Showing papers on "Pain assessment published in 2022"

Procedural Pain Assessment in Infants Without Analgosedation: Comparison of Newborn Infant Parasympathetic Evaluation and Skin Conductance Activity - A Pilot Study

Abstract: Objective: New technologies to measure pain responses, such as heart rate variability and skin conductance hold promise in the development of tools that can be reliable and quantifiable of detecting pain. The main objective of this study was to assess the capability of two monitors i.e., Newborn Infant Parasympathetic Evaluation (NIPE) and Skin Conductance Algesimeter for detecting procedural pain in non-anesthetized infants. Materials and Methods: Thirty-three non-anesthetized infants were enrolled to the study. To detect pain caused by heel stick, NIPE, and Skin Conductance monitors and behavioral pain scales were used. Three minutes before and just after heel stick, pain was evaluated by behavioral scales, and simultaneously over the whole period by NIPE and SCA. Results: A statistically significant decrease of NIPE Index and an increase of SCA values were found after the HS procedure. There were no statistically significant differences between the decrease in NIPEi values and the increase in PPS values between subgroups based on pain assessment by behavioral-scale scores. Conclusion: Both NIPE and SCA can be useful for detection of procedural pain and may constitue an additional valuable tool for better handling of pain among patients treated in NICUs. More studies on larger groups of patients are needed.

Automated Pain Assessment in Children Using Electrodermal Activity and Video Data Fusion via Machine Learning

Abstract: Pain assessment in children continues to challenge clinicians and researchers, as subjective experiences of pain require inference through observable behaviors, both involuntary and deliberate. The presented approach supplements the subjective self-report-based method by fusing electrodermal activity (EDA) recordings with video facial expressions to develop an objective pain assessment metric. Such an approach is specifically important for assessing pain in children who are not capable of providing accurate self-pain reports, requiring nonverbal pain assessment. We demonstrate the performance of our approach using data recorded from children in post-operative recovery following laparoscopic appendectomy. We examined separately and combined the usefulness of EDA and video facial expression data as predictors of children's self-reports of pain following surgery through recovery. Findings indicate that EDA and facial expression data independently provide above chance sensitivities and specificities, but their fusion for classifying clinically significant pain vs. clinically nonsignificant pain achieved substantial improvement, yielding 90.91% accuracy, with 100% sensitivity and 81.82% specificity. The multimodal measures capitalize upon different features of the complex pain response. Thus, this paper presents both evidence for the utility of a weighted maximum likelihood algorithm as a novel feature selection method for EDA and video facial expression data and an accurate and objective automated classification algorithm capable ofdiscriminating clinically significant pain from clinically nonsignificant pain in children.

A Comprehensive and Context-Sensitive Neonatal Pain Assessment Using Computer Vision

Abstract: Infants receiving care in the Neonatal Intensive Care Unit (NICU) experience several painful procedures during their hospitalization. Assessing neonatal pain is difficult because the current standard for assessment is subjective, inconsistent, and discontinuous. The intermittent and inconsistent assessment can induce poor treatment and, therefore, cause serious long-term outcomes. In this paper, we present a comprehensive pain assessment system that utilizes facial expressions along with crying sounds, body movement, and vital sign changes. The proposed automatic system generates a standardized pain assessment comparable to those obtained by conventional nurse-derived pain scores. The system achieved 95.56 percent accuracy using decision fusion of different pain responses that were recorded in a challenging clinical environment. In addition to the decision fusion, we present the performance of multimodal assessment using other fusion schemes as well as a unimodal assessment approach. We also discuss the impact of different factors (e.g., gestational age) on pain, propose several group-specific models for pain assessment (e.g., pre-term and full-term models), and compare the performance of these models with the performance of general models. While further research is needed, our results show that the automatic assessment of neonatal pain is a viable and more efficient alternative to the manual assessment.

Clinical rating scales for assessing pain in newborn infants

Abstract: Objectives This is a protocol for a Cochrane Review (methodology). The objectives are as follows: To systematically review the current literature describing the development, content, and measurement properties of clinical rating scales for the assessment of pain in newborn infants.

Challenges with Assessing and Treating Pain in Research Primates: A Focused Survey and Literature Review

Abstract: Simple Summary It is crucial that research primates receive adequate pain treatment from ethical, animal welfare, and research-related perspectives. There is limited research on current pain management in research primates. A survey was administered to primate veterinarians (n = 93 respondents) to investigate a veterinary approach to pain recognition and alleviation as well as the pain management challenges that primate veterinarians may face. Survey results were used to guide a subsequent literature review on the topic. This review discusses current evidence and challenges in research primate pain management such as limited pharmacokinetic data and efficacy testing as well as a lack of validated pain assessment tools to recognize and evaluate pain in primates. Both the survey and literature review demonstrate gaps and challenges in primate pain management, and suggest science-based recommendations for improving current management guidance as well as future areas of research. Abstract Research primates may undergo surgical procedures making effective pain management essential to ensure good animal welfare and unbiased scientific data. Adequate pain mitigation is dependent on whether veterinarians, technicians, researchers, and caregivers can recognize and assess pain, as well as the availability of efficacious therapeutics. A survey was conducted to evaluate primate veterinary approaches to pain assessment and alleviation, as well as expressed challenges for adequately managing primate pain. The survey (n = 93 respondents) collected information regarding institutional policies and procedures for pain recognition, methods used for pain relief, and perceived levels of confidence in primate pain assessment. Results indicated that 71% (n = 60) of respondents worked at institutions that were without formal experimental pain assessment policies. Pain assessment methods were consistent across respondents with the majority evaluating pain based on changes in general activity levels (100%, n = 86) and food consumption (97%, n = 84). Self-reported confidence in recognizing and managing pain ranged from slightly confident to highly confident, and there was a commonly expressed concern about the lack of objective pain assessment tools and science-based evidence regarding therapeutic recommendations of analgesics for research primates. These opinions correspond with significant gaps in the primate pain management literature, including limited specific pharmacokinetic data and efficacy testing for commonly used analgesics in research primate species as well as limited research on objective and specific measures of pain in research primates. These results demonstrate that there are inconsistencies in institutional policies and procedures surrounding pain management in research primates and a lack of objective pain assessment methods. Demonstrating the gaps and challenges in primate pain management can inform guideline development and suggest areas for future research.

Technological Interventions for Pain Management

Abstract: Information and communication technologies are shaping the way health is managed. The present chapter will summarize the state of the art in relation to technology use for pain assessment and treatment and will include a review of the most recent advances in the field (e.g., the use of mixed realities, online interventions, smartphone applications, and wearable devices), as well as a discussion of future research directions.

Acute postoperative pain after orthognathic surgery can be predicted by the preoperative evaluation of conditioned pain modulation and pain catastrophizing

Abstract: The incidence and severity of chronic postoperative pain (POP) are major clinical challenges, and presurgical conditioned pain modulation (CPM) and pain catastrophizing scale (PCS) assessments have exhibited predictive values for POP. However, whether CPM and PCS assessments are also predictive of acute POP is unknown.We aimed to investigate the relationship between preoperative CPM and PCS and acute POP severity after orthognathic surgery by assessing preoperative CPM and PCS in 43 patients.The pressure pain threshold and tonic painful cold-heat pulse stimulation (applied with a pain intensity score of 70 on a visual analogue scale [VAS 0-100]) were used as the test and conditioning stimuli, respectively. The pain area under the postoperative VAS area under the curve (VASAUC) was estimated. The associations between CPM, PCS, and VASAUC were also analyzed.No patient experienced chronic POP after 1 month. Negative and positive CPM effects (test stimulus threshold was 0% > and 0% ≤ during conditioning stimulation, respectively) were detected in 36 and 7 patients, respectively. For patients with negative CPM effects (CPM responders), multiple regression analysis revealed a prediction formula of log (VASAUC) = (-0.02 × CPM effect) + (0.13 × PCS-magnification) + 5.10 (adjusted R2 = 0.4578, P = 0.00002, CPM effect; P = 0.002, PCS-magnification; P = 0.0004), indicating that a weaker CPM and higher PCS scores were associated with more acute POP after surgery.CPM and PCS can predict acute POP after orthognathic surgery.

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients

Abstract: Objective Pain assessment based on facial expressions is an essential issue in critically ill patients, but an automated assessment tool is still lacking. We conducted this prospective study to establish the deep learning-based pain classifier based on facial expressions. Methods We enrolled critically ill patients during 2020–2021 at a tertiary hospital in central Taiwan and recorded video clips with labeled pain scores based on facial expressions, such as relaxed (0), tense (1), and grimacing (2). We established both image- and video-based pain classifiers through using convolutional neural network (CNN) models, such as Resnet34, VGG16, and InceptionV1 and bidirectional long short-term memory networks (BiLSTM). The performance of classifiers in the test dataset was determined by accuracy, sensitivity, and F1-score. Results A total of 63 participants with 746 video clips were eligible for analysis. The accuracy of using Resnet34 in the polychromous image-based classifier for pain scores 0, 1, 2 was merely 0.5589, and the accuracy of dichotomous pain classifiers between 0 vs. 1/2 and 0 vs. 2 were 0.7668 and 0.8593, respectively. Similar accuracy of image-based pain classifier was found using VGG16 and InceptionV1. The accuracy of the video-based pain classifier to classify 0 vs. 1/2 and 0 vs. 2 was approximately 0.81 and 0.88, respectively. We further tested the performance of established classifiers without reference, mimicking clinical scenarios with a new patient, and found the performance remained high. Conclusions The present study demonstrates the practical application of deep learning-based automated pain assessment in critically ill patients, and more studies are warranted to validate our findings.

The Analysis of Pain Research through the Lens of Artificial Intelligence and Machine Learning.

Abstract: Traditional pain assessment methods have significant limitations due to the high variability in patient reported pain scores and perception of pain by different individuals. There is a need for generalized and automatic pain detection and recognition methods. In this paper, state-of-the-art machine learning (ML) and deep learning methods in this field are analyzed as well as pain management techniques.The objective of the study is to analyze the current use of artificial intelligence (AI) and ML in the analysis and management of pain and to disseminate this knowledge prompting future utilization by medical professionals.A narrative review of the literature focusing on the latest algorithms in AI and ML for pain assessment and management.Research studies were collected using a literature search on PubMed, Science Direct and IEEE Xplore between 2018 and 2020.The results of our assessment resulted in the identification of 47 studies meeting inclusion criteria. Pain assessment was the most studied subject with 11 studies, followed by automated measurements with 10 studies, spinal diagnosis with 8 studies, facial expression with 7 studies, pain assessment in special settings evaluated in 5 studies, 4 studies described treatment algorithms, and 2 studies assessed neonatal pain. These studies varied from simple to highly complex methodology. The majority of the studies suffered from inclusion of a small number of patients and without replication of results. However, considering AI and ML are dynamic and emerging specialties, the results shown here are promising. Consequently, we have described all the available literature in summary formats with commentary. Among the various assessments, facial expression and spinal diagnosis and management appear to be ready for inclusion as we continue to progress.This review is not a systematic review of ML and AI applications in pain research. This review only provides a general idea of the upcoming techniques but does not provide an authoritative evidence-based conclusive opinion of their clinical application and effectiveness.While a majority of the studies focused on classification tasks, very few studies have explored the diagnosis and management of pain. Usage of ML techniques as support tools for clinicians holds an immense potential in the field of pain management.

Faces of Pain in Dementia: Learnings From a Real-World Study Using a Technology-Enabled Pain Assessment Tool

Abstract: Pain is common in people living with dementia (PLWD), including those with limited verbal skills. Facial expressions are key behavioral indicators of the pain experience in this group. However, there is a lack of real-world studies to report the prevalence and associations of pain-relevant facial micro-expressions in PLWD. In this observational retrospective study, pain-related facial features were studied in a sample of 3,144 PLWD [mean age 83.3 years (SD = 9.0); 59.0% female] using the Face domain of PainChek®, a point-of-care medical device application. Pain assessments were completed by 389 users from two national dementia-specific care programs and 34 Australian aged care homes. Our analysis focused on the frequency, distribution, and associations of facial action units [AU(s)] with respect to various pain intensity groups. A total of 22,194 pain assessments were completed. Of the AUs present, AU7 (eyelid tightening) was the most frequent facial expression (48.6%) detected, followed by AU43 (closing eyes; 42.9%) and AU6 (cheek raising; 42.1%) during severe pain. AU20 (horizontal mouth stretch) was the most predictive facial action of higher pain scores. Eye-related AUs (AU6, AU7, AU43) and brow-related AUs (AU4) were more common than mouth-related AUs (e.g., AU20, AU25) during higher pain intensities. No significant effect was found for age or gender. These findings offer further understanding of facial expressions during clinical pain in PLWD and confirm the usefulness of artificial intelligence (AI)-enabled real-time analysis of the face as part of the assessment of pain in aged care clinical practice.

The practice of procedural pain assessment and management in neonatal intensive care unit in Ethiopia: Cross‐sectional study

Abstract: Neonates in intensive care units undergo frequent painful procedures for diagnostic or care‐related purposes. Untreated pain has serious short‐term and long‐term complications. This study aims to evaluate the frequency of painful procedures, pain assessment, and their analgesic management practice among neonates admitted to the NICU.

Improving pain management in a neonatal intensive care unit with single‐family room—A quality improvement project

Abstract: Preterm birth is a risk factor for early experience of pain. Despite advances in neonatal care, evidence‐based knowledge of the importance of adequate pain management and strong international guidelines for assessment and treatment of neonatal pain, only 10% of sick term and preterm infants were assessed for pain and stress on a daily basis. The aim of this quality improvement (QI) project is evaluation of implemented guidelines for pain assessment and management, and increased parental involvement in a Norwegian single‐family room NICU. Method: The different steps of the project entailed translation of the English version of COMFORTneo, development and implementation of guidelines with flowcharts for pain management, and pain assessment certification of the interprofessional staff. Part two of the project is supervision of the interprofessional staff in parental involvement in stress‐ and painful procedures. Our study showed that one year after implementation, 88.8% of the COMFORTneo assessments were performed according to the pain management guidelines. The staff used the flowcharts to assess, treat and reassess pain and stress. There was a high interrater reliability with linearly weighted Cohen's kappa values ranging from 0.81 to 0.95, with a median of 0.90. In addition, our study showed increased parental involvement in procedures, from 50.3% before to 82.3% after the quality improvement project. The success of this quality improvement project is explained by systematic use of flowcharts and implemented guidelines for pain management, interprofessional collaboration, and cultural change agents. Theoretical lectures and practical bedside supervision to interprofessional staff increased parental involvement in stress‐ and painful procedures.

Acute pain assessments and records: a pilot study of digital transformation.

Abstract: BACKGROUND Pain is a leading cause of disability worldwide. Pain assessments are an essential part of evidence-based care and management. Among comparable care providers, there is variation in how nurses document assessments as well as the content in them, and there is a notable associated administrative burden. AIMS This study evaluated the impact and significance of a new, structured, digitised pain assessment form from quality, safety and efficiency standpoints. METHODS Samples of pain assessments were examined at three consecutive stages: first, the pre-existing form was used, then the new structured form was introduced and, finally, the structured form was taken away and nurses went back to completing the original form. Assessments were scored by two clinical analysts against 18 clinically defined pain-related characteristics and factors. The time taken to extract and interpret the assessments was also recorded. Statistically significant changes were assessed using Welch's t-tests and Fisher's exact tests. FINDINGS There was a significant improvement in data quality using the new structured form compared with the pre-existing template, including an increase in the capture of five safety-related variables. Less time was needed to extract and interpret data with the new form. CONCLUSION Intelligent structured forms are highly effective for documenting pain assessments, and offer notable benefits in quality, safety, and efficiency.

Diagnostic Values of the Critical Care Pain Observation Tool and the Behavioral Pain Scale for Pain Assessment among Unconscious Patients: A Comparative Study

Abstract: Abstract Background Pain assessment in unconscious patients is a major challenge for healthcare providers. This study aims to compare the diagnostic value of the critical-care pain observation tool (CPOT) and the behavioral pain scale (BPS) for pain assessment among unconscious patients. Materials and methods This cross-sectional study was conducted in 2019. Forty-five unconscious patients were selected randomly from four general intensive care units (ICUs) in the north of Iran. The discriminant validity of CPOT and BPS were evaluated for pain during a nociceptive and a nonnociceptive procedure. For reliability assessment, interrater agreement was obtained using Lin's concordance correlation coefficient and weighted kappa coefficient. Results Patients who had been hospitalized in ICU due to surgery or trauma (57.70%) or medical problems (42.30%) were studied. During the nociceptive procedure, the mean scores of CPOT and BPS and all their dimensions, except for the compliance with ventilator dimension, were significantly greater than the nonnociceptive procedure (p <0.05) although the effect size of both instruments was small (0.32 vs 0.18). The Lin's concordance correlation coefficient in nonnociceptive and nociceptive procedures was respectively 0.67 and 0.62 for CPOT and 0.74 and 0.88 for BPS. Conclusion CPOT and BPS have acceptable discriminant validity in differentiating nonnociceptive and nociceptive procedural pain although the effect size of CPOT is larger than that of BPS. Although both instruments have low reliability, the reliability of BPS is better. How to cite this article Nazari R, Froelicher ES, Nia HS, Hajihosseini F, Mousazadeh N. Diagnostic Values of the Critical Care Pain Observation Tool and the Behavioral Pain Scale for Pain Assessment among Unconscious Patients: A Comparative Study. Indian J Crit Care Med 2022;26(4):472–476.

Documented nursing practices of pain assessment and management when communicating about pain in dementia care

Abstract: Abstract Aim To evaluate nurses' documented practice when communicating about pain for people with dementia in hospital. Design Retrospective medical record review. Method Medical records were retrieved from four inpatient units in a district and a tertiary teaching hospital of people aged 65 years and over with documented dementia. Data were extracted on nurses' documented pain assessment and management. Pain frequency and association between patient self‐report, pain scores, cognition levels and analgesics used during hospitalization were analysed using descriptive and inferential statistics. Multivariate regression examined patient characteristics, pain characteristics and length of hospital stay. Results One‐hundred patient records met the inclusion criteria between 1 January and 31 August 2017. Sixty‐six percent of patients with dementia had pain documented at least once during hospitalization with 58% reported as moderate to severe pain intensity. Patients' pain severity during admission was associated with their length of hospital stay. Ninety‐three percent of nurses used a self‐reporting pain tool and 7% used an observational pain tool. Pain scores were not associated with patients' cognition level, nurses' pain reports or analgesic management. Conclusion Pain frequently occurs in people with dementia during hospitalization. Fragmented pain reporting influences the translation of pain messages. Disproportionate pain tool application and non‐association between pain scores and analgesic management suggest a potential knowledge gap among nurses about the practical use of pain tools and practice gap between pain assessment and management in dementia care. Impact Pain was regularly assessed by nurses and implemented as a fifth vital sign for people with dementia in hospitals. However, the high frequency of pain affects care outcomes. Areas for improvement include nursing practice of pain assessment and management in dementia care in hospitals. Further understanding of the usefulness of pain tools and the efficacy of pain scores when communicating about pain in dementia care in hospitals is required.

Congruence of pain perceptions between Black cancer patients and their family caregivers.

Abstract: This cross-sectional study evaluated congruence in pain assessment among Black cancer patients taking opioids for pain and their family caregivers and the effects of patient-reported depressive symptoms and cognitive complaints on the congruence. Patient-reported pain scores (current, average, and worst pain severity and pain interference) and caregiver proxy scores were independently assessed (Brief Pain Inventory). Patient-reported depressive symptoms (Patient Health Questionnaire-8) and cognitive complaints (Cognitive Difficulties Scale) were also assessed. Paired t-test, intraclass correlation coefficient (ICC), and Bland–Altman (BA) plots were used to evaluate group and dyad level congruence in pain assessment. The influence of patient depressive symptoms and cognitive complaints on congruence was examined using bivariate analyses and BA plots. Among 50 dyads, 62% of patients and 56% of caregivers were female. Patients were older than caregivers (57 vs. 50 years, p = .008). Neither statistically significant (t-test) nor clinically relevant mean differences in pain severity and interference were found at a group level. At the dyad level, congruence was poor in pain now (ICC = 0.343) and average pain severity (ICC = 0.435), but moderate in worst pain severity (ICC = 0.694) and pain interference (ICC = 0.603). Results indicated better congruence in pain severity between patients with depressive symptoms and their caregivers, compared to patients without depressive symptoms. Patient CDS scores had no significant correlations with score differences between patients and caregivers in any pain variables. Congruence varied depending on how the analysis was done. More information is needed to understand pain assessment between patients and caregivers.

An overview of pain assessment and management.

Abstract: Pain is a distressing, subjective and complex phenomenon that all nurses will encounter in their clinical practice. Effective pain management requires nurses to undertake a structured assessment to identify the probable causes of pain and guide management. Interventions used to reduce pain can be varied and multimodal. This article provides an overview of pain including its definition, classifications, assessment and management. It emphasises the importance of a person-centred approach to care which reflects Margo McCaffery's seminal quote that pain is 'whatever the experiencing person says it is'.

Development and testing of a standardized pain interview assessment for use in post‐acute care

Abstract: Pain is highly prevalent among patients in post‐acute care (PAC) settings and can affect quality of life, treatment outcomes, and transitions in care. Routine, accurate assessment of pain across settings is important for pain management and care planning; however, existing PAC assessment instruments do not assess patient pain in a standardized manner.

Deep Learning-Based Grimace Scoring Is Comparable to Human Scoring in a Mouse Migraine Model

Abstract: Pain assessment is essential for preclinical and clinical studies on pain. The mouse grimace scale (MGS), consisting of five grimace action units, is a reliable measurement of spontaneous pain in mice. However, MGS scoring is labor-intensive and time-consuming. Deep learning can be applied for the automatic assessment of spontaneous pain. We developed a deep learning model, the DeepMGS, that automatically crops mouse face images, predicts action unit scores and total scores on the MGS, and finally infers whether pain exists. We then compared the performance of DeepMGS with that of experienced and apprentice human scorers. The DeepMGS achieved an accuracy of 70–90% in identifying the five action units of the MGS, and its performance (correlation coefficient = 0.83) highly correlated with that of an experienced human scorer in total MGS scores. In classifying pain and no pain conditions, the DeepMGS is comparable to the experienced human scorer and superior to the apprentice human scorers. Heatmaps generated by gradient-weighted class activation mapping indicate that the DeepMGS accurately focuses on MGS-relevant areas in mouse face images. These findings support that the DeepMGS can be applied for quantifying spontaneous pain in mice, implying its potential application for predicting other painful conditions from facial images.

Nurses' Perception of Preterm Infants' Pain and the Factors of Their Pain Assessment and Management

Abstract: In the neonatal intensive care unit, preterm infants undergo many painful procedures. Although these can impair their neurodevelopment if not properly managed, only half of the painful procedures are optimally handled. This cross-sectional study aimed to evaluate nurses' perceptions of preterm infants' pain, to evaluate nurses' pain assessment and management practices, as well as to identify the individual and contextual factors that influence nurses' assessments and interventions for pain management. Secondary analyses, including a mixed-model analysis, were performed with data from a larger study (n = 202 nurses). Nurses were found to have attitudes and perceptions in favor of preterm infants' pain management, although they reported using few standardized instruments to assess pain. Nurses stated that they widely used sucrose, non-nutritive sucking, and positioning as pain management interventions, while skin-to-skin contact was rarely practiced. Nurses' attitudes and perceptions influenced their pain assessment practices, which predicted their implementation of interventions. Several contextual (country, level of care, and work shift) and individual factors (age, level of education, had a preterm infant, perceptions of family-centered care, and skin-to-skin contact) also predicted nurses' pain assessment and management practices.

Neonatal Infant Pain Scale in assessing pain and pain relief for newborn male circumcision

Abstract: Circumcision-partial or total removal of the penile prepuce-requires cutting nerve-laden, sensitive genital tissue and is therefore liable to be painful. The aim of this review is to evaluate the evidence concerning pain felt by newborns during circumcision and to determine whether current analgesic methods can eliminate such pain. I performed a search in medical databases, selecting the trials published in the last 20 years that assessed pain in neonatal circumcision. Twenty-three trials have been retrieved. To get reliable findings, those trials that used validated pain scales were selected; then it was investigated which trials had comparable data for using the same pain scale. The only pain scale that was used in more than two trials was the modified Neonatal Infant Pain Scale (mNIPS) that ranges 0-6. The results of these trials show that none of the analgesic strategies used obtained the absence of pain. Some differences between circumcision techniques can be noticed, but most assessments exceed the score of 3, chosen as the clinically significant pain.

Nurses' Pain Assessment Practices for Cognitively Intact and Impaired Older Adults in Intensive Care Units

Abstract: Introduction: Pain is still under-recognized and undertreated among intensive care unit (ICU) patients, such as those being intubated or with dementia, cognitive impairments, or communication deficits due to inability to self-report. This study aimed to describe nurses’ pain assessment practices for cognitively intact and impaired older adult ICU patients. Methods: A descriptive correlational study of a convenience sample of 200 registered nurses was conducted in private, public, and university-affiliated hospitals in Irbid, Jordan. Descriptive statistics, such as mean, standard deviation, and frequency, were used to analyze the study data. Results: Statistically significant differences were found in the proportion of nurses who assessed and documented pain every 1–4 h in cognitively intact patients than those with cognitive impairment (n = 67, 63.21% vs. n = 39, 36.79%), p = 0.002, compared to the proportion of nurses who never assessed and document pain in cognitively impaired patients than those without cognitive impairment (n = 38, 76.0% vs. n = 12, 24%), p < 0.001. Discussion/Conclusion: Our study results showed that the majority of participant nurses felt that the use of pain assessment tools for cognitively intact and impaired older adult ICU patients to self-report is somewhat not at all important. This study also reported that nurses perceived themselves as the individuals who accurately rate the pain in cognitively intact patients, followed by the patients themselves.