An Ensemble of Psychological and Physical Health Indices Discriminates Between Individuals with Chronic Pain and Healthy Controls with High Reliability: A Machine Learning Study

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Arts Therapy and Its Implications in Chronic Pain Management: A Narrative Review

Chronic nonmalignant pain is recognized as a complex, dynamic, phenomenological interplay between biological, psychological, and social factors that are individual to the person suffering from it. Therefore, its management and treatment ought to entail the individual's biopsychosocial aspects that are often addressed by collaborative, inter/multidisciplinary multimodal care, as there is no biologic treatment. In an effort to enhance inter/multidisciplinary multimodal care, a narrative review of arts therapy as a mind-body intervention and its efficacy in chronic pain populations has been conducted. Changes in emotional and physical symptoms, especially pain intensity, during arts therapy sessions have also been discussed in in the context of attention distraction strategy. Arts therapy (visual art, music, dance/movement therapy, etc.) have been investigated to summarize relevant findings and to highlight further potential benefits, limitations, and future directions in this area. We reviewed 16 studies of different design, and the majority reported beneficial effects of art therapy in patients' management of chronic pain and improvement in pain, mood, stress, and quality of life. However, the results are inconsistent and unclear. It was discovered that there is a limited amount of high-quality research available on the implications of arts therapy in chronic nonmalignant pain management. Due to the reported limitations, low effectiveness, and inconclusive findings of arts therapy in the studies conducted so far, further research with improved methodological standards is required.

Clinical and psychological factors associated with resilience in patients with schizophrenia: data from the Italian network for research on psychoses using machine learning

BACKGROUND

Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR).

METHODS

SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients.

RESULTS

The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (pFDR < 0.05).

CONCLUSIONS

We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia.

Role of Artificial Intelligence and Machine Learning in the prediction of the pain: A scoping systematic review

Artificial Intelligence in healthcare is growing quickly in diagnostics and treatment management. Despite the quantity and variety of studies its role in clinical care is not clear. To identify the evidence gaps and characteristics of the Artificial Intelligence and Machine Learning techniques in predicting/diagnosing the pain? Pubmed/Embase were searched from the inception to October 2021 for articles without any language restrictions specifically addressing the following: the accuracy of AI in pain considering Brain Imaging, Patient-reported measures, and Electrophysiology, the ability of AI to differentiate stratify severity/types of pain, the ability of AI to predict pain and lastly the most accurate AI technique for given inputs. All the included studies were on humans. Eight hundred forty abstracts were reviewed, and 23 articles were finally included. Identified records were independently screened and relevant data was extracted. We performed conceptual synthesis by grouping the studies using available concepts of AL/ML techniques in diagnosing pain. Then we summarized the number of features/physiological measurements. Structured tabulation synthesis was used to show patterns predictions along with a narrative commentary. A total of 23 articles, published between 2015 and 2020 from 12 countries were included. Most studies were experimental in design. The most common design was cross sectional. Chronic or acute pains were predicted more often. Compared to control, the pain prediction was in the range of 57%–96% by AI techniques. Support Vector Machine and deep learning showed higher accuracy for classifying pain. From this study, it can be inferred that AI/ML can be used to differentiate healthy controls from patients. It can also facilitate categorizing them into new and different clinical subgroups. Lastly, it can predict future pain. The limitations are with respect to studies done after the search period. AL/ ML has a supportive role in pain diagnostics.

Comparing Perceived Pain Impact Between Younger and Older Adults With High Impact Chronic Pain: A Cross-Sectional Qualitative and Quantitative Survey

High impact chronic pain (HICP) is a recently proposed concept for treatment stratifying patients with chronic pain and monitoring their progress. The goal is to reduce the impact of chronic pain on the individual, their family, and society. The US National Pain Strategy defined HICP as the chronic pain associated with substantial restrictions on participation in work, social, and self-care activities for at least 6 months. To understand the meaning and characteristics of HICP from the younger (<65 years old) and older adults (≥65 years old) with chronic pain, our study examined patients' perceived pain impact between the two age groups. We also characterize the degree of pain impact, assessed with the Patient-Reported Outcomes Measurement Information System (PROMIS) pain interference (PI), between adults and older adults with HICP. We recruited patients at a tertiary pain clinic. The survey included open-ended questions about pain impact, the Graded Chronic Pain Scale-Revised to identify patients' meeting criteria for HICP, and the Patient-Reported Outcomes Measurement Information System (PROMIS^®) 8-item PI short form (v.8a). A total of 55 younger adults (65.5% women, 72.7% HICP, mean age = 55.0 with SD of 16.2) and 28 older adults (53.6% women, 64.3% HICP, mean age = 72.6 with SD of 5.4) with chronic pain participated in this study. In response to an open-ended question in which participants were asked to list out the areas of major impact pain, those with HICP in the younger group most commonly listed work, social activity, and basic physical activity (e.g., walking and standing); for those in the older group, basic physical activity, instrumental activity of daily living (e.g., housework, grocery shopping), and participating in social or fun activity for older adults with HICP were the most common. A 2 × 2 ANOVA was conducted using age (younger adults vs. older adults) and HICP classification (HICP vs. No HICP). A statistically significant difference was found in the PROMIS-PI T-scores by HICP status (HICP: M = 58.4, SD = 6.3; No HICP: M = 67.8, SD = 6.3), but not by age groups with HICP. In conclusion, perceived pain impacts were qualitatively, but not quantitatively different between younger and older adults with HICP. We discuss limitations and offer recommendations for future research.

Influence of Physical Activity on Pain, Depression and Quality of Life of Patients in Palliative Care: A Proof-of-Concept Study

Introduction: Palliative care not only focuses on physical ailments associated with the disease, but also considers the psychological, social and spiritual needs of the patients. The aim of this study is to assess the impact of physical activity on palliative care patients, with special regard to the subjective assessment of severity of total pain and quality of life. Materials and methods: The study was conducted on 92 palliative care patients either in a hospice or at home. The tool used to assess the patients was an original questionnaire focusing on the area of their independence and motor abilities. The study attempted to understand whether an appropriate physical activity and the instruction of palliative care patients and their families in the field of independence would improve the quality of life and reduce the intensity of total pain in the patients. Results: All of the patients were at an advanced stage of cancer. The survey at time “0”, conducted before the start of the instructions for patients and their relatives, showed that a majority of patients (47, 51.09%) often experienced limitations during the performance of daily activities. In the fourth visit, conducted one week after the fourth educational session, there was a significant increase in patients who did not experience any limitations in performing their daily activities or experienced them just sometimes. Conclusions: The ultimate effect of the proposed educational program on physical activity was an increase in the quality of life, a reduction in pain and a mood improvement. These results would need confirmation with more extensive studies.