Showing papers on "Pain assessment published in 2023"

Quantification of Patient-Reported Pain Locations

Abstract: Patient-reported pain locations are critical for comprehensive pain assessment. Our study aim was to introduce an automated process for measuring the location and distribution of pain collected during a routine outpatient clinic visit. In a cross-sectional study, 116 adults with sickle cell disease–associated pain completed PAINReportItⓇ. This computer-based instrument includes a two-dimensional, digital body outline on which patients mark their pain location. Using the ImageJ software, we calculated the percentage of the body surface area marked as painful and summarized data with descriptive statistics and a pain frequency map. The painful body areas most frequently marked were the left leg-front (73%), right leg-front (72%), upper back (72%), and lower back (70%). The frequency of pain marks in each of the 48 body segments ranged from 3 to 79 (mean, 33.2 ± 21.9). The mean percentage of painful body surface area per segment was 10.8% ± 7.5% (ranging from 1.3% to 33.1%). Patient-reported pain locations can be easily analyzed from digital drawings using an algorithm created via the free ImageJ software. This method may enhance comprehensive pain assessment, facilitating research and personalized care over time for patients with various pain conditions.

The Clinical Suitability of an Artificial Intelligence–Enabled Pain Assessment Tool for Use in Infants: Feasibility and Usability Evaluation Study

Abstract: Background Infants are unable to self-report their pain, which, therefore, often goes underrecognized and undertreated. Adequate assessment of pain, including procedural pain, which has short- and long-term consequences, is critical for its management. The introduction of mobile health–based (mHealth) pain assessment tools could address current challenges and is an area requiring further research. Objective The purpose of this study is to evaluate the accuracy and feasibility aspects of PainChek Infant and, therefore, assess its applicability in the intended setting. Methods By observing infants just before, during, and after immunization, we evaluated the accuracy and precision at different cutoff scores of PainChek Infant, which is a point-of-care mHealth–based solution that uses artificial intelligence to detect pain and intensity based solely on facial expression. We used receiver operator characteristic analysis to assess interpretability and establish a cutoff score. Clinician comprehensibility was evaluated using a standardized questionnaire. Other feasibility aspects were evaluated based on comparison with currently available observational pain assessment tools for use in infants with procedural pain. Results Both PainChek Infant Standard and Adaptive modes demonstrated high accuracy (area under the curve 0.964 and 0.966, respectively). At a cutoff score of ≥2, accuracy and precision were 0.908 and 0.912 for Standard and 0.912 and 0.897 for Adaptive modes, respectively. Currently available data allowed evaluation of 16 of the 17 feasibility aspects, with only the cost of the outcome measurement instrument unable to be evaluated since it is yet to be determined. PainChek Infant performed well across feasibility aspects, including interpretability (cutoff score defined), ease of administration, completion time (3 seconds), and clinician comprehensibility. Conclusions This work provides information on the feasibility of using PainChek Infant in clinical practice for procedural pain assessment and monitoring, and demonstrates the accuracy and precision of the tool at the defined cutoff score.

Behavioral cancer pain intervention dosing: results of a Sequential Multiple Assignment Randomized Trial.

Abstract: ABSTRACT Behavioral pain management interventions are efficacious for reducing pain in patients with cancer. However, optimal dosing of behavioral pain interventions for pain reduction is unknown, and this hinders routine clinical use. A Sequential Multiple Assignment Randomized Trial (SMART) was used to evaluate whether varying doses of Pain Coping Skills Training (PCST) and response-based dose adaptation can improve pain management in women with breast cancer. Participants (N = 327) had stage I-IIIC breast cancer and a worst pain score of >5/10. Pain severity (a priori primary outcome) was assessed before initial randomization (1:1 allocation) to PCST-Full (5 sessions) or PCST-Brief (1 session) and 5 to 8 weeks later. Responders (>30% pain reduction) were rerandomized to a maintenance dose or no dose and nonresponders (<30% pain reduction) to an increased or maintenance dose. Pain severity was assessed again 5 to 8 weeks later (assessment 3) and 6 months later (assessment 4). As hypothesized, PCST-Full resulted in greater mean percent pain reduction than PCST-Brief (M [SD] = -28.5% [39.6%] vs M [SD]= -14.8% [71.8%]; P = 0.041). At assessment 3 after second dosing, all intervention sequences evidenced pain reduction from assessment 1 with no differences between sequences. At assessment 4, all sequences evidenced pain reduction from assessment 1 with differences between sequences (P = 0.027). Participants initially receiving PCST-Full had greater pain reduction at assessment 4 (P = 0.056). Varying PCST doses led to pain reduction over time. Intervention sequences demonstrating the most durable decreases in pain reduction included PCST-Full. Pain Coping Skills Training with intervention adjustment based on response can produce sustainable pain reduction.

Diagnosing Pain in Individuals with Intellectual and Developmental Disabilities: Current State and Novel Technological Solutions

Abstract: Pain assessment poses a challenge in several groups of clients, yet specific barriers arise when it comes to pain assessment of individuals with intellectual and developmental disabilities (IDD), due mostly to communication challenges preventing valid and reliable self-reports. Despite increased interest in pain assessment of those diagnosed with IDD within recent years, little is known about pain behavior in this group. The present article overviews the current state of pain diagnosis for individuals with IDD, focusing on existing pain assessment scales. In addition, it suggests technological developments offering new ways to diagnose existence of pain in this population, such as a Smartphone App for caregivers based on unique acoustic characteristics of pain-related vocal responses, or the use of smart wearable shirts that enable continuous surveillance of vital physiological signs. Such novel technological solutions may improve diagnosis of pain in the IDD population, as well as in other individuals with complex communication needs, to provide better pain treatment and enhance overall quality of life.

A Systematic Review of the Measurement Properties of Face, Legs, Activity, Cry and Consolability Scale for Pediatric Pain Assessment

Abstract: We performed this systematic review to summarize the psychometric properties of Face, Legs, Activity, Cry and Consolability (FLACC) scale in pediatric patients in different settings.Two investigators independently searched PubMed, EMBASE, OVID and China National Knowledge Infrastructure (CNKI) for eligible studies through July 2021. We assessed the psychometric properties using the modified critical appraisal tool (CAT). Finally, we systematically reviewed the results of the included studies.A total of 15 studies were eventually included. The overall quality of each eligible study was low to moderate. The FLACC scale has been available in different versions and in different settings. Although eligible studies have demonstrated significant clinical benefit in assessing postoperative pain in pediatric patients aged 0 to 10 years from post-anesthetic care unit (PACU), pediatric intensive care unit (PICU) and inpatient unit, and in assessing procedural pain in pediatric patients aged 0.5 to 7 years from emergency unit, immunization center and PICU, mostly without test-retest analysis.Although the absence of a gold standard of pain assessment, the currently available data support the usefulness of the FLACC from the perspective of criterion validity. Therefore, the FLACC scale can be considered for measuring observational pain in infants and children. However, further studies are still needed to provide more robust evidence.

Neonatal pain assessment: Do we have the right tools?

Abstract: Background The assessment and management of neonatal pain is crucial for the development and wellbeing of vulnerable infants. Specifically, neonatal pain is associated with adverse health outcomes but is often under-identified and therefore under-treated. Neonatal stress may be misinterpreted as pain and may therefore be treated inappropriately. The assessment of neonatal pain is complicated by the non-verbal status of patients, age-dependent variation in pain responses, limited education on identifying pain in premature infants, and the clinical utility of existing tools. Objective We review research surrounding neonatal pain assessment scales currently in use to assess neonatal pain in the neonatal intensive care unit. Methods We performed a systematic review of original research using PRISMA guidelines for literature published between 2016 and 2021 using the key words “neonatal pain assessment” in the databases Web of Science, PubMed, and CINAHL. Fifteen articles remained after review, duplicate, irrelevant, or low-quality articles were eliminated. Results We found research evaluating 13 neonatal pain scales. Important measurement categories include behavioral parameters, physiological parameters, continuous pain, acute pain, chronic pain, and the ability to distinguish between pain and stress. Provider education, inter-rater reliability and ease of use are important factors that contribute to an assessment tool's success. Each scale studied had strengths and limitations that aided or hindered its use for measuring neonatal pain in the neonatal intensive care unit, but no scale excelled in all areas identified as important for reliably identifying and measuring pain in this vulnerable population. Conclusion A more comprehensive neonatal pain assessment tool and more provider education on differences in pain signals in premature neonates may be needed to increase the clinical utility of pain scales that address the different aspects of neonatal pain.

People Living With Dementia: Dementia Characteristics and Family Caregiver Pain Assessment.

Abstract: People living with dementia (PLWD) experience pain like other older adults, but with changes due to dementia, they rely more on family caregivers for pain assessment. Many different elements contribute to a pain assessment. Changes in characteristics of PLWD may be associated with changes in the use of these different pain assessment elements. The current study reports associations between PLWD's agitation, cognitive function, and dementia severity and the frequency with which family caregivers use pain assessment elements. In a sample of family caregivers (N = 48), statistically significant associations were found between worsening cognitive function and greater use of rechecking for pain after intervention (rho = 0.36, p = 0.013), and between lower cognitive scores on a subscale of dementia severity and asking others if they have noticed a behavior change in the PLWD (rho = 0.30, p = 0.044). Limited statistically significant associations suggest that, overall, family caregivers of PLWD do not use pain assessment elements more frequently with changes in characteristics of PLWD. [Journal of Gerontological Nursing, 49(7), 17-23.].

Reliability and Validity of UNESP-Botucatu Cattle Pain Scale and Cow Pain Scale in Bos taurus and Bos indicus Bulls to Assess Postoperative Pain of Surgical Orchiectomy

Abstract: Simple Summary This study aimed to investigate the reliability and validity of the UNESP-Botucatu cattle pain scale (UCAPS) and the cow pain scale (CPS) for postoperative pain assessment in Bos taurus (Angus) and Bos indicus (Nelore) bulls undergoing general anaesthesia and castration. Video recording performed for 3 min at five different time points (M0 and M1 preoperative; M2, M3 and M4 postoperative), resulting in 95 randomised videos, were assessed by two evaluators in two phases. The pain was assessed with UCAPS, CPS, a numerical rating scale (NRS) and a visual analogue scale (VAS). There were no significant differences in the scores of any scale between breeds. Intra- and inter-rater reliability varied from good (>0.70) to very good (>0.81). The UCAPS and CPS were responsive, specific (81–85%) and sensitive (82–87%). The cut-off point for rescue analgesia was >4 for UCAPS and >3 for CPS. Both instruments are valid and reliable instruments to assess postoperative pain in Bos taurus and Bos indicus bulls. The defined cut-off point of both scales can guide decision-making for rescue analgesia. Abstract Pain assessment guides decision-making in pain management and improves animal welfare. We aimed to investigate the reliability and validity of the UNESP-Botucatu cattle pain scale (UCAPS) and the cow pain scale (CPS) for postoperative pain assessment in Bos taurus (Angus) and Bos indicus (Nelore) bulls after castration. Methods: Ten Nelore and nine Angus bulls were anaesthetised with xylazine–ketamine–diazepam–isoflurane–flunixin meglumine. Three-minute videos were recorded at -48 h, preoperative, after surgery, after rescue analgesia and at 24 h. Two evaluators assessed 95 randomised videos twice one month apart. Results: There were no significant differences in the pain scores between breeds. Intra and inter-rater reliability varied from good (>0.70) to very good (>0.81) for all scales. The criterion validity showed a strong correlation (0.76–0.78) between the numerical rating scale and VAS versus UCAPS and CPS, and between UCAPS and CPS (0.76). The UCAPS and CPS were responsive; all items and total scores increased after surgery. Both scales were specific (81–85%) and sensitive (82–87%). The cut-off point for rescue analgesia was >4 for UCAPS and >3 for CPS. Conclusions. The UCAPS and CPS are valid and reliable to assess postoperative pain in Bos taurus and Bos indicus bulls.

Chronic pain interference assessment tools for children and adults who are unable to self‐report: A systematic review of psychometric properties

Abstract: To identify and evaluate psychometric properties of assessment tools for assessing pain interference in children, adolescents, and adults with chronic pain and the inability to self‐report.

Explainable automated pain recognition in cats

Abstract: Abstract Manual tools for pain assessment from facial expressions have been suggested and validated for several animal species. However, facial expression analysis performed by humans is prone to subjectivity and bias, and in many cases also requires special expertise and training. This has led to an increasing body of work on automated pain recognition, which has been addressed for several species, including cats. Even for experts, cats are a notoriously challenging species for pain assessment. A previous study compared two approaches to automated ‘pain’/‘no pain’ classification from cat facial images: a deep learning approach, and an approach based on manually annotated geometric landmarks, reaching comparable accuracy results. However, the study included a very homogeneous dataset of cats and thus further research to study generalizability of pain recognition to more realistic settings is required. This study addresses the question of whether AI models can classify ‘pain’/‘no pain’ in cats in a more realistic (multi-breed, multi-sex) setting using a more heterogeneous and thus potentially ‘noisy’ dataset of 84 client-owned cats. Cats were a convenience sample presented to the Department of Small Animal Medicine and Surgery of the University of Veterinary Medicine Hannover and included individuals of different breeds, ages, sex, and with varying medical conditions/medical histories. Cats were scored by veterinary experts using the Glasgow composite measure pain scale in combination with the well-documented and comprehensive clinical history of those patients; the scoring was then used for training AI models using two different approaches. We show that in this context the landmark-based approach performs better, reaching accuracy above 77% in pain detection as opposed to only above 65% reached by the deep learning approach. Furthermore, we investigated the explainability of such machine recognition in terms of identifying facial features that are important for the machine, revealing that the region of nose and mouth seems more important for machine pain classification, while the region of ears is less important, with these findings being consistent across the models and techniques studied here.

Explainable Automated Pain Recognition in Cats

Abstract: Abstract Manual tools for pain assessment from facial expressions have been suggested and validated for several animal species. However, facial expression analysis performed by humans is prone to subjectivity and bias, and in many cases also requires special expertise and training. This has led to an increasing body of work on automated pain recognition, which has been addressed for several species, including cats. Even for experts, cats are a notoriously challenging species for pain assessment. A previous study compared two approaches to automated ‘pain’/‘no pain’ classification from cat facial images: a deep learning approach, and an approach based on manually annotated geometric landmarks, reaching comparable accuracy results. However, the study included a very homogeneous dataset of cats and thus further research to study generalizability of pain recognition to more realistic settings is required. This study addresses the question of whether AI models can classify ‘pain’/‘no pain’ in cats in a more realistic (multi-breed, multi-sex) setting using a more heterogenous and thus potentially ‘noisy’ dataset of 84 client-owned cats. Cats were a convenience sample presented to the Department of Small Animal Medicine and Surgery of the University of Veterinary Medicine Hannover and included individuals of different breeds, ages, sex, and with varying medical conditions/medical histories. Cats were scored by veterinary experts using the Glasgow composite measure pain scale; the scoring was then used for training AI models using two different approaches. We show that in this context the landmark-based approach performs better, reaching accuracy above 77% in pain detection as opposed to only above 65% reached by the deep learning approach. Furthermore, we investigated the explainability of such machine recognition in terms of identifying facial features that are important for the machine, revealing that the region of nose and mouth seems more important for machine pain classification, while the region of ears is less important, with these findings being consistent across the models and techniques studied here.

Factors associated with pain reporting and severity of pain in the prehospital setting

Abstract: An effective pain management strategy requires understanding of the epidemiology of pain in the population of interest and accurate measurement upon which to base quality improvement plans. The aims of this study were to estimate the incidence of pain in the prehospital setting and to explore features that impact on (1) documentation of pain; (2) severity of pain reported by patients. This retrospective cohort study included 212,401 care episodes attended by National Ambulance Service practitioners during 2020. Descriptive analysis of patient and care episode characteristics and regression analyses for the outcomes pain recorded and severity of pain were performed. We also used text pattern-matching of the notes field to estimate the proportion of patients in pain for whom a pain score assessment had not been documented. Sixty-five percent of all patients had a pain score documented and 29.5% were in pain (11% in severe pain). Likelihood of pain being recorded was most strongly associated with: Glasgow Coma Scale (GCS) Score, working diagnosis of the patient, location of the incident, and patient age. Likelihood of pain severity was most strongly associated with: transport status of patient, GCS score, and patient age. We treated missing data as a separate category and found consistent associations between the outcomes and missing data. We also found that pain was a symptom in approximately 15% of cases where no formal pain score assessment was documented. The data showed associations between routinely collected variables and the likelihood of pain recording and pain severity. Our findings also demonstrate the impact of missing data. To mitigate missing data impact, we suggest that EMS agencies consider making pain score assessment a mandatory requirement of their reporting for every patient. We also recommend that services report the extent and impact of missing data when measuring clinical performance.

Development and validation of a cynomolgus macaque grimace scale for acute pain assessment

Abstract: Cynomolgus macaques may undergo surgical procedures for scientific and veterinary purposes. Recognition and assessment of pain using validated tools is a necessary first step for adequately managing pain in these primates. Grimace scales are one means of assessing the occurance of acute pain using action units such as facial expressions and posture. The aim of this study was to create and validate a Cynomolgus Macaque Grimace Scale (CMGS). Cynomolgus macaques (n = 43) were video recorded before and after a surgical procedure. Images were extracted from videos at timepoints at which breakthrough pain might be expected based on analgesic pharmacokinetics. Using the CMGS images were scored by 12 observers blinded to animal identification, times, and conditions. To validate the tool, detailed behavioral analyses emphasizing changes to baseline activity ethograms were compared to grimace scores. Four action units were identified related to potential pain including orbital tightening, brow lowering, cheek tightening, and hunched posture. The CMGS tool was found to have moderate inter- (ICCaverage action unit mean ± SD: 0.67 ± 0.28) and good intra- (ICCsingle mean ± SD: 0.79 ± 0.14) observer reliability. Grimace scores increased significantly (p < 0.0001) in the first four post-operative timepoints compared to baseline, correlating with behavioral findings (rho range = 0.22-0.35, p < 0.001). An analgesic intervention threshold was determined and should be considered when providing additional pain relief. The CMGS was shown to be a reliable and valid tool; however, more research is needed to confirm external validity. This tool will be highly valuable for refining analgesic protocols and acute peri-procedural care for cynomolgus macaques.

Psychometric evaluation of the electronic faces thermometer scale for pain assessment in children 8–17 years old: A study protocol

Abstract: It is often a challenge for a child to communicate their pain, and their possibilities to do so should be strengthened in healthcare settings. Digital self-assessment provides a potential solution for person-centered care in pain management and promotes child participation when a child is ill. A child's perception of pain assessment differs when it is assessed using digital or analog formats. As we move into the digital era, there is an urgent need to validate digital pain assessment tools, including the newly developed electronic Faces Thermometer Scale (eFTS). This study protocol describes three studies with the overall aim to evaluate psychometric properties of the eFTS for assessing pain in children 8–17 years of age. A multi-site project design combining quantitative and qualitative methods will be used for three observational studies. Study 1: 100 Swedish-speaking children will report the level of anticipated pain from vignettes describing painful situations in four levels of pain and a think-aloud method will be used for data collection. Data will be analyzed with phenomenography as well as descriptive and comparative statistics. Study 2: 600 children aged 8–17 years at pediatric and dental settings in Sweden, Denmark, Iceland, and USA will be included. Children will assess their pain intensity due to medical or dental procedures, surgery, or acute pain using three different pain Scales for each time point; the eFTS, the Faces Pain Scale Revised, and the Coloured Analogue Scale. Descriptive and comparative statistics will be used, with subanalysis taking cultural context into consideration. Study 3: A subgroup of 20 children out of these 600 children will be purposely included in an interview to describe experiences of grading their own pain using the eFTS. Qualitative data will be analyzed with content analysis. Our pilot studies showed high level of adherence to the study procedure and rendered only a small revision of background questionnaires. Preliminary analysis indicated that the instruments are adequate to be used by children and that the analysis plan is feasible. A digital pain assessment tool contributes to an increase in pain assessment in pediatric care. The Medical Research Council framework for complex interventions in healthcare supports a thorough development of a new scale. By evaluating psychometric properties in several settings by both qualitative and quantitative methods, the eFTS will become a well-validated tool to strengthen the child's voice within healthcare.

Technology-guided assessment of vocalisations and their diagnostic value as pain indicators for people living with dementia

Abstract: Abstract Background during pain assessment in persons unable to self-report, such as people living with dementia, vocalisations are commonly used as pain indicators. However, there is a lack of evidence from clinical practice regarding their diagnostic value and relationship with pain. We aimed to explore vocalisations and pain in people with dementia undergoing pain assessments in clinical practice settings. Methods a total of 22,194 pain assessments were reviewed in people with dementia (n = 3,144) from 34 different Australian aged care homes and two dementia specific programs. Pain assessments were conducted by 389 purposely trained health care professionals and cares using PainChek pain assessment tool. Vocalised expressions were determined based on nine vocalisation features included in the tool. Linear mixed models were used to examine the relationship of pain scores with vocalisation features. Using a single pain assessment for each of the 3,144 people with dementia, additional data analysis was conducted via Receiver Operator Characteristic (ROC) analysis and Principal Component Analysis. Results vocalisation scores increased with increasing pain intensity. High pain scores were more likely with the presence of sighing and screaming (8 times). The presence of vocalisation features varied depending on the intensity of pain. The ROC optimal criterion for the voice domain yielded a cut-off score of ≥2.0 with a Youden index of 0.637. The corresponding sensitivity and specificity were 79.7% [confidence interval (CI): 76.8–82.4%] and 84.0% (CI: 82.5–85.5%), respectively. Conclusion we describe vocalisation features during presence of different levels of pain in people with dementia unable to self-report, therefore providing evidence in regard to their diagnostic value in clinical practice.

Pain assessment and management for people with dementia in the community.

Abstract: People with dementia commonly experience pain, but it is often unrecognised, unrelieved and remains an underlying issue as the condition progresses. As a result, pain management for people with dementia is inadequate. Community nurses have a fundamental role in the assessment and management of pain and in supporting family carers. This article details the causes and effects of pain in people with dementia, explains the components of a holistic approach to individualised pain assessment, and describes various pharmacological and non-pharmacological interventions that can be used to manage pain in this population.

A Multicenter Collaborative to Improve Postoperative Pain Management in the NICU.

Abstract: OBJECTIVES This quality improvement initiative aimed to decrease unrelieved postoperative pain and improve family satisfaction with pain management. METHODS NICUs within the Children's Hospitals Neonatal Consortium that care for infants with complex surgical problems participated in this collaborative. Each of these centers formed multidisciplinary teams to develop aims, interventions, and measurement strategies to test in multiple Plan-Do-Study-Act cycles. Centers were encouraged to adopt evidence-based interventions from the Clinical Practice Recommendations, which included pain assessment tools, pain score documentation, nonpharmacologic treatment measures, pain management guidelines, communication of a pain treatment plan, routine discussion of pain scores during team rounds, and parental involvement in pain management. Teams submitted data on a minimum of 10 surgeries per month, spanning from January to July 2019 (baseline), August 2019 to June 2021 (improvement work period), and July 2021 to December 2021 (sustain period). RESULTS The percentage of patients with unrelieved pain in the 24-hour postoperative period decreased by 35% from 19.5% to 12.6%. Family satisfaction with pain management measured on a 3-point Likert scale with positive responses ≥2 increased from 93% to 96%. Compliance with appropriate pain assessment and numeric documentation of postoperative pain scores according to local NICU policy increased from 53% to 66%. The balancing measure of the percentage of patients with any consecutive sedation scores showed a decrease from 20.8% at baseline to 13.3%. All improvements were maintained during the sustain period. CONCLUSIONS Standardization of pain management and workflow in the postoperative period across disciplines can improve pain control in infants.

An Automated Rat Grimace Scale for the Assessment of Pain

Abstract: Abstract Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81-93%. The system’s performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS.

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

Abstract: Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research.

Development and Validation of the Dystonia-Pain Classification System: A Multicenter Study.

Abstract: BACKGROUND Dystonia is associated with disabling nonmotor symptoms like chronic pain (CP), which is prevalent in dystonia and significantly impacts the quality of life (QoL). There is no validated tool for assessing CP in dystonia, which substantially hampers pain management. OBJECTIVE The aim was to develop a CP classification and scoring system for dystonia. METHODS A multidisciplinary group was established to develop the Dystonia-Pain Classification System (Dystonia-PCS). The classification of CP as related or unrelated to dystonia was followed by the assessment of pain severity score, encompassing pain intensity, frequency, and impact on daily living. Then, consecutive patients with inherited/idiopathic dystonia of different spatial distribution were recruited in a cross-sectional multicenter validation study. Dystonia-PCS was compared to validated pain, mood, QoL, and dystonia scales (Brief Pain Inventory, Douleur Neuropathique-4 questionnaire, European QoL-5 Dimensions-3 Level Version, and Burke-Fahn-Marsden Dystonia Rating Scale). RESULTS CP was present in 81 of 123 recruited patients, being directly related to dystonia in 82.7%, aggravated by dystonia in 8.8%, and nonrelated to dystonia in 7.5%. Dystonia-PCS had excellent intra-rater (Intraclass Correlation Coefficient - ICC: 0.941) and inter-rater (ICC: 0.867) reliability. In addition, pain severity score correlated with European QoL-5 Dimensions-3 Level Version's pain subscore (r = 0.635, P < 0.001) and the Brief Pain Inventory's severity and interference scores (r = 0.553, P < 0.001 and r = 0.609, P < 0.001, respectively). CONCLUSIONS Dystonia-PCS is a reliable tool to categorize and quantify CP impact in dystonia and will help improve clinical trial design and management of CP in patients affected by this disorder. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Deep learning-guided postoperative pain assessment in children.

Abstract: ABSTRACT Current automated pain assessment methods only focus on infants or youth. They are less practical because the children who suffer from postoperative pain in clinical scenarios are in a wider range of ages. In this article, we present a large-scale Clinical Pain Expression of Children (CPEC) dataset for postoperative pain assessment in children. It contains 4104 preoperative videos and 4865 postoperative videos of 4104 children (from 0 to 14 years of age), which are collected from January 2020 to December 2020 in Anhui Provincial Children's Hospital. Moreover, inspired by the dramatic successful applications of deep learning in medical image analysis and emotion recognition, we develop a novel deep learning-based framework to automatically assess postoperative pain according to the facial expression of children, namely Children Pain Assessment Neural Network (CPANN). We train and evaluate the CPANN with the CPEC dataset. The performance of the framework is measured by accuracy and macro-F1 score metrics. The CPANN achieves 82.1% accuracy and 73.9% macro-F1 score on the testing set of CPEC. The CPANN is faster, more convenient, and more objective compared with using pain scales according to the specific type of pain or children's condition. This study demonstrates the effectiveness of deep learning-based method for automated pain assessment in children.

AB163. SOH23ABS_050. Digital delivery of pain assessment education in the hospital setting—breaking barriers to enhance skills

Abstract: Background: Audit of nursing documentation found that standards on pain assessment were not being met when measured against national quality care metrics. Face-to-face supportive education was impacting on staff and clinical nurse specialist (CNS) workload. Further education was needed to support nursing staff of the 12 identified units. The aim of the study was to identify the effectiveness of the delivery of supportive pain assessment education utilizing a digital platform in the hospital setting.