Advancing VR therapy research: strengthening health economic analysis in postoperative pain management

Dear Editor,

The application of artificial intelligence (AI) in research is increasing, and AI-driven smart healthcare is currently a hot topic and at the forefront of research, with virtual reality (VR) therapy being an integral part of this evolution[1]. We read with great interest the recent study by Groenveld et al[2], titled “Early Health Economic Analysis of Virtual Reality Therapy for Pain Management After Surgery” and were truly impressed by the innovative nature of this work, particularly its exploration of the cost-effectiveness of VR therapy in postoperative pain management. The study makes significant contributions to the field, and I believe it has the potential to influence future approaches to pain management. We would like to offer several suggestions that could further enhance the robustness of the conclusions. We believe that incorporating these points may improve the accuracy and applicability of the model, thus instilling even greater confidence in the results (Fig. 1). The manuscript followed the TITAN guidelines[3].

Figure 1.

A cost-effectiveness analysis of VR therapy in postoperative pain management. Several key analysis directions are proposed: refining the classification of health status; re-examining the utility value; adding a three-factor sensitivity analysis; improving the impact of VR therapy; considering productivity loss due to chronic pain; and expanding the time range for analysis. Overall, through analysis from multiple aspects, a comprehensive evaluation of the cost-effectiveness of VR therapy in postoperative pain management would be conducted to provide a more accurate and comprehensive basis for decision-making.

First, the categorization of health states in the current model provides a solid foundation; however, it may be beneficial to refine this categorization to better capture the complexities of real-world clinical scenarios[4]. For example, considering the inclusion of both tumor and non-tumor abdominal surgery patients, incorporating additional health states, such as disease recurrence in cancer patients, would help make the model more reflective of clinical reality.

Second, the utility value assigned to the “no pain” state (1.0) is understandable, but it may not fully capture the potential residual effects of functional impairments that some patients may experience after surgery[5]. Similarly, the utility value for “opioid use” (0.61) is based on long-term opioid users, which may not be entirely applicable to the acute postoperative phase. This discrepancy may result in an overestimation of the quality-adjusted life years gained from VR therapy. It may be worth revisiting these values and considering utility data that are more specific to the acute postoperative period, ensuring that they more accurately reflect the patient population.

Third, while a univariate sensitivity analysis was conducted, incorporating a three-factor sensitivity analysis could provide additional insights into the robustness of the model. By focusing on the most influential factors jointly identified in the univariate analysis[6], we could gain a deeper understanding of how these factors impact the results and offer a clearer picture of the model’s reliability under different assumptions.

Fourth, the effectiveness of VR therapy can vary widely depending on several factors, such as the type of surgery, VR intervention method, and patient characteristics[7]. We suggest conducting a more comprehensive literature review on this topic and perhaps incorporating additional empirical evidence to refine the assumptions regarding VR therapy’s impact. This would strengthen the validity of the model’s assumptions and provide a more robust estimate of the potential benefits of VR therapy.

Fifth, the inclusion of various costs, such as those related to VR therapy, opioids, and general practitioner visits, is well-supported by the Dutch healthcare system data. However, it may be worth considering the inclusion of productivity losses related to chronic pain, particularly in light of the aging population in the Netherlands[8]. While the median age of the cohort is 70, and such losses might be deemed less relevant in older populations, a discussion of this decision could help clarify the exclusion of productivity costs and its potential impact on the study’s results.

Lastly, given that chronic pain often extends well beyond 12 months[9], limiting the time horizon to one year might result in an underestimation of the long-term costs associated with chronic pain. Expanding the analysis to a longer period would provide a more comprehensive understanding of the economic burden of chronic pain. Additionally, not applying discounting due to the “short-term” nature of the analysis might undervalue the long-term benefits of VR therapy. We recommend discussing these aspects in more detail to ensure that the time horizon fully captures the long-term effects[10].

In conclusion, this study provides a strong foundation for understanding the cost-effectiveness of VR therapy in postoperative pain management, and we believe that addressing these suggestions could further strengthen the results. We truly appreciate the depth of the work and look forward to seeing how it evolves. We hope these suggestions prove helpful and encourage further research in this promising area, particularly with longitudinal studies and empirical trials to gather more evidence on the effectiveness of VR therapy.

Ethical approval

Not applicable.

Consent

Not applicable.

Sources of funding

Supported by the National Natural Science Foundation of China (No. 82402403), and the GuangDong Basic and Applied Basic Research Foundation (No. 2025A1515011330).

Author contributions

Y.P. wrote the manuscript; Q.-F.C. and X.-Y.J. revised the manuscript. All authors approved the final manuscript.

Conflicts of interest disclosure

All authors declare no competing interests.

Guarantor

Dr. Xiong-Ying Jiang is the Guarantor.

Research registration unique identifying number (UIN)

Not applicable.

Provenance and peer review

Not applicable.

Data availability statement

All data are included in this article. Further enquiries can be directed to the corresponding author.