Showing papers on "Pain assessment published in 2021"
TL;DR: Three distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation and an extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system.
Abstract: The subjective nature of pain makes it a very challenging phenomenon to assess. Most of the current pain assessment approaches rely on an individual’s ability to recognise and report an observed pain episode. However, pain perception and expression are affected by numerous factors ranging from personality traits to physical and psychological health state. Hence, several approaches have been proposed for the automatic recognition of pain intensity, based on measurable physiological and audiovisual parameters. In the current paper, an assessment of several fusion architectures for the development of a multi-modal pain intensity classification system is performed. The contribution of the presented work is two-fold: (1) 3 distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation. (2) An extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system. The assessment is based on the SenseEmotion Database and experimental validation demonstrates the relevance of the multi-modal classification approach, which achieves classification rates of respectively $83.39\%$ 83 . 39 % , $59.53\%$ 59 . 53 % and $43.89\%$ 43 . 89 % in a 2-class, 3-class and 4-class pain intensity classification task.
47 citations
TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus as a source of infection in animals and its role in the immune system is investigated.
Abstract: The burden of pain in newborn infants has been investigated in numerous studies, but little is known about the appropriateness of the use of pain scales according to the specific type of pain or infant condition. This systematic review aimed to evaluate the reporting of neonatal pain scales in randomized trials. A systematic search up to March 2019 was performed in Embase, PubMed, PsycINFO, CINAHL, Cochrane Library, Scopus, and Luxid. Randomized and quasirandomized trials reporting neonatal pain scales were included. Screening of the studies for inclusion, data extraction, and quality assessment was performed independently by 2 researchers. Of 3718 trials found, 352 with 29,137 infants and 22 published pain scales were included. Most studies (92%) concerned procedural pain, where the most frequently used pain scales were the Premature Infant Pain Profile or Premature Infant Pain Profile-Revised (48%), followed by the Neonatal Infant Pain Scale (23%). Although the Neonatal Infant Pain Scale is validated only for acute pain, it was also the second most used scale for ongoing and postoperative pain (21%). Only in a third of the trials, blinding for those performing the pain assessment was described. In 55 studies (16%), pain scales that were used lacked validation for the specific neonatal population or type of pain. Six validated pain scales were used in 90% of all trials, although not always in the correct population or type of pain. Depending on the type of pain and population of infants included in a study, appropriate scales should be selected. The inappropriate use raises serious concerns about research ethics and use of resources. (Less)
43 citations
TL;DR: This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions, and covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and the machine learning methods used.
Abstract: Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.
41 citations
TL;DR: In this article, the authors provide an overview of pain management including assessment and treatment applied to the most common husbandry procedures, and recommendations to improve animal welfare in these species, focusing on cattle, sheep, and pigs.
Abstract: Pain causes behavioral, autonomic, and neuroendocrine changes and is a common cause of animal welfare compromise in farm animals. Current societal and ethical concerns demand better agricultural practices and improved welfare for food animals. These guidelines focus on cattle, sheep, and pigs, and present the implications of pain in terms of animal welfare and ethical perspectives, and its challenges and misconceptions. We provide an overview of pain management including assessment and treatment applied to the most common husbandry procedures, and recommendations to improve animal welfare in these species. A cost-benefit analysis of pain mitigation is discussed for food animals as well as the use of pain scoring systems for pain assessment in these species. Several recommendations are provided related to husbandry practices that could mitigate pain and improve farm animal welfare. This includes pain assessment as one of the indicators of animal welfare, the use of artificial intelligence for automated methods and research, and the need for better/appropriate legislation, regulations, and recommendations for pain relief during routine and husbandry procedures.
34 citations
TL;DR: The authors reviewed relevant articles, chapters, and guidance documents from the European Medicines Agency and U.S. Food and Drug Administration relevant to pain-related COAs that are relevant to clinical trials to describe the different types of COAs and provide an overview of key considerations for evaluating COAs.
30 citations
TL;DR: FNIRS technology could enhance the ability to evaluate evoked and persistent pain across different age groups and clinical populations and the current and potential applications in various pain conditions.
30 citations
TL;DR: The case is made for home cage wheel running as an effective and clinically relevant method to screen novel analgesics for therapeutic potential and the literature using wheel running to assess pain.
Abstract: Chronic pain affects approximately one-third of the population worldwide. The primary goal of animal research is to understand the neural mechanisms underlying pain so better treatments can be developed. Despite an enormous investment in time and money, almost no novel treatments for pain have been developed. There are many factors that contribute to this lack of translation in drug development. The mismatch between the goals of drug development in animals (inhibition of pain-evoked responses) and treatment in humans (restoration of function) is a major problem. To solve this problem, a number of pain-depressed behavioral tests have been developed to assess changes in normal behavior in laboratory animals. The use of home cage wheel running as a pain assessment tool is especially useful in that it is easy to use, provides an objective measurement of the magnitude and duration of pain, and is a clinically relevant method to screen novel drugs. Pain depresses activity in humans and animals, and effective analgesic treatments restore activity. Unlike traditional pain-evoked tests (e.g., hot plate, tail flick, von Frey test), restoration of home cage wheel running evaluates treatments for both antinociceptive efficacy and the absence of disruptive side effects (e.g., sedation, paralysis, nausea). This article reviews the literature using wheel running to assess pain and makes the case for home cage wheel running as an effective and clinically relevant method to screen novel analgesics for therapeutic potential.
25 citations
TL;DR: In this article, the authors presented an automatic pain assessment tool using GSR signals to predict different pain intensities in non-communicative, postoperative patients, which can identify features of emotional states and anxiety induced by varying pain levels.
Abstract: Background: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects.
Objective: The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients.
Methods: The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models.
Results: Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier.
Conclusions: We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities.
24 citations
TL;DR: In this paper, the authors have developed consensus recommendations on the design, conduct, analysis, and interpretation of RCTs of spinal cord stimulation (SCS) for chronic pain.
Abstract: Spinal cord stimulation (SCS) is an interventional nonpharmacologic treatment used for chronic pain and other indications. Methods for evaluating the safety and efficacy of SCS have evolved from uncontrolled and retrospective studies to prospective randomized controlled trials (RCTs). Although randomization overcomes certain types of bias, additional challenges to the validity of RCTs of SCS include blinding, choice of control groups, nonspecific effects of treatment variables (eg, paresthesia, device programming and recharging, psychological support, and rehabilitative techniques), and safety considerations. To address these challenges, 3 professional societies (Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials, Institute of Neuromodulation, and International Neuromodulation Society) convened a meeting to develop consensus recommendations on the design, conduct, analysis, and interpretation of RCTs of SCS for chronic pain. This article summarizes the results of this meeting. Highlights of our recommendations include disclosing all funding source and potential conflicts; incorporating mechanistic objectives when possible; avoiding noninferiority designs without internal demonstration of assay sensitivity; achieving and documenting double-blinding whenever possible; documenting investigator and site experience; keeping all information provided to patients balanced with respect to expectation of benefit; disclosing all information provided to patients, including verbal scripts; using placebo/sham controls when possible; capturing a complete set of outcome assessments; accounting for ancillary pharmacologic and nonpharmacologic treatments in a clear manner; providing a complete description of intended and actual programming interactions; making a prospective ascertainment of SCS-specific safety outcomes; training patients and researchers on appropriate expectations, outcome assessments, and other key aspects of study performance; and providing transparent and complete reporting of results according to applicable reporting guidelines.
24 citations
TL;DR: This review summarizes available evidence while suggesting practical clinical approaches to pain assessment and avoidance, procedural analgesia, postoperative analgesIA, sedation during mechanical ventilation and therapeutic hypothermia, and the issues of tolerance and withdrawal in vulnerable critically ill neonates.
Abstract: The prevention, assessment, and treatment of neonatal pain and agitation continues to challenge clinicians and researchers. Substantial progress has been made in the past three decades, but numerous outstanding questions remain. In this setting, clinicians must establish safe and compassionate standardized practices that consider available efficacy data, long-term outcomes, and research gaps. Novel approaches with limited data must be carefully considered against historic standards of care with robust data suggesting limited benefit and clear adverse effects. This review summarizes available evidence while suggesting practical clinical approaches to pain assessment and avoidance, procedural analgesia, postoperative analgesia, sedation during mechanical ventilation and therapeutic hypothermia, and the issues of tolerance and withdrawal. Further research in all areas represents an urgent priority for optimal neonatal care. In the meantime, synthesis of available data offers clinicians challenging choices as they balance benefit and risk in vulnerable critically ill neonates.
23 citations
TL;DR: CP is associated with higher cognitive decline, in particular in processing speed, and the importance of actively treating CP with pharmacological and non-pharmacological strategies to prevent its consequences, including cognitive consequences is reinforced.
Abstract: Chronic pain (CP) was associated with impaired cognitive performance in several cross-sectional studies conducted in older adults; however, fewer longitudinal studies assessed this link that remains still debated. With a prospective design, the present analysis was aimed at evaluating the relationship between CP and the change in several tests assessing memory, attention, verbal fluency, and processing speed. The study population was selected from the PAQUID study, a cohort of community dwellers aged 65 years and older; 693 subjects receiving a pain assessment were included. Chronic pain was evaluated using a questionnaire administered at 3-year follow-up. Cognitive performances were assessed every 2 to 3 years between 3 and 15 years assessing general cognition (Mini-Mental State Examination), verbal and visual memory (word paired-associate test and Benton test), attention and speed processing (Wechsler Digit Symbol Substitution Test and Zazzo's Cancellation Task), and language skills and executive functions (Isaacs Set Test). The link between CP and the change in cognitive function was assessed with latent process mixed models controlled for age, sex, education, comorbidities, depression, and analgesic drugs. The association between CP and each of the cognitive scores was then tested with the same procedure. A significant relationship was observed between CP and poorer 15-year scores on global cognitive performance (P = 0.004), and specifically, the Digit Symbol Substitution Test (P = 0.002) was associated with a higher slope of decline (P = 0.02). Chronic pain is associated with a higher cognitive decline, particularly in processing speed. This result reinforces the importance of actively treating CP with pharmacological and nonpharmacological strategies to prevent its consequences, including cognitive consequences.
TL;DR: In this article, the authors extracted features from Electrodermal activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain.
Abstract: In current clinical settings, typically pain is measured by a patient's self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.
TL;DR: In this paper, the authors evaluated the effect of virtual reality distraction on anxiety and pain during buccal infiltration anesthesia in pediatric patients and found that younger subjects and females had higher mean FLACC behavioral pain assessment scale scores.
Abstract: Different distraction techniques have been used in dentistry and have shown great results in managing anxious pediatric patients specially during local anesthesia administration. One of the recently invented techniques is virtual reality. The purpose of the study was to evaluate the effect of virtual reality distraction on anxiety and pain during buccal infiltration anesthesia in pediatric patients. Healthy, cooperative 6- to 12-year-old children requiring buccal infiltration anesthesia were randomly assigned to a test or control group. In the test group, local anesthesia was administered while the subjects were watching a cartoon video using virtual reality goggles. Subjects in the control group watched a cartoon video on a screen during the administration of local anesthesia. To assess anxiety in both groups, heart rate was recorded using a pulse oximeter at five time points: (1) once the subject sets on the dental chair as a baseline; (2) when video is on; (3) at topical anesthesia application; (4) during needle insertion; (5) after the administration of local anesthesia. The face, legs, activity, cry, consolability (FLACC) behavioral pain assessment scale and the Wong–Baker FACES pain rating scale were used to assess pain. A total of 50 subjects were included with a mean age of 8.4 ± 1.46 years. Twenty-nine (58.0%) of the subjects were females. The mean heart rate at all time points except baseline was significantly higher among the test group compared to the control group. Multiple regression analysis showed that younger subjects and females had higher mean FLACC behavioral pain assessment scale scores (P = 0.034 and P = 0.004, respectively) regardless of the distraction technique used. Younger subjects and subjects with higher baseline heart rate reported higher mean Wong–Baker FACES pain rating scale score (P = 0.031 and P = 0.010, respectively), controlling for all other variables. Female subjects and the younger age group were more likely to report higher pain scores during local anesthesia administration regardless of the type of distraction used. The study was retrospectively registered in ClinicalTrials.gov with the identifier: NCT04483336 on 23/07/2020.
TL;DR: Global pain measures are associated with increased risk of injurious falls in older adults and interventions are needed to prevent fall injuries among elders with chronic pain.
Abstract: Background Fall injuries are a leading cause of death in older adults. The potential impact of chronic pain characteristics on risk for injurious falls is not well understood. This prospective cohort study examined the relationship between chronic pain and risk for injurious falls in older adults. Methods The MOBILIZE Boston Study enrolled 765 community-dwelling adults aged 70 years and older living in and around Boston, Massachusetts. Chronic pain characteristics, including pain severity, pain interference, and pain distribution, were measured at baseline using the Brief Pain Inventory subscales and a joint pain questionnaire. Occurrence of falls and fall-related injuries were recorded using monthly fall calendar postcards and fall follow-up interviews during the 4-year follow-up period. Results Negative binomial regression models showed that pain interference and pain distribution, but not pain severity, independently predicted injurious falls adjusting for potential confounders. Participants in the highest third of pain interference scores had a 61% greater risk of injurious falls compared to those reporting little or no pain interference. Compared to no pain, multisite pain was associated with a 57% greater risk of injurious falls. Stratified by gender, the association was only significant in women. In the short term, moderate to severe pain in a given month was associated with increased risk of injurious falls in the subsequent month. Conclusions Global pain measures are associated with increased risk of injurious falls in older adults. Pain assessment should be incorporated into fall risk assessments. Interventions are needed to prevent fall injuries among elders with chronic pain.
TL;DR: In this article, a double-blind, placebo-controlled, within-subject outpatient clinical laboratory study sought to determine the analgesic effects, abuse liability, safety and tolerability of acute CBD (0, 200, 400 and 800 mg orally) in healthy non-cannabis-using volunteers.
Abstract: AIMS: Preclinical studies demonstrate that cannabidiol (CBD) elicits an antinociceptive response in animal models of neuropathic pain; in humans, limited data are available to support such analgesic effects. Few studies have examined CBD's analgesic effects when administered without other compounds, and little is known regarding dose-dependent effects in noncannabis users. METHODS: This double-blind, placebo-controlled, within-subject outpatient clinical laboratory study sought to determine the analgesic effects, abuse liability, safety and tolerability of acute CBD (0, 200, 400 and 800 mg orally) in healthy noncannabis-using volunteers (n = 17; 8 men, 9 women). Outcomes included experimental pain threshold and pain tolerance using the cold pressor test (CPT), subjective ratings of CPT painfulness and bothersomeness, subjective ratings of abuse liability and mood, and cardiovascular measures, which were assessed at baseline and several time points after drug administration. Data analyses included repeated measures analysis of variance (ANOVA) with planned comparisons. RESULTS: CBD failed to consistently affect pain threshold and tolerance in the CPT relative to placebo. All doses of CBD increased ratings of painfulness compared to placebo (P < .01). Further, CBD had dose-dependent, modest effects on mood and subjective drug effects associated with abuse liability. Oral CBD was safe and well tolerated, producing small decreases in blood pressure (P < .01). CONCLUSION: CBD did not elicit consistent dose-dependent analgesia and in fact increased pain on some measures. Future studies exploring CBD-induced pain relief should consider using a more extensive pain assessment paradigm in different participant populations.
TL;DR: The Bristol Rabbit Pain Scale (BRPS) as discussed by the authors was developed over five phases using a unique combination of methods: focus groups and behavioural observation, which led to a composite pain scale of six categories (Demeanour, Posture, Locomotion, Ears, Eyes and Grooming) with four intensities of pain (0, 1, 2, 3, and 3).
Abstract: A species-specific composite pain scale is a prerequisite for adequate pain assessment. The aim of this study was to develop a multidimensional pain scale specific to rabbits (Oryctolagus cuniculus) called the Bristol Rabbit Pain Scale (BRPS). The scale was developed over five phases using a unique combination of methods: focus groups and behavioural observation. The first two phases aimed at identifying descriptors to describe a rabbit in pain, and then reducing their number, both using focus groups. A total of 72 pain descriptors were grouped under six categories (Demeanour, Posture, Facial expression, Attention to the painful area, Audible and Other) and 'No pain' descriptors were added. The third phase aimed to confirm, through video observation of rabbits, the categories and descriptors previously described, to reject those terms that were ambiguous, and identify any new descriptors that had not been included in the previous list of descriptors. This led to the rejection of the categories Audible and Attention to the painful area and of 34 descriptors. Seven new descriptors were identified. The last two phases constructed the final format of the BRPS by refining the categories, ranking the descriptors on an ordinal scale and testing the internal reliability of the scale using Cronbach's alpha test. This led to a composite pain scale of six categories (Demeanour, Posture, Locomotion, Ears, Eyes and Grooming) with four intensities of pain (0, 1, 2, and 3), a total score of 0-18, and a high Cronbach's alpha coefficient (alpha = 0.843). This BRPS fills an important gap in the field of rabbit medicine and has the potential to improve the assessment and management of pain in rabbits providing veterinary professionals with a novel multidimensional pain assessment tool. Further studies will investigate the clinical utility, validity and reliability of the BRPS.
TL;DR: Novel composite indexes could help to refine pain assessment by changing the physician’s perception of patient condition on the basis of objective and holistic metrics, and by providing new insights to therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.
Abstract: The multidimensionality of chronic pain forces us to look beyond isolated assessment such as pain intensity, which does not consider multiple key parameters, particularly in post-operative Persistent Spinal Pain Syndrome (PSPS-T2) patients. Our ambition was to produce a novel Multi-dimensional Clinical Response Index (MCRI), including not only pain intensity but also functional capacity, anxiety-depression, quality of life and quantitative pain mapping, the objective being to achieve instantaneous assessment using machine learning techniques. Two hundred PSPS-T2 patients were enrolled in the real-life observational prospective PREDIBACK study with 12-month follow-up and received various treatments. From a multitude of questionnaires/scores, specific items were combined, as exploratory factor analyses helped to create a single composite MCRI; using pairwise correlations between measurements, it appeared to more accurately represent all pain dimensions than any previous classical score. It represented the best compromise among all existing indexes, showing the highest sensitivity/specificity related to Patient Global Impression of Change (PGIC). Novel composite indexes could help to refine pain assessment by informing the physician's perception of patient condition on the basis of objective and holistic metrics, and also by providing new insights regarding therapy efficacy/patient outcome assessments, before ultimately being adapted to other pathologies.
TL;DR: The mouse Grimace Scale (MGS) was developed 10 years ago as a method for assessing pain through the characterisation of changes in five facial features or action units as mentioned in this paper.
Abstract: The Mouse Grimace Scale (MGS) was developed 10 years ago as a method for assessing pain through the characterisation of changes in five facial features or action units. The strength of the technique is that it is proposed to be a measure of spontaneous or non-evoked pain. The time is opportune to map all of the research into the MGS, with a particular focus on the methods used and the technique's utility across a range of mouse models. A comprehensive scoping review of the academic literature was performed. A total of 48 articles met our inclusion criteria and were included in this review. The MGS has been employed mainly in the evaluation of acute pain, particularly in the pain and neuroscience research fields. There has, however, been use of the technique in a wide range of fields, and based on limited study it does appear to have utility for pain assessment across a spectrum of animal models. Use of the method allows the detection of pain of a longer duration, up to a month post initial insult. There has been less use of the technique using real-time methods and this is an area in need of further research.
TL;DR: In this article, the authors developed and evaluated an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.
Abstract: Background: There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients.
Objective: The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain.
Methods: The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study.
Results: Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2).
Conclusions: We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients.
TL;DR: In this paper, a longitudinal cohort study was conducted to examine whether associations exist between uncontrolled pain and risk for two common BPSDs (depression and behavioral symptoms) among long-term care (LTC) residents with Alzheimer disease and related dementia (ADRD).
TL;DR: In this article, the authors evaluated the validity and utility of four commonly used measures of pain intensity in a sample of patients with chronic pain from Thailand and compared findings in the current sample with published findings from research conducted in other countries, in order to identify the measure or measures which might be most appropriate for cross-country research.
Abstract: Background The majority of previous research that has examined the validity of pain intensity rating scales has been conducted in western and developed countries. Research to evaluate the generalizability of previous findings in non-developed countries is necessary for identifying the scales that are most appropriate for use in international research. Purpose The aims of the current study were to (1) evaluate the validity and utility of four commonly used measures of pain intensity in a sample of patients with chronic pain from Thailand and (2) compare findings in the current sample with published findings from research conducted in other countries, in order to identify the measure or measures which might be most appropriate for cross-country research. Methods Three hundred and sixty patients with chronic pain seen in a hospital in Bangkok, Thailand, were asked to rate their current pain and average, worst, and least pain intensity in the past week using the Visual Analogue Scale (VAS), 6-point Verbal Rating Scale (VRS-6), 0-10 Numerical Rating Scale (NRS-11), and Faces Pain Scale-Revised (FPS-R). We evaluated the utility and validity of each measure by examining the (1) rates of correct responding and (2) association of each measure with a factor score representing the variance shared across measures, respectively. We also evaluated the associations between incorrect response rates and both age and education level, and then compared the findings from this sample with the findings from research conducted in other countries. Results The results indicated support for the validity of all measures among participants who were able to use these measures. However, there was variability in the incorrect response rates, with the VAS having the highest (45%) and the NRS-11 having the lowest (15%) incorrect response rates. The VAS was also the least preferred (9%) and the NRS-11 the most preferred (52%) scale. Education and age were significantly associated with incorrect response rates, and education level with scale preference. Conclusion The findings indicate that the NRS-11 has the most utility in our sample of Thai individuals with chronic pain. However, when considered in light of the findings from other countries, the results of this study suggest that the FPS-R may have the most utility for use in cross-cultural and international research. Research in additional samples in developing countries is needed to evaluate the generalizability of the current findings.
TL;DR: In this paper, the authors developed and evaluated a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method.
Abstract: The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.
TL;DR: It is essential that nurses gain confidence in distinguishing signs and symptoms of pain from behavioral changes in dementia, and it is important to improve interdisciplinary communication and to get physicians to listen and prioritize pain assessment and management.
TL;DR: The UNESP-Botucatu multidimensional feline pain assessment scale (UFEPS) is a valid and reliable instrument for acute pain assessment in cats as mentioned in this paper.
Abstract: Background The UNESP-Botucatu multidimensional feline pain assessment scale (UFEPS) is a valid and reliable instrument for acute pain assessment in cats. However, its limitations are that responsiveness was not tested using a negative control group, it was validated only for ovariohysterectomy, and it can be time-consuming. We aimed to evaluate the construct and criterion validity, reliability, sensitivity, and specificity of the UFEPS and its novel short form (SF) in various clinical or painful surgical conditions. Methods Ten client-owned healthy controls (CG) and 40 client-owned cats requiring pain management for clinical or surgical care (20 clinical and 20 surgery group (12 orthopedic and eight soft tissue surgeries) were recruited. Three evaluators assessed pain, in real-time, in clinical cases before and 20 min after rescue analgesia and in surgical cases before and up to 6.5 hours postoperatively, by using the visual analog, numerical ratio, and a simple descriptive scale, in this order, followed by the UFEPS-SF, UFEPS and Glasgow multidimensional feline pain (Glasgow CMPS-Feline) in random order. For the surgical group, rescue analgesia (methadone 0.2 mg/kg IM or IV and/or dipyrone 12.5 mg/kg IV) was performed when the UFEPS-SF score was ≥4 or exceptionally according to clinical judgement. If a third interventional analgesia was required, methadone (0.1-0.2 mg/kg IM) and ketamine (1 mg/kg IM) were administered. For the clinical group, all cats received rescue analgesia (methadone 0.1-0.2 mg/kg IM or IV or nalbuphine 0.5 mg/kg IM or IV), according to the clinician in charge, regardless of pain scores. Construct (1-comparison of scores in cats undergoing pain vs pain-free control cats by unpaired Wilcoxon-test and 2-responsiveness to analgesia by paired Wilcoxon test) and concurrent criterion validity (Spearman correlation of the total score among scales), inter-rater reliability, specificity and sensitivity were calculated for each scale (α = 0.05). Results Reliability ranged between moderate and good for the UFEPS and UFEPS-SF (confidence intervals of intraclass coefficients = 0.73-0.86 and 0.63-0.82 respectively). The Spearman correlation between UFEPS and UFEPS-SF was 0.85, and their correlation with Glasgow CMPS-Feline was strong (0.79 and 0.78 respectively), confirming criterion validity. All scales showed construct validity or responsiveness (higher scores of cats with clinical and postoperative pain vs healthy controls, and the reduction in scores after rescue analgesia). The sensitivity and specificity of the UFEPS, UFEPS-SF and Glasgow CMPS-Feline were moderate (sensitivity 83.25, 78.60% and 74.28%; specificity 72.00, 84.67 and 70.00%, respectively). Conclusions Both UFEPS and UFEPS-SF showed appropriate concurrent validity, responsiveness, reliability, sensitivity, and specificity for feline acute pain assessment in cats with various clinical and orthopedic and soft tissue surgical conditions.
TL;DR: In this paper, a systematic search of pain assessment and management in critical care patient-relevant literature from four databases was done, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
Abstract: Aim This review aims to examine nurses' perceived barriers to and facilitators of pain assessment and management in adult critical care patients. Background Pain is one of the worst memories among critically ill patients. However, pain among those patients is still undertreated due to several barriers that impede effective management. Therefore, addressing the perceived barriers and facilitators to pain assessment management among critical care nurses is crucial. Methods A systematic search of pain assessment and management in critical care patient-relevant literature from four databases was done, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results The barriers and facilitators were categorized into four groups: nurse-related, patient-related, physician-related, and system-related. The most frequently reported barriers in this study included nurses' lack of knowledge regarding the use of pain assessment tools, patients' inability to communicate, physicians' prescription of analgesics being independent of pain scores evaluation, and absence of standardized guidelines and protocols for pain evaluation and control. For the facilitators, the most reported ones include ongoing education and professional training related to pain assessment and management, patients' ability to self-report pain, effective collaboration between physicians and nurses, and productive discussion of patients' pain scores during nurse-to-nurse handovers. Conclusion Various barriers and facilitators to pain assessment and management were identified and examined in this review. However, future research is still needed to further investigate these barriers and facilitators and examine any other potential associated factors among critical care nurses. Relevance to Clinical Practice The findings of our study could help hospital managers in developing continuous education and staff development training programs on assessing and managing pain for critical care patients. Also, our findings could be used to develop an evidence-based standard pain management protocol tailored to effectively assess and promptly treat pain in critical care patients.
TL;DR: In this article, a systematic review on pain prevalence rates during cancer treatment dates already from 2016, the aim of the present systematic review was to provide an overview of pain prevalence rate during cancer treatments since this previous review.
TL;DR: PWD and caregiver characteristics were differentially associated with PWD versus caregiver report of pain interference, and results suggest the importance of caregiver reports to inform assessment, as well as factors complicating assessment.
Abstract: Objectives: Pain assessment and treatment is challenging among persons with dementia (PWDs). To better understand reports of pain interference, we examined ratings made by PWDs, as well as corresponding ratings about PWDs, as reported by the caregiver. We aimed to assess alignment between and predictors of caregiver and PWD report of pain interference. Methods: The sample consisted of 203 veterans with pain and mild to moderately severe dementia and an informal caregiver. Results: Most PWDs and their caregivers reported at least some pain interference and similar levels of pain interference. PWDs with greater cognitive impairment reported less pain interference, whereas caregivers who perceived the PWD to have greater depression reported more pain interference. Conclusions: PWD and caregiver characteristics were differentially associated with PWD versus caregiver report of pain interference. Results suggest the importance of caregiver reports to inform assessment, as well as factors complicating assessment. Pain in Dementia As one ages, the risk of developing both dementia and pain increases substantially (Scherder et al., 2009). It is estimated that 30% to 50% of persons with dementia (PWDs) experience persistent pain, a complex multifactor problem (Corbett et al., 2014). Despite the high prevalence of pain among older adults with dementia, and major advances in pain management, pain often remains unrecognized or undertreated (Hodgson et al., 2014).
01 Jan 2021
TL;DR: In this article, the authors investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain.
Abstract: Introduction We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain. Methods We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain. Results Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited. Conclusion There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.
TL;DR: Joint learning of robust pain-related facial expression features from fused RGB appearance and shape-based latent representation results in a more robust pain assessment model as compared to learning from either of the representation independently.