Psychometric Assessment of Physiologic and Behavioral Pain Indicators in Polish Versions of the Pain Assessment Scales

Pain assessment tools in adults with communication disorders: systematic review and meta-analysis

BACKGROUND

Verbal communication is the "gold standard" for assessing pain. Consequently, individuals with communication disorders are particularly vulnerable to incomplete pain management. This review aims at identifying the current pain assessment instruments for adult patients with communication disorders.

METHODS

A systematic review with meta-analysis was conducted on PubMed, PEDRO, EBSCOhost, VHL and Cochrane databases from 2011 to 2023 using MeSH terms "pain assessment, "nonverbal communication" and "communication disorders" in conjunction with additional inclusion criteria: studies limited to humans, interventions involving adult patients, and empirical investigations.

RESULTS

Fifty articles were included in the review. Seven studies report sufficient data to perform the meta-analysis. Observational scales are the most common instruments to evaluate pain in individuals with communication disorders followed by physiological measures and facial recognition systems. While most pain assessments rely on observational scales, current evidence does not strongly endorse one scale over others for clinical practice. However, specific observational scales appear to be particularly suitable for identifying pain during certain potentially painful procedures, such as suctioning and mobilization, in these populations. Additionally, specific observational scales appear to be well-suited for certain conditions, such as mechanically ventilated patients.

CONCLUSIONS

While observational scales dominate pain assessment, no universal tool exists for adults with communication disorders. Specific scales exhibit promise for distinct populations, yet the diverse landscape of tools hampers a one-size-fits-all solution. Crucially, further high-quality research, offering quantitative data like reliability findings, is needed to identify optimal tools for various contexts. Clinicians should be informed to select tools judiciously, recognizing the nuanced appropriateness of each in diverse clinical situations.

TRIAL REGISTRATION

This systematic review is registered in PROSPERO (International prospective register of systematic reviews) with the ID: CRD42022323655 .

Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy

Objective

Assessing pain in individuals with neurological conditions like cerebral palsy is challenging due to limited self-reporting and expression abilities. Current methods lack sensitivity and specificity, underlining the need for a reliable evaluation protocol. An automated facial recognition system could revolutionize pain assessment for such patients.The research focuses on two primary goals: developing a dataset of facial pain expressions for individuals with cerebral palsy and creating a deep learning-based automated system for pain assessment tailored to this group.

Methods

The study trained ten neural networks using three pain image databases and a newly curated CP-PAIN Dataset of 109 images from cerebral palsy patients, classified by experts using the Facial Action Coding System.

Results

The InceptionV3 model demonstrated promising results, achieving 62.67% accuracy and a 61.12% F1 score on the CP-PAIN dataset. Explainable AI techniques confirmed the consistency of crucial features for pain identification across models.

Conclusion

The study underscores the potential of deep learning in developing reliable pain detection systems using facial recognition for individuals with communication impairments due to neurological conditions. A more extensive and diverse dataset could further enhance the models' sensitivity to subtle pain expressions in cerebral palsy patients and possibly extend to other complex neurological disorders. This research marks a significant step toward more empathetic and accurate pain management for vulnerable populations.

Pain Assessment with the BPS and CCPOT Behavioral Pain Scales in Mechanically Ventilated Patients Requiring Analgesia and Sedation

BACKGROUND

Intensive Care Unit (ICU) patients often experience pain, especially during diagnostic, nursing, and therapeutic interventions. Pain assessment using the Behavioral Pain Scale (BPS) and Critical Care Pain Observation Tool (CCPOT) are recommended, but they are difficult to do in patients undergoing deep sedation. This study analyzed the usefulness of the BPS and CCPOT scales in assessing pain among patients with varying degrees of sedation.

METHODS

In 81 mechanically ventilated and sedated ICU patients, 1005 measurements were performed using the BPS and CCPOT scales. The study was conducted by 3 trained observers 3 times a day (each measurement at rest, during painful nursing interventions, and after the intervention). The Richmond Agitation-Sedation Scale (RASS), the Simplified Acute Physiology Score (SAPS II), and the Acute Physiology and Chronic Health Evaluation (APACHE II) were also analyzed from medical records as well as information on the length of hospitalization and treatment.

RESULTS

It was shown that signs of pain increased significantly (p < 0.001) during interventions in patients on both scales (BPS and CCPOT), and then returned to values close to the resting period. RASS results correlated significantly (p < 0.05) and positively with the results of the BPS and CCPOT. A strong correlation was found between the results of both scales at each stage of the study (R = 0.622-0.907).

CONCLUSIONS

Nursing procedures are a source of pain in analgosedated patients. The BPS and CCPOT scales are useful tools for assessing the occurrence of pain in mechanically ventilated patients, including those in deep sedation.

Nursing Interventions to Prevent Delirium in Critically Ill Patients in the Intensive Care Unit during the COVID19 Pandemic-Narrative Overview

It has become a standard measure in recent years to utilise evidence-based practice, which is associated with a greater need to implement and use advanced, reliable methods of summarising the achievements of various scientific disciplines, including such highly specialised approaches as personalised medicine. The aim of this paper was to discuss the current state of knowledge related to improvements in "nursing" involving management of delirium in intensive care units during the SARS-CoV-2 pandemic. This narrative review summarises the current knowledge concerning the challenges associated with assessment of delirium in patients with COVID-19 by ICU nurses, and the role and tasks in the personalised approach to patients with COVID-19.

A Psychometric Analysis Update of Behavioral Pain Assessment Tools for Noncommunicative, Critically Ill Adults

This is an updated, comprehensive review of the psychometric properties of behavioral pain assessment tools for use with noncommunicative, critically ill adults. Articles were searched in 5 health databases. A total of 106 articles were analyzed, including 54 recently published papers. Nine behavioral pain assessment tools developed for noncommunicative critically ill adults and 4 tools developed for other non-communicative populations were included. The scale development process, reliability, validity, feasibility, and clinical utility were analyzed using a 0 to 20 scoring system, and quality of evidence was also evaluated. The Behavioral Pain Scale, the Behavioral Pain Scale-Nonintubated, and the Critical-Care Pain Observation Tool remain the tools with the strongest psychometric properties, with validation testing having been conducted in multiple countries and various languages. Other tools may be good alternatives, but additional research on them is necessary.