• A feasibility study of non-invasive continuous estimation of brachial pressure derived from arterial and venous lines during dialysis

      Stewart, Jill; Walker, Thomas; Eldehini, Tarek; Horner, Daniela Viramontes; Lucas, Bethany; White, Kelly; Muggleton, Andy; Selby, Nicholas M; Taal, Martin W; Stewart, Paul; et al. (IEEE, 2020)
      Intradialytic haemodynamic instability is a significant clinical problem, leading to end-organ ischaemia and contributing to morbidity and mortality in haemodialysis patients. Non-invasive continuous blood pressure monitoring is not part of routine practice but may aid detection and prevention of significant falls in blood pressure during dialysis. Brachial blood pressure is currently recorded intermittently during haemodialysis via a sphygmomanometer. Current methods of continuous non-invasive blood pressure monitoring tend to restrict movement, can be sensitive to external disturbances and patient movement, and can be uncomfortable for the wearer. Additionally, poor patient blood circulation can lead to unreliable measurements. In this study we performed an initial validation of a novel method and associated technology via a feasibility study to continuously estimate blood pressure using pressure sensors in the extra-corporeal dialysis circuit, which does not require any direct contact with the person receiving dialysis treatment.\\ The paper describes the development of the measurement system and subsequent \emph{in vivo} patient feasibility study with concurrent measurement validation by \emph{Finapres Nova} experimental physiological measurement device. We identify a mathematical function to describe the relationship between arterial line pressure and brachial artery BP, which is confirmed in the patient study. The methodology presented requires no interfacing to proprietery dialysis machine systems, no sensors to be attached to the patient directly, and to be robust to patient movement during treatment and also to the effects of the cyclical pressure waveforms induced by the hemodialysis pump. This represents a key enabling factor to the development of a practical continuous blood pressure monitoring device for dialysis patients.
    • An iterative run-to-run learning model to derive continuous brachial pressure estimates from arterial and venous lines during dialysis treatment

      Stewart, Jill; Stewart, Paul; Walker, Tom; Viramontes-Hörner, Daniela; Lucas, Bethany; White, Kelly; Taal, Maarten W.; Selby, Nicholas M.; Morris, Mel; University of Derby; et al. (Elsevier BV, 2020-11-28)
      Objective: Non-invasive continuous blood pressure monitoring is not yet part of routine practice in renal dialysis units but could be a valuable tool in the detection and prevention of significant variations in patient blood pressure during treatment. Feasibility studies have delivered an initial validation of a method which utilises pressure sensors in the extra-corporeal dialysis circuit, without any direct contact with the person receiving treatment. Our main objective is to further develop this novel methodology from its current early development status to a continuous-time brachial artery pressure estimator. Methods: During an in vivo patient feasibility study with concurrent measurement validation by Finapres Nova experimental physiological measurement device, real-time continuous dialysis line pressures, and intermittent occluding arm cuff pressure data were collected over the entire period of (typically 4-hour) dialysis treatments. There was found to be an underlying quasi-linear relationship between arterial line and brachial pressure measurements which supported the development of a mathematical function to describe the relationship between arterial dialysis line pressure and brachial artery BP. However, unmodelled non-linearities, dynamics and time-varying parameters present challenges to the development of an accurate BP estimation system. In this paper, we start to address the problem of physiological parameter time variance by novel application of an iterative learning run-to-run modelling methodology originally developed for process control engineering applications to a parameterised BP model. Results: The iterative run-to-run learning methodology was applied to the real-time data measured during an observational study in 9 patients, supporting subsequent development of an adaptive real-time BP estimator. Tracking of patient BP is analysed for all the subjects in our patient study, supported only by intermittent updates from BP cuff measurements. Conclusion: The methodology and associated technology is shown to be capable of tracking patient BP noninvasively via arterial line pressure measurement during complete 4-hour treatment sessions. A robust and tractable method is demonstrated, and future refinements to the approach are defined.