Electrosurgery and Temperature Increase in Tissue With a Passive Metal Implant

Importance: During monopolar electrosurgery in patients, current paths can be influenced by metal implants, which can cause unintentional tissue heating in proximity to implants. Guidelines concerning electrosurgery and active implants such as pacemakers or implantable cardioverter defibrillators have been published, but most describe interference between electrosurgery and the active implant rather than the risk of unintended tissue heating. Tissue heating in proximity to implants during electrosurgery may cause an increased risk of patient injury.Objective: To determine the temperature of tissue close to metal implants during electrosurgery in an in-vitro model.Design, Setting, and Participants: Thirty tissue samples (15 with a metal implant placed in center, 15 controls without implant) were placed in an in vitro measurement chamber. Electrosurgery was applied at 5–60 W with the active electrode at three defined distances from the implant while temperatures at four defined distances from the implant were measured using fiber-optic sensors.Main Outcomes and Measures: Tissue temperature increase at the four tissue sites was determined for all power levels and each of the electrode-to-implant distances. Based on a linear mixed effects model analysis, the primary outcomes were the difference in temperature increase between implant and control tissue, and the estimated temperature increase per watt per minute.Results: Tissues with an implant had higher temperature increases than controls at all power levels after 1 min of applied electrosurgery (mean difference of 0.16°C at 5 W, 0.50°C at 15 W, 1.11°C at 30 W, and 2.22°C at 60 W, all with p < 0.001). Temperature increase close to the implant was estimated to be 0.088°C/W/min (95% CI: 0.078–0.099°C/W/min; p < 0.001). Temperature could increase to above 43°C after 1 min of 60 W. Active electrode position had no significant effect on temperature increases for tissues with implant (p = 0.6).Conclusions and Relevance: The temperature of tissue close to a metal implant increases with passing electrosurgery current. There is a significant risk of high tissue temperature when long activation times or high power levels are used

Optically isolated current source

There is a need for isolated current sources for use in selected bio- impedance measurement circuits. The requirement for good isolation is particularly important in medical settings because of safety concerns. A new circuit for producing voltage-controlled current is presented. Measurements have been made on a prototype and simulations have been done on a SPICE model. The presented circuit is an H-bridge where the output devices are the output photodiodes of high-linearity optocouplers. Five operational amplifiers, four high linearity optocouplers, and passive components are used. Output current capability is ±35 μA with an output impedance that is more than 1 MΩ. It is possible to achieve bandwidths above 1 MHz for small load impedances. This circuit is well suited for medical applications thanks to the isolation in the optocouplers

Age-related differences in the morphology of the impedance cardiography signal

Impedance cardiography (ICG) is a non-invasive method of hemodynamic measurement, mostly known for estimation of stroke volume and cardiac output based on characteristic features of the signal. Compared with electrocardiography, the knowledge on the morphology of the ICG signal is scarce, especially with respect to age-dependent changes in ICG waveforms. Based on recordings from ten younger (20–29 years) and ten older (60–79) healthy human subjects after three different levels of physical activity, the typical interbeat ICG waveforms were derived based on ensemble averages. Comparison of these waveforms between the age groups indicates the following differences: a later initial upward deflection for the younger group, an additional hump in the waveform from many older subjects not presented in the younger group, and a more pronounced second wave in the younger group. The explanation for these differences is not clear, but may be related to arterial stiffness. Further studies are suggested to determine whether these morphological differences have clinical value

Assessing ischemic injury in human intestine ex vivo with electrical impedance spectroscopy

Abstract Electrical impedance spectroscopy is a well-established tool for monitoring changes in the electrical properties of tissue. Most tissue and organ types have been investigated in various studies. As for the small intestine, there are several published studies conducted on pig and rat models. This study investigates the changes in passive electrical properties of the complete wall of the human intestine non-invasively during ischemia. We aim to use the passive electrical properties to assess intestinal viability. The bioimpedance measurements were performed using a two-electrode set-up with a Solartron 1260 Impedance/gain-phase analyser. The small intestinal samples were resected from patients who underwent pancreaticoduodenectomy. Impedance measurements were conducted following resection by placing the electrodes on the surface of the intestine. A voltage was applied across the intestinal sample and the measured electrical impedance was obtained in the ZPlot software. Impedance data were further fitted into a Cole model to obtain the Cole parameters. The P y value was calculated from the extracted Cole parameters and used to assess the cell membrane integrity, thus evaluate the intestinal viability. Eight small intestinal segments from different patients were used in this study and impedance measurements were performed once an hour for a ten-hour period. One hour after resection, the impedance decreased, then increased the next two hours, before decreasing until the end of the experiment. For all the intestinal segments, the P y values first increased and reached a plateau which lasted for 1 - 2 hours, before it decreased irreversibly. The time interval where P y value reached the maximum is consistent with reported viable/non-viable limits from histological analysis

Automatic prediction of ischemia-reperfusion injury of small intestine using convolutional neural networks: A pilot study

Acute intestinal ischemia is a life-threatening condition. The current gold standard, with evaluation based on visual and tactile sensation, has low specificity. In this study, we explore the feasibility of using machine learning models on images of the intestine, to assess small intestinal viability. A digital microscope was used to acquire images of the jejunum in 10 pigs. Ischemic segments were created by local clamping (approximately 30 cm in width) of small arteries and veins in the mesentery and reperfusion was initiated by releasing the clamps. A series of images were acquired once an hour on the surface of each of the segments. The convolutional neural network (CNN) has previously been used to classify medical images, while knowledge is lacking whether CNNs have potential to classify ischemia-reperfusion injury on the small intestine. We compared how different deep learning models perform for this task. Moreover, the Shapley additive explanations (SHAP) method within explainable artificial intelligence (AI) was used to identify features that the model utilizes as important in classification of different ischemic injury degrees. To be able to assess to what extent we can trust our deep learning model decisions is critical in a clinical setting. A probabilistic model Bayesian CNN was implemented to estimate the model uncertainty which provides a confidence measure of our model decisions

Machine learning for intraoperative prediction of viability in ischemic small intestine

Objective: Evaluation of intestinal viability is essential in surgical decision-making in patients with acute intestinal ischemia. There has been no substantial change in the mortality rate (30-93%) of patients with acute mesenteric ischemia (AMI) since the 1980’s. As the accuracy from the first laparotomy alone is 50%, the gold standard is a second-look laparotomy, increasing the accuracy to 87-89%. This study investigates the use of machine learning to classify intestinal viability and histological grading in pig jejunum, based on multivariate time-series of bioimpedance sensor data. Approach: We have previously used a bioimpedance sensor system to acquire electrical parameters from perfused, ischemic and reperfused pig jejunum (7 + 15 pigs) over 1-16 hours of ischemia and 1-8 hours of reperfusion following selected durations of ischemia. In this study we compare the accuracy of using end-point bioimpedance measurements with a feedforward neural network (FNN), versus the accuracy when using a recurrent neural network with long short-term memory units (LSTM-RNN) with bioimpedance data history over different periods of time. Main results: Accuracies in the range of what has been reported clinically can be achieved using FNN’s on a single bioimpedance measurement, and higher accuracies can be achieved when employing LSTM-RNN on a sequence of data history. Significance: Intraoperative bioimpedance measurements on intestine of suspect viability combined with machine learning can increase the accuracy of intraoperative assessment of intestinal viability. Increased accuracy in intraoperative assessment of intestinal viability has the potential to reduce the high mortality and morbidity rate of the patients

Small intestinal ischemia and reperfusion - Bioimpedance measurements

Objective: Trans-intestinal bioimpedance measurements have previously been used to investigate changes in electrical parameters during 6h of ischemia in the small intestine. Knowledge is lacking regarding the time course of trans-intestinal bioimpedance parameters during reperfusion. As reperfusion is an important part in the clinical treatment of intestinal ischemia, we need to know how it affects the bioimpedance measurements. Approach: We performed bioimpedance measurements, using a two-electrode setup on selected segments of the jejunum in 15 pigs. A controlled voltage signal was applied while measuring the resulting current. In each pig, five or six 30 cm segments of the jejunum were made ischemic by clamping the mesenteric arteries and veins creating segments with ischemia from 1–16h duration. Reperfusion was initiated at selected time intervals of ischemia, and measured for 5–15h afterwards. Main results: The tan δ parameter (loss tangent) was different (p < 0.016) comparing ischemic and control tissue for the duration of the experiment (16h). Comparing the control tissue 30 cm from the ischemic area with the control tissue 60 cm from the ischemic tissue, we found that the mean tan δ amplitude in the frequency range (3900–6300 Hz) was significantly higher (p < 0.036) in the proximal control after 10h of experiment duration. After reperfusion, the time development of tanδm (loss tangent maximum over a frequency range) amplitude and frequency overlapped and periodically increased above the tanδm in the ischemic intestine. Dependent on the ischemic duration pre-reperfusion, the initial increase in tan δ stabilizes or increases drastically over time, compared to the tan δ amplitude of the ischemic tissue. Significance: As during ischemia, the electrical parameters during reperfusion also follow a characteristic timecourse, depending on the ischemic exposure before pre-reperfusion. The temporal changes in electrical parameters during small intestinal ischemia followed by reperfusion provides important information for assessment of tissue injury

Utilization of dielectric properties for assessment of liver ischemia-reperfusion injury in vivo and during machine perfusion

Abstract There is a shortage of donor livers and patients consequently die on waiting lists worldwide. Livers are discarded if they are clinically judged to have a high risk of non-function following transplantation. With the aim of extending the pool of available donor livers, we assessed the condition of porcine livers by monitoring the microwave dielectric properties. A total of 21 livers were divided into three groups: control with no injury (CON), biliary injury by hepatic artery occlusion (AHEP), and overall hepatic injury by static cold storage (SCS). All were monitored for four hours in vivo, followed by ex vivo plurithermic machine perfusion (PMP). Permittivity data was modeled with a two-pole Cole–Cole equation, and dielectric properties from one-hour intervals were analyzed during in vivo and normothermic machine perfusion (NMP). A clear increasing trend in the conductivity was observed in vivo in the AHEP livers compared to the control livers. After four hours of NMP, separations in the conductivity were observed between the three groups. Our results indicate that dielectric relaxation spectroscopy (DRS) can be used to detect and differentiate liver injuries, opening for a standardized and reliable point of evaluation for livers prior to transplantation

A multiparameter model for non-invasive detection of hypoglycemia

Objective: Severe hypoglycemia is the most serious acute complication for people with type 1 diabetes (T1D). Approximately 25% of people with T1D have impaired ability to recognize impending hypoglycemia, and nocturnal episodes are feared. Approach: We have investigated the use of non-invasive sensors for detection of hypoglycemia based on a mathematical model which combines several sensor measurements to identify physiological responses to hypoglycemia. Data from randomized single-blinded euglycemic and hypoglycemic glucose clamps in 20 participants with T1D and impaired awareness of hypoglycemia was used in the analyses. Main results: Using a sensor combination of sudomotor activity at three skin sites, ECG-derived heart rate and heart rate corrected QT interval, near-infrared and bioimpedance spectroscopy; physiological responses associated with hypoglycemia could be identified with an F1 score accuracy up to 88%. Significance: We present a novel model for identification of non-invasively measurable physiological responses related to hypoglycemia, showing potential for detection of moderate hypoglycemia using a wearable sensor system