Detection of Needle Dislodgement Using Extracorporeal Pressure Signals: A Feasibility Study.

Venous needle dislodgement (VND) during dialysis is a rarely occurring adverse event, which becomes life-threatening if not handled promptly. Because the standard venous pressure alarm, implemented in most dialysis machines, has low sensitivity, a novel approach using extracted cardiac information to detect needle dislodgement is proposed. Four features are extracted from the arterial and venous pressure signals of the dialysis machine, characterizing the mean venous pressure, the venous cardiac pulse pressure, the time delay, and the correlation between the two pressure signals. The features serve as input to a support vector machine (SVM), which determines whether dislodgement has occurred. The SVM is first trained on a set of laboratory data, and then tested on another set of laboratory data as well as on a small data set from clinical hemodialysis sessions. The results show that dislodgement can be detected after 12-17 s, corresponding to 24-143 ml blood loss. The standard venous pressure alarm used in clinical routine only detects 50% of the VNDs, whereas the novel method detects all VNDs and has a false alarm rate of 0.12 per hour, provided that the amplitude of the extracted cardiac pressure signal exceeds 1 mmHg. The results are promising; however, the method needs to be tested on a larger set of clinical data to better establish its performance.

A New Method for Continuous Relative Blood Volume and Plasma Sodium Concentration Estimation During Hemodialysis.

OBJECTIVE: Non-invasive sensing and reliable estimation of physiological parameters are important features of hemodialysis machines, especially for therapy customization (biofeedback). In this paper, we present a new method for joint estimation of two important hemodialysis-related physiological parameters-relative blood volume and plasma sodium concentration. METHODS: Our method makes use of a non-invasive sensor setup and a mathematical estimator. The estimator, based on the Kalman filter, allows merging data from multiple sensors, newly designed as well as onboard, with modeling knowledge about the hemodialysis process. The system was validated on in vitro hemodialysis sessions using bovine blood. RESULTS: The estimation error we obtained (0.97 ± 0.73% on relative blood volume and 0.47 ± 0.19 mM on plasmatic sodium) proved to be comparable with that of the reference data for both parameters-the system is sufficiently accurate to be relevant in a clinical context. CONCLUSION: Our system has the potential to provide accurate and important information on the state of a patient undergoing hemodialysis, while only low-cost modifications to the existing onboard sensors are required. SIGNIFICANCE: Through improved knowledge of blood parameters during hemodialysis, our method will allow better patient monitoring and therapy customization in hemodialysis.

Monitoring respiration using the pressure sensors in a dialysis machine.

OBJECTIVE: Although respiratory problems are common among patients with end-stage renal disease, respiration is not continuously monitored during dialysis. The purpose of the present study is to investigate the feasibility of monitoring respiration using the pressure sensors of the dialysis machine. APPROACH: Respiration induces variations in the blood pressure that propagates to the extracorporeal circuit of the dialysis machine. However, the magnitude of these variations are very small compared to pressure variations induced by the dialysis machine. We propose a new method, which involves adaptive template subtraction and peak conditioned spectral averaging, to estimate respiration rate from the pressure sensor signals. Using this method, an estimate of the respiration rate is obtained every 5th second provided that the signal quality is sufficient. The method is evaluated for continuous monitoring of respiration rate in nine dialysis treatment sessions. MAIN RESULTS: The median absolute deviation between the estimated respiration rate from the pressure sensor signals and a reference capnography recording was 0.02 Hz (1.3 breaths per min). SIGNIFICANCE: Our results suggest that continuous monitoring of respiration using the pressure sensors of the dialysis machine is feasible. The main advantage with such monitoring is that no additional sensors are required which may cause patient discomfort.

Detection of ventricular premature beats based on the pressure signals of a hemodialysis machine.

Monitoring of ventricular premature beats (VPBs), being abundant in hemodialysis patients, can provide information on cardiovascular instability and electrolyte imbalance. In this paper, we describe a method for VPB detection which explores the signals acquired from the arterial and the venous pressure sensors, located in the extracorporeal blood circuit of a hemodialysis machine. The pressure signals are mainly composed of a pump component and a cardiac component. The cardiac component, severely overshadowed by the pump component, is estimated from the pressure signals using an earlier described iterative method. A set of simple features is extracted, and linear discriminant analysis is performed to classify beats as either normal or ventricular premature. Performance is evaluated on signals from nine hemodialysis treatments, using leave-one-out crossvalidation. The simultaneously recorded and annotated photoplethysmographic signal serves as the reference signal, with a total of 149,686 normal beats and 3574 VPBs. The results show that VPBs can be reliably detected, quantified by a Youden's J statistic of 0.9, for average cardiac pulse pressures exceeding 1 mmHg; for lower pressures, the J statistic drops to 0.55. It is concluded that the cardiac pressure signal is suitable for VPB detection, provided that the average cardiac pulse pressure exceeds 1 mmHg.

Extracting a cardiac signal from the extracorporeal pressure sensors of a hemodialysis machine.

Although patients undergoing hemodialysis treatment often suffer from cardiovascular disease, monitoring of cardiac rhythm is not performed on a routine basis. Without requiring any extra sensor, this study proposes a method for extracting a cardiac signal from the built-in extracorporeal venous pressure sensor of the hemodialysis machine. The extraction is challenged by the fact that the cardiac component is much weaker than the pressure component caused by the peristaltic blood pump. To further complicate the extraction problem, the cardiac component is difficult to separate when the pump and heart rates coincide. The proposed method estimates a cardiac signal by subtracting an iteratively refined blood pump model signal from the signal measured at the extracorporeal venous pressure sensor. The method was developed based on simulated pressure signals, and evaluated on clinical pressure signals acquired during hemodialysis treatment. The heart rate estimated from the clinical pressure signal was compared to that derived from a photoplethysmographic reference signal, resulting in a difference of 0.07 ± 0.84 beats/min. The accuracy of the heartbeat occurrence times was studied for different strengths of the cardiac component, using both clinical and simulated signals. The results suggest that the accuracy is sufficient for analysis of heart rate and certain arrhythmias.