Pain Management in the Most Vulnerable Intellectual Disability: A Review

Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review

Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.

A retrospective cohort study of prescription drug use among youth with intellectual/developmental disabilities in British Columbia

BACKGROUND

People with intellectual/developmental disabilities (IDD) are known to have high rates of prescription drug use, particularly for psychotropic medications. This is of concern due to the many side effects associated with these medications and because of the risks of polypharmacy. In this paper we compare the most commonly dispensed drugs and all psychotropic medications for youth with IDD compared with youth without IDD.

METHODS

Using population-level administrative health data over a 10-year period, this study examined medications dispensed to youth with an IDD aged 15-24 years compared with youth without an IDD. The most common medications dispensed and the number of youth they were dispensed to were determined. As well a wide variety of psychotropic medications were examined.

RESULTS

There were a total of 20 591 youth with IDD and 1 293 791 youth without IDD identified. Youth with IDD had higher odds of being dispensed pain medications, amoxicillin, salbutamol, levothyroxine and all the psychotropic medications (antidepressants, antipsychotics, anxiolytics, anti-adrenergic agents, mood stabilisers and stimulants). For youth with IDD, 6558 (31.85%) were dispensed two or more different psychotropic medications within a year, compared with 75 963 (5.87%) of youth without IDD.

DISCUSSION

Compared to youth without IDD, youth with IDD had significantly higher odds of being dispensed most of the prescription medications studied, including all of the psychotropic medications. They were also twice as likely to be dispensed two or more medications from different classes of psychotropic drugs within the same year. These findings have important implications for the health of people with IDD and for their health care providers.

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

BACKGROUND

The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images.

METHODS

The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots.

RESULTS

A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns.

CONCLUSIONS

This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies.

TRIAL REGISTRATION

PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.

The Challenges of Pain Assessment in Geriatric Patients With Dementia: A Review

Pain in dementia patients is common, poorly measured, and undertreated. It is important to discuss the challenges in the pain assessment and management to find a possible solution for adequate pain management. The aim of this article is to discuss the challenges in the assessment of pain in geriatric patients with dementia. An extensive online database search was conducted via multiple websites using the following keywords: "dementia," "pain assessments," "pain assessment with dementia," "causes of pain with dementia," "pain assessments using recent technology," "geriatric," and "old age" to identify the relevant articles. Our inclusion criteria were articles that focused on pain in geriatric patients diagnosed with dementia, in English, published between January 2018 and January 2023, and available as free full text and those which were clinical trials, observational studies, review articles, systemic reviews, meta-analysis, or case series. The exclusion criteria were articles that did not have pain in geriatric patients diagnosed with dementia as their primary focus, involving geriatric or non-geriatric patients with major psychological distress, not in the English language, not published between January 2018 and January 2023, and not available as free full-text and those which were case reports and editorial articles. After manually excluding the articles that did not meet our inclusion criteria, we ended up with 38 articles. In conclusion, any instruments have been made for the pain assessment in patients with dementia. The two most common tools used to assess pain in this vulnerable population are the Pain Assessment in Advanced Dementia (PAINAD) and Pain Assessment Checklist for Seniors with Limited Ability to Communicate (PACSLAC) scales. The utilization of new technology may offer promising solutions for the pain assessment in patients with dementia.