Accuracy of Tissue Oxygen Saturation Measurements of a Textile-Based NIRS Sensor.

Pressure injuries (PI) are dangerous tissue lesions that heal very slowly and pose a high risk of serious infections. They are caused by pressure applied to the tissue, which stops blood circulation and therefore induces hypoxia, i.e., low tissue oxygen saturation (StO2). PI cause severe suffering and are expensive to treat. Hence it is essential to prevent them with a device that detects a dangerous situation, e.g., by measuring StO2 using near-infrared spectroscopy (NIRS). For such a device to be wearable without causing PI, it must not introduce pressure points itself. This can be achieved by integrating optical fibers into a textile to transport light to and from the tissue.The aim of this paper is to investigate the accuracy of StO2 measurements using a NIRS device based only on textile-integrated optical fibers.Bundles of fibers were stitched into a textile in such a way that loops of <1 mm diameters were formed at the stitching locations. Detection points (DPs) on the fabric consisted of 8 fibers with 3 loops each. Emission points (EPs) were made from 4 fibers with 3 loops each. All fiber ends of a DP were connected to an avalanche photodiode. One end of each fiber belonging to an EP was connected to an LED (740 nm, 810 nm, or 880 nm; 290, 560, or 610 mW).To verify the accuracy of this textile-based sensor, we placed it on a subject's forearm and compared the derived StO2 during arterial occlusion to the values of a gold-standard NIRS device (ISS Imagent), which was placed on the forearm too.We found that our textile-based sensor repeatedly measured StO2 values over a range of 40% with a deviation of <10% from the reference device.By showing the ability to measure StO2 using textile-integrated optical fibers accurately, we have reached a significant milestone on our way to building a wearable device to monitor tissue health and prevent PI.

Feasibility to Measure Tissue Oxygen Saturation Using Textile-Integrated Polymer Optical Fibers.

Tissue oxygen saturation (StO2) is a crucial factor in the aetiology of pressure injury (PI), since hypoxia leads to necrotization. Pressure on the tissue occludes blood circulation and reduces the StO2, resulting in hypoxia. PI causes severe suffering, heals slowly and is expensive to treat. Hence it is important to prevent PI by detecting hypoxia, e.g., by near-infrared spectroscopy (NIRS) monitoring of StO2. For this, the NIRS device has to be wearable for a long time and it is crucial that it provokes no pressure itself. An integration of optical fibres into a textile achieves this. The aim was to investigate the feasibility of such a textile NIRS device.Knots and loops were tested as textile light emitters (LEs) or detectors (LDs) on a phantom. The light coupling efficiency of the LEs and LDs was investigated.Results show that knots perform similarly to loops. More loops per fibre increase efficiency both in LEs and in LDs. The best trade-off is at 3 loops. LEs are slightly more efficient than LDs, with an average attenuation from baseline of about -2 dB for loops of 0.5 mm diameter. Adding fibres multiplies the signal by the number of fibres. Inclusions mimicking hypoxia in phantoms were successfully identified. In-vivo arm occlusion tests showed the expected decrease in StO2. This shows feasibility of optical fibres in a textile to prevent PI.

Detection and quantification of overactive bladder activity in patients: Can we make it better and automatic?

AIMS: To explore the use of time-frequency analysis as an analytical tool to automatically detect pattern changes in bladder pressure recordings of patients with overactive bladder (OAB). To provide quantitative data on the bladder's non-voiding activity which could improve the current diagnosis and potentially the treatment of OAB. METHODS: We developed an algorithm, based on time-frequency analysis, to analyze bladder pressure during the filling phase of urodynamic studies. The algorithm was used to generate a bladder overactivity index (BOI) for a quantitative estimation of the average bladder non-voiding-activity. We tested the algorithm with one control group and two groups of patients with OAB symptoms: one group with detrusor overactivity (DO), assessed by an experienced urologist (OAB-with-DO group), and another group for which detrusor overactivity was not diagnosed (OAB-without-DO group). RESULTS: The algorithm identified diagnostically significant data on the bladder non-voiding activity in a specified frequency range. BOI was significantly higher for both OAB groups compared to the control group: the median value of BOI was twice as big in OAB-without-DO and more than four times higher in OAB-with-DO compared to control group. Moreover the algorithm was successfully tested to detect episodes of detrusor overactivity. CONCLUSIONS: We have shown that a simple algorithm, based on time-frequency analysis of bladder pressure, may be a promising tool in the clinical setting. The algorithm can provide quantitative data on non-voiding bladder activity in patients and quantify the changes according to phenotype. Moreover the algorithm can detect DO, showing potential for triggering conditional bladder stimulation.

Detectability of low-oxygenated regions in human muscle tissue using near-infrared spectroscopy and phantom models.

The present work aims to describe the detectability limits of hypoxic regions in human muscle under moderate thicknesses of adipose tissue to serve as a groundwork for the development of a wearable device to prevent pressure injuries. The optimal source-detector distances, detection limits, and the spatial resolution of hypoxic volumes in the human muscle are calculated using finite element method-based computer simulations conducted on 3-layer tissue models. Silicone phantoms matching the simulation geometries were manufactured, and their measurement results were compared to the simulations. The simulations showed good agreement with the performed experiments. Our results show detectability of hypoxic volumes under adipose tissue thicknesses of up to 1.5 cm. The maximum tissue depth, at which hypoxic volumes could be detected was 2.8 cm. The smallest detectable hypoxic volume in our study was 1.2 cm3. We thus show the detectability of hypoxic volumes in sizes consistent with those of early-stage pressure injury formation and, consequently, the feasibility of a device to prevent pressure injuries.

Angioarchitecture and hemodynamics of microvascular arterio-venous malformations.

INTRODUCTION: Arteriovenous malformations (AVMs) are characterized by pathological high flow, low resistance connections between arteries and veins. Treatment is critically dependent on correct interpretation of angioarchitectural features. However, some microfistular AVMs do not match the characteristics described in current AVM classification systems. Therefore, we propose a new subgroup of microfistular AVMs, composed of enlarged, fistulous paths on the venous half of capillaries and/or dilated draining venules (hyperdynamic, capillary-venulous malformation [CV-AVM]). CV-AVMs still ensure arterial flow to the periphery and fistulous venous drainage is less pronounced than in classical AVMs such that these lesions are often misinterpreted as venous malformations. MATERIALS AND METHODS: We developed a computational model to study the effects of microvascular anomalies on local hemodynamics, as well as their impact on angiographic contrast propagation. Flow rates and pressures were computed with a lumped parameter description, while contrast propagation was determined by solving the 1D advection-diffusion equation. RESULTS AND CONCLUSIONS: For the newly proposed CV-AVM angioarchitecture, the computational model predicts increased arterio-venous contrast agent transit times and highly dispersive transport characteristics, compared to microfistular, interstitial type IV AVMs and high flow type II and III AVMs. We related these findings to time-contrast intensity curves sampled from clinical angiographies and found that there is strong evidence for the existence of CV-AVM.