received continuous noninvasive monitoring of heart rate via ECG and haemodynamic parameters using pulse wave analysis (Finapres NOVA) during entire dialysis treatments.…”

Section: Introductionmentioning

confidence: 99%

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Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

Stewart

Walker

et al. 2020

Journal of Healthcare Engineering

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Short-term cardiovascular compensatory responses to perturbations in the circulatory system caused by haemodialysis can be investigated by the spectral analysis of heart rate variability, thus providing an important variable for categorising individual patients’ response, leading to a more personalised treatment. This is typically accomplished by resampling the irregular heart rate to generate an equidistant time series prior to spectral analysis, but resampling can further distort the data series whose interpretation can already be compromised by the presence of artefacts. The Lomb–Scargle periodogram provides a more direct method of spectral analysis as this method is specifically designed for large, irregularly sampled, and noisy datasets such as those obtained in clinical settings. However, guidelines for preprocessing patient data have been established in combination with equidistant time-series methods and their validity when used in combination with the Lomb–Scargle approach is missing from literature. This paper examines the effect of common preprocessing methods on the Lomb–Scargle power spectral density estimate using both real and synthetic heart rate data and will show that many common techniques for identifying and editing suspect data points, particularly interpolation and replacement, will distort the resulting power spectrum potentially misleading clinical interpretations of the results. Other methods are proposed and evaluated for use with the Lomb–Scargle approach leading to the main finding that suspicious data points should be excluded rather than edited, and where required, denoising of the heart rate signal can be reliably accomplished by empirical mode decomposition. Some additional methods were found to be particularly helpful when used in conjunction with the Lomb–Scargle periodogram, such as the use of a false alarm probability metric to establish whether spectral estimates are valid and help automate the assessment of valid heart rate records, potentially leading to greater use of this powerful technique in a clinical setting.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

Stewart

Walker

et al. 2020

Journal of Healthcare Engineering

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“…This may ultimately lead to more individualized approaches to prevent or manage IDH. We speculate that future interventions to reduce IDH could be targeted based on individual EP frequency profile, underpinned by ongoing work to develop more clinically relevant approaches for continuous BP measurement [37, 38]. For example, interventions which improve the vascular tone like cool dialysis might be more effective in HF group to improve haemodynamic stability on HD.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of Frequency of Continuous Blood Pressure Variation and Haemodynamic Responses during Haemodialysis

et al. 2021

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Background: Higher beat-to-beat blood pressure (BP) variation during haemodialysis (HD) has been shown to be associated with elevated cardiac damage markers and white matter ischaemic changes in the brain suggesting relevance to end-organ perfusion. We aimed to characterize individual patterns of BP variation and associated haemodynamic responses to HD. Methods: Fifty participants underwent continuous non-invasive haemodynamic monitoring during HD and BP variation were assessed using extrema point (EP) frequency analysis. Participants were divided into those with a greater proportion of low frequency (LF, n = 21) and high frequency (HF, n = 22) of BP variation. Clinical and haemodynamic data were compared between groups. Results: Median EP frequencies for mean arterial pressure (MAP) of mid-week HD sessions were 0.54 Hz (interquartile range 0.18) and correlated with dialysis vintage (r = 0.32, p = 0.039), NT pro-BNP levels (r = 0.32, p = 0.038), and average real variability (ARV) of systolic BP (r = 0.33, p = 0.029), ARV of diastolic BP (r = 0.46, p = 0.002), and ARV of MAP (r = 0.57, p < 0.001). In the LF group, MAP positively correlated with cardiac power index (CPI) in each hour of dialysis, but not with total peripheral resistance index (TPRI). In contrast, in the HF group, MAP correlated with TPRI in each hour of dialysis but only with CPI in the first hour. Conclusions: EP frequency analysis of continuous BP monitoring during dialysis allows assessment of BP variation and categorization of individuals into low- or high-frequency groups, which were characterized by different haemodynamic responses to dialysis. This may assist in improved individualization of dialysis therapy.

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“…The development of noninvasive methods for continuous intradialytic blood pressure monitoring will enhance early detection of intradialytic hypotension and may even facilitate prediction. 180 The use of advanced analytics and remote biosensor devices to support medical decision making may offer innovative steps regarding prescription and monitoring of personalized hemodialysis therapy. Personalized medicine based on mathematical models and artificial intelligence tools (neuronal networks, machine learning) combined with remote wearable biosensors may support physicians' decision making for individual patients regarding the selection of appropriate treatment modalities and technical options such as control of ultrafiltration rate and dialysate sodium and electrolytic concentrations.…”

Section: A Personalized Medicine Approachmentioning

confidence: 99%

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

Canaud

Kooman

Selby

et al. 2020

Kidney International Reports

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Hemodialysis has saved many lives, albeit with significant residual mortality. Although poor outcomes may reflect advanced age and comorbid conditions, hemodialysis per se may harm patients, contributing to morbidity and perhaps mortality. Systemic circulatory "stress" resulting from hemodialysis treatment schedule may act as a disease modifier, resulting in a multiorgan injury superimposed on preexistent comorbidities. New functional intradialytic imaging (i.e., echocardiography, cardiac magnetic resonance imaging [MRI]) and kinetic of specific cardiac biomarkers (i.e., Troponin I) have clearly documented this additional source of end-organ damage. In this context, several factors resulting from patienthemodialysis interaction and/or patient management have been identified. Intradialytic hypovolemia, hypotensive episodes, hypoxemia, solutes, and electrolyte fluxes as well as cardiac arrhythmias are among the contributing factors to systemic circulatory stress that are induced by hemodialysis. Additionally, these factors contribute to patients' symptom burden, impair cognitive function, and finally have a negative impact on patients' perception and quality of life. In this review, we summarize the adverse systemic effects of current intermittent hemodialysis therapy, their pathophysiologic consequences, review the evidence for interventions that are cardioprotective, and explore new approaches that may further reduce the systemic burden of hemodialysis. These include improved biocompatible materials, smart dialysis machines that automatically may control the fluxes of solutes and electrolytes, volume and hemodynamic control, health trackers, and potentially disruptive technologies facilitating a more personalized medicine approach.

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A Feasibility Study of Non-Invasive Continuous Estimation of Brachial Pressure Derived from Arterial and Venous Lines During Dialysis

Cited by 5 publications

References 9 publications

Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

Application of the Lomb-Scargle Periodogram to InvestigateHeart Rate Variability during Haemodialysis

An Analysis of Frequency of Continuous Blood Pressure Variation and Haemodynamic Responses during Haemodialysis

Dialysis-Induced Cardiovascular and Multiorgan Morbidity

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