Heart rate monitoring to detect acute pain in non-verbal patients: a study protocol for a randomized controlled clinical trial.

BACKGROUND: Autism entails reduced communicative abilities. Approximately 30% of individuals with autism have intellectual disability (ID). Some people with autism and ID are virtually non-communicative and unable to notify their caregivers when they are in pain. In a pilot study, we showed that heart rate (HR) monitoring may identify painful situations in this patient group, as HR increases in acutely painful situations. OBJECTIVES: This study aims to generate knowledge to reduce the number of painful episodes in non-communicative patients' everyday lives. We will 1) assess the effectiveness of HR as a tool for identifying potentially painful care procedures, 2) test the effect of HR-informed changes in potentially painful care procedures on biomarkers of pain, and 3) assess how six weeks of communication through HR affects the quality of communication between patient and caregiver. METHODS: We will recruit 38 non-communicative patients with autism and ID residing in care homes. ASSESSMENTS: HR is measured continuously to identify acutely painful situations. HR variability and pain-related cytokines (MCP-1, IL-1RA, IL-8, TGFß1, and IL-17) are collected as measures of long-term pain. Caregivers will be asked to what degree they observe pain in their patients and how well they believe they understand their patient's expressions of emotion and pain. Pre-intervention: HR is measured 8 h/day over 2 weeks to identify potentially painful situations across four settings: physiotherapy, cast use, lifting, and personal hygiene. INTERVENTION: Changes in procedures for identified painful situations are in the form of changes in 1) physiotherapy techniques, 2) preparations for putting on casts, 3) lifting techniques or 4) personal hygiene procedures. DESIGN: Nineteen patients will start intervention in week 3 while 19 patients will continue data collection for another 2 weeks before procedure changes are introduced. This is done to distinguish between specific effects of changes in procedures and non-specific effects, such as caregivers increased attention. DISCUSSION: This study will advance the field of wearable physiological sensor use in patient care. TRIAL REGISTRATION: Registered prospectively at ClinicalTrials.gov (NCT05738278).

Development of a prototype toe sensor for detection of diabetic peripheral small fiber neuropathy.

Diabetic peripheral neuropathy (DPN) affects a large proportion of people with diabetes, and early detection is essential to prevent further progression. Widespread clinical testing relies on simplicity and cost-effectiveness of examination. Early signs of DPN may be detected by assessing the sudomotor nerves, and sudomotor activity can be measured by bioimpedance. We present a prototype toe probe for DPN detection including sensors for measuring skin AC conductance, skin temperature and humidity. The prototype was tested on five participants with DPN and five healthy age-matched controls in a pilot study. Sudomotor sensor responses to a simple deep breathing test were very weak or absent in the DPN group, with all controls having larger responses than the DPN group. Evaporation was lower for the DPN group, and skin temperature was higher on average. For the same foot, the results for sudomotor responses were in agreement with sensory neurography amplitudes from the sural nerve whereas the monofilament test gave normal results for two of the DPN participants. If sufficient detection accuracy is confirmed in larger studies, the method may provide a simple and cost-effective tool to support clinical examination. Clinical Relevance- We present the early realization and testing of a simple device to support early detection of diabetic peripheral neuropathy.

Reduced heart rate variability is related to the number of metabolic syndrome components and manifest diabetes in the sixth Tromsø study 2007-2008.

Both diabetes mellitus (DM) and the metabolic syndrome (MetS) are associated with autonomic neuropathy, which predisposes to cardiac events and death. Measures of heart rate variability (HRV) can be used to monitor the activity of the autonomic nervous system (ANS), and there are strong indications that HRV can be used to study the progression of ANS-related diabetes complications. This study aims to investigate differences in HRV in healthy, MetS and diabetic populations. Based on 7880 participants from the sixth health survey in Tromsø (Tromsø 6, 2007-2008), we found a significant negative association between the number of MetS components and HRV as estimated from short-term pulse wave signals (PRV). This decrease in PRV did not appear to be linear, instead it leveled off after the third component, with no significant difference in PRV between the MetS and DM populations. There was a significant negative association between HbA1c and PRV, showing a decrease in PRV occurring already within the normal HbA1c range. The MetS and DM populations are different from healthy controls with respect to PRV, indicating impaired ANS in both conditions. In the future, a study with assessment of PRV measurements in relation to prospective cardiovascular events seems justified.

Small intestinal viability assessment using dielectric relaxation spectroscopy and deep learning.

Intestinal ischemia is a serious condition where the surgeon often has to make important but difficult decisions regarding resections and resection margins. Previous studies have shown that 3 h (hours) of warm full ischemia of the small bowel followed by reperfusion appears to be the upper limit for viability in the porcine mesenteric ischemia model. However, the critical transition between 3 to 4 h of ischemic injury can be nearly impossible to distinguish intraoperatively based on standard clinical methods. In this study, permittivity data from porcine intestine was used to analyze the characteristics of various degrees of ischemia/reperfusion injury. Our results show that dielectric relaxation spectroscopy can be used to assess intestinal viability. The dielectric constant and conductivity showed clear differences between healthy, ischemic and reperfused intestinal segments. This indicates that dielectric parameters can be used to characterize different intestinal conditions. In addition, machine learning models were employed to classify viable and non-viable segments based on frequency dependent dielectric properties of the intestinal tissue, providing a method for fast and accurate intraoperative surgical decision-making. An average classification accuracy of 98.7% was obtained using only permittivity data measured during ischemia, and 96.2% was obtained with data measured during reperfusion. The proposed approach allows the surgeon to get accurate evaluation from the trained machine learning model by performing one single measurement on an intestinal segment where the viability state is questionable.

Current trends and opportunities in the methodology of electrodermal activity measurement.

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Diabetic Foot Assessment using Skin Impedance in a Custom Made Sensor-sock.

Diabetic peripheral neuropathy (DPN) may lead to several changes in the skin, and some of these may influence the skin impedance spectrum. In the present study we have developed a prototype solution for skin impedance spectroscopy at selected skin sites (big toe pulp, heel and toe ball) that was tested in a pilot study on five patients with DPN and five healthy controls. At the big toe, most of the controls had markedly lower impedance than the DPN group, especially in the range of 1-100 kHz. The separation between the groups seems to be weaker at the heel and weakest at the toeball. The results may indicate that monitoring of the skin impedance spectrum may be a method for detection of skin changes associated with DPN, encouraging further studies with the big toe sensor in particular.

Quantification of aerosol dispersal from suspected aerosol-generating procedures.

BACKGROUND: Oxygen-delivering modalities like humidified high-flow nasal cannula (HFNC) and noninvasive positive-pressure ventilation (NIV) are suspected of generating aerosols that may contribute to transmission of disease such as coronavirus disease 2019. We sought to assess if these modalities lead to increased aerosol dispersal compared to the use of non-humidified low-flow nasal cannula oxygen treatment (LFNC). METHODS: Aerosol dispersal from 20 healthy volunteers using HFNC, LFNC and NIV oxygen treatment was measured in a controlled chamber. We investigated effects related to coughing and using a surgical face mask in combination with the oxygen delivering modalities. An aerodynamic particle sizer measured aerosol particles (APS3321, 0.3-20 µm) directly in front of the subjects, while a mesh of smaller particle sensors (SPS30, 0.3-10 µm) was distributed in the test chamber. RESULTS: Non-productive coughing led to significant increases in particle dispersal close to the face when using LFNC and HFNC but not when using NIV. HFNC or NIV did not lead to a statistically significant increase in aerosol dispersal compared to LFNC. With non-productive cough in a room without air changes, there was a significant drop in particle levels between 100 cm and 180 cm from the subjects. CONCLUSIONS: Our results indicate that using HFNC and NIV does not lead to increased aerosol dispersal compared to low-flow oxygen treatment, except in rare cases. For a subject with non-productive cough, NIV with double-limb circuit and non-vented mask may be a favourable choice to reduce the risk for aerosol spread.

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.

Blood pressure and cardiac output during caesarean delivery under spinal anaesthesia: a prospective cohort study.

OBJECTIVES: We have previously established a method to measure transfer of nutrients between mother, placenta and fetus in vivo. The method includes measurements of maternal and fetal blood flow by Doppler ultrasound prior to spinal anaesthesia. Spinal anaesthesia affects maternal blood pressure and cardiac output. We aimed to determine the effect of spinal anaesthesia in mothers undergoing an elective caesarean section on blood pressure, heart rate and cardiac output, and whether cardiac output levels were comparable before induction of spinal anaesthesia and before delivery. DESIGN: Prospective cohort study. SETTING: Tertiary hospital in Norway. PARTICIPANTS: 76 healthy women with uneventful pregnancies undergoing an elective caesarean section. INTERVENTIONS: We induced spinal anaesthesia with a standard prevention of hypotension including intravenous fluid coloading and phenylephrine infusion. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measure was maternal cardiac output, and secondary outcome measures were invasive systolic blood pressure and heart rate. We measured heart rate and blood pressure by continuous invasive monitoring with a cannula in the radial artery. Cardiac output was estimated based on continuous arterial waveform. We compared maternal parameters 30 s before induction of spinal anaesthesia to 30 s before delivery. RESULTS: Median age at delivery was 34.5 (range 21-43) years and 17 of 76 women were nulliparous. The most prevalent indications were previous caesarean section and maternal request. Among 76 included women, 71 had sufficient data for analysis of endpoints. Median cardiac output was 6.51 (IQR (5.56-7.54) L/min before spinal anaesthesia and 6.40 (5.83-7.56) L/min before delivery (p=0.40)). Median invasive systolic blood pressure increased from 128.5 (120.1-142.7) mm Hg to 134.1 (124.0-146.6) mm Hg (p=0.014), and mean heart rate decreased from 86.0 (SD 13.9) to 75.2 (14.2) (p<0.001). CONCLUSIONS: Maternal cardiac output at the time of caesarean delivery is comparable to levels before induction of spinal anaesthesia. TRIAL REGISTRATION NUMBER: NCT00977769.

Maternal haemodynamics during labour epidural analgesia with and without adrenaline.

OBJECTIVES: Labour is one of the most painful experiences in a woman's life. Epidural analgesia using low-concentration local anaesthetics and lipophilic opioids is the gold standard for pain relief during labour. Pregnancy in general, particularly labour, is associated with changes in maternal haemodynamic variables, such as cardiac output and heart rate, which increase and peak during uterine contractions. Adrenaline is added to labour epidural solutions to enhance efficacy by stimulating the α2-adrenoreceptor. The minimal effective concentration of adrenaline was found to be 2 µg mL-1 for postoperative analgesia. The addition of adrenaline may also produce vasoconstriction, limiting the absorption of fentanyl into the systemic circulation, thereby reducing foetal exposure. However, adrenaline may influence the haemodynamic fluctuations, possibly adding to the strain on the circulatory system. The aim of this study was to compare the haemodynamic changes after application of labour epidural analgesia with or without adrenaline 2 µg mL-1. METHODS: This was a secondary analysis of a single-centre, randomised double-blind trial. Forty-one nulliparous women in labour requesting epidural analgesia were randomised to receive epidural solution of bupivacaine 1 mg mL-1, fentanyl 2 µg mL-1 with or without adrenaline 2 µg mL-1. The participants were monitored using a Nexfin CC continuous non-invasive blood pressure and cardiac output monitor. The primary outcomes were changes in peak systolic blood pressure and cardiac output during uterine contraction within 30 min after epidural activation. The effect of adrenaline was tested statistically using a linear mixed-effects model of the outcome variables' dependency on time, adrenaline, and their interaction. RESULTS: After excluding three patients due to poor data quality and two due to a malfunctioning epidural catheter, 36 patients (18 in each group) were analysed. The addition of adrenaline to the solution had no significant effect on the temporal changes in peak systolic blood pressure (p=0.26), peak cardiac output (0.84), or heart rate (p=0.91). Furthermore, no significant temporal changes in maternal haemodynamics (peak systolic blood pressure, p=0.54, peak cardiac output, p=0.59, or heart rate p=0.55) were associated with epidural analgesia during 30 min after epidural activation in both groups despite good analgesia. CONCLUSIONS: The addition of 2 µg mL-1 adrenaline to the epidural solution is not likely to change maternal haemodynamics during labour.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data.

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text], F[Formula: see text] and F[Formula: see text]-score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Estimation of Heart Rate Variability from Finger Photoplethysmography During Rest, Mild Exercise and Mild Mental Stress.

Due to the possibilities in miniaturization and wearability, photoplethysmography (PPG) has recently gained a large interest not only for heart rate measurement, but also for estimating heart rate variability, which is derived from ECG by convention. The agreement between PPG and ECG-based HRV has been assessed in several studies, but the feasibility of PPG-based HRV estimation is still largely unknown for many conditions. In this study, we assess the feasibility of HRV estimation based on finger PPG during rest, mild physical exercise and mild mental stress. In addition, we compare different variants of signal processing methods including selection of fiducial point and outlier correction. Based on five minutes synchronous recordings of PPG and ECG from 15 healthy participants during each of these three conditions, the PPG-based HRV estimation was assessed for the SDNN and RMSSD parameters, calculated based on two different fiducial points (foot point and maximum slope), with and without outlier correction. The results show that HRV estimation based on finger PPG is feasible during rest and mild mental stress, but can give large errors during mild physical exercise. A good estimation is very dependent on outlier correction and fiducial point selection, and SDNN seems to be a more robust parameter compared to RMSSD for PPG-based HRV estimation.

Classification of Emotions Based on Electrodermal Activity and Transfer Learning - a Pilot Study.

This paper describes the development, execution and results of an experiment assessing emotions with electrodermal response measurements and machine learning. With ten participants, the study was carried out by eliciting emotions through film clips. The data was gathered with the Sudologger 3 and processed with continuous wavelet transformation. A machine learning algorithm was used to classify the data with the use of transfer learning and random forest classification. The results showed that the experiment lays a foundation for further exploration in the field. The addition of augmented data strengthened the classification and proved that more data would benefit the machine learning algorithm. The pilot study brought to light several areas to help with the expansion of the study for larger scale assessment of emotions with electrodermal response measurements and machine learning for the benefit of fields like psychology.

The correlations among the skin conductance features responding to physiological stress stimuli.

BACKGROUND/AIM: The skin conductance responses (SCRs) are a well-accepted indicator of physiological arousal for both research purposes and clinical approaches. The shape of SCRs is analyzed by various features. However, the estimation of how much (in %) one feature can explain another is still an open issue. The aim of this study was to assess whether variation in one SCR feature predicts changes in other features. METHODS: Skin conductance (SC) was measured during relaxation and mental stress in 40 subjects. SCRs were induced by three external stimuli, which were deep breath, a mental arithmetic, task and a visual task. RESULTS: The findings of this study showed that about 55% (R2  = 0.55) of the variation in the half recovery time (SCRs_rec 50%) can be explained by the rise time (SCRs_ris), whereas variation in amplitude of the skin conductance responses (SCRs_amp) and the skin conductance level (SCL) is independent and cannot be explained by the other features, as R2 values obtained from all analyses among these SCR features in average were lower 0.19. CONCLUSIONS: The study results suggest that the two timing phases (SCRs_rec and SCRs_ris) are not completely independent from each other, although they might be governed by different sweating mechanisms (secretion and reabsorption). However, SCRs_amp and SCL were independent. These findings can help in choosing the optimal set of features of an automated system for processing EDA, which reflect the alterations in the activation level generated during an emotional episode.

Accuracy and trending abilities of finger plethysmographic blood pressure and cardiac output compared to invasive measurements during caesarean delivery in healthy women: an observational study.

BACKGROUND: In women presenting for caesarean section under spinal anesthesia, continuous measurement of circulatory aspects, such as blood pressure and cardiac output, is often needed. At present, invasive techniques are used almost exclusively. Reliable non-invasive monitoring would be welcome, as it could be safer, less uncomfortable, and quick and easy to apply. We aimed to evaluate whether a non-invasive, finger plethysmographic device, the ccNexFin monitor, can replace invasively measured blood pressure in the radial artery, and whether cardiac output measurements from this device can be used interchangeably with measurements from the mini-invasive LiDCO monitor currently in use at our institution. METHODS: Simultaneous invasive measurements were compared to ccNexFin in 23 healthy women during elective caesarean section under spinal anesthesia. We used Bland Altman statistics to assess agreement, and polar plot methodology to judge trending abilities with pre-defined limits. RESULTS: Mean arterial and systolic pressures showed biases (invasive - ccNexFin) of - 4.3 and 12.2 mmHg, with limits of agreement of - 15.9 - 7.4 and - 11.1 - 35.6, respectively. The ccNexFin trending abilities were within the suggested limits for mean pressure but insufficient for systolic pressure compared to invasive measurements. Cardiac output had a small bias of 0.2 L/min, but wide limits of agreement of - 2.6 - 3.0. The ccNexFin trending abilities compared to the invasive estimated values (LiDCO) were unsatisfactory. CONCLUSIONS: We consider the ccNexFin monitor to have sufficient accuracy in measuring mean arterial pressure. The limits of agreement for systolic measurements were wider, and the trending ability compared to invasive measurements was outside the recommended limit. The ccNexFin is not reliable for cardiac output measurements or trend in pregnant women for caesarean delivery under spinal anesthesia. TRIAL REGISTRATION: Registered May 23, 2013, at ClinicalTrials.gov under number NCT01861132 .

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.

An Investigation on Bilateral Asymmetry in Electrodermal Activity.

The Multiple Arousal Theory (Picard et al., 2016) was proposed to explain retrospective observations of bilateral differences in electrodermal activities occurring in threat-related high-stake situations. The theory proposes different cortical and subcortical structures to be involved in the processing of various facets of emotional states. Systematic investigations of this effect are still scarce. This study tested the prediction of bilateral electrodermal effects in a controlled laboratory environment where electrodermal activity (EDA) was recorded bilaterally during normal activity and two stress-tasks in 25 healthy volunteers. A visual search stress task with a performance-related staircase algorithm was used, ensuring intersubjectively comparable stress levels across individuals. After completion of the task, a sense of ownership of an attractive price was created and loss aversion introduced to create a high-stake situation. Confirmation of the theory should satisfy the hypothesis of a bilateral difference in EDA between the dominant and non-dominant hand, which is larger during high-stake stressors than during low-stake stressors. The bilateral difference was quantified and compared statistically between the two stress-tasks, revealing no significant difference between them nor any significant difference between the stress tasks and the period of normal activity. Subgroup analysis of only the participants with maximum self-rating of their desire to win the price (n = 7) revealed neither any significant difference between the two tasks nor between the stress-tasks and the period of normal activity. Although the theory was not confirmed by this study, eight cases suggestive of bilateral difference within the recordings were identified and are presented. Because the study is limited in using one of several possible operationalizations of the phenomenon, it is not possible to draw a general conclusion on the theory. Nevertheless, the study might contribute to a better understanding and encourage systematic review and hypothesis development regarding this new theory. Possible explanations and suggestions for future pathways to systematically investigate the Multiple Arousal Theory are discussed.

Author Correction: Evaluation of Hypoglycaemia with Non-Invasive Sensors in People with Type 1 Diabetes and Impaired Awareness of Hypoglycaemia.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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.