Managing Pain and Discomfort in Children with Cancer

Biopsychosocial risk factors for pain in early phases of pediatric cancer treatment

Introduction

Cancer pain remains a significant burden among children with cancer, and many patients experience pain starting around the time of diagnosis and throughout the course of treatment. A biopsychosocial treatment approach has been recommended to improve pain management in this population; however, specific psychosocial factors that contribute to pain in the early phases of pediatric cancer treatment have yet to be identified. The purpose of this study was to explore the biopsychosocial factors associated with pain experiences during the early phases of pediatric cancer treatment, with the goal of identifying children who may be at the highest risk for pain to inform future intervention and prevention efforts.

Methods

Data were collected from 203 children with cancer (M = 12.3 years of age, 53.2% male, 41.4% White, 26.6% Latino) and their primary caregiver within the first several weeks of treatment (M = 10 weeks). Children completed self-report questionnaires and caregivers completed self- and parent-proxy questionnaires at baseline. Cancer-related data, including diagnosis and date of diagnosis, were abstracted from the child's electronic medical record. Multiple regression analysis was used to examine associations between biopsychosocial risk factors, pain intensity, and pain interference.

Results

Older age, female gender, and elevated depressive symptoms, fatigue, and child self-reported pain catastrophizing were significantly associated with increased pain intensity. Additionally, lower annual income, decreased physical functioning, and greater fatigue, child pain catastrophizing, and parent stress were significantly associated with increased pain interference.

Discussion

Findings underscore the importance of utilizing a comprehensive biopsychosocial approach to pain assessment and management in pediatric oncology. This approach highlights the need for targeted interventions that address not only the physical aspects of pain but also the psychological and social contexts of patients, ensuring a more holistic and effective treatment strategy.

Virtual reality technology reduces the pain and anxiety of children undergoing vein puncture: a meta-analysis

BACKGROUND

Pain management is an important part of nursing care for children. The objective of this study was to systematically assess the impact of virtual reality (VR) technology on alleviating the pain and anxiety experienced by children during venipuncture procedures.

METHODS

This study searched Pubmed, Web of Sciences, Scopus, The Cochrane Library, Cumulative Index of Nursing and Allied Health Literature (CINAHL), Embase, Medline, China National Knowledge Infrastructure (CNKI), Wanfang, Weipu and China biomedical databases on the randomized controlled trials (RCTs) of virtual reality technology for relieving pain and anxiety associated with venous puncture for children up to July 6, 2024. Risk of bias tool recommended by Cochrane library was used to evaluate the RCT quality. RevMan 5.3 software was used for statistical analysis.

RESULTS

A total of 10 RCTs involving 874 children were included. 429 children received VR intervention during vein puncture. VR was beneficial to reduce the children's self-reported pain scores [SMD=-0.48, 95% CI (- 0.61, - 0.35)], children's caregivers reported needle-related pain level [SMD=-0.93, 95% CI (-1.45, - 0.42)], children's self-reported anxiety scores [SMD=-0.45, 95% CI (- 0.65, - 0.25)], children's caregivers reported needle-related anxiety level [SMD=-0.47, 95% CI (- 0.73, - 0.21)]. Egger regression tests indicated that there were no publication biases in the synthesized outcomes (all P > 0.05).

CONCLUSIONS

VR technology has been shown to effectively mitigate the pain and anxiety experienced by children during venipuncture. Despite the positive findings, more research is needed to better understand the role of VR in children undergoing venipuncture.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.