Letter to editor: Identifying early risk factors for chronic pain development following vertebral fractures: a single-center prospective cohort study

Article information

Asian Spine J. 2025;19(6):1105-1106

Publication date (electronic) : 2025 December 22

doi : https://doi.org/10.31616/asj.2025.0611.r1

Hina Vaish

School of Health Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, India

Corresponding author: Hina Vaish, School of Health Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh, India, Tel: +91-9450124758, E-mail: hina22vaish@gmail.com; hinavaish@csjmu.ac.in

Received 2025 September 23; Accepted 2025 October 6.

Dear Editor,

We read with great interest the article titled “Identifying early risk factors for chronic pain development following vertebral fractures: a single-center prospective cohort study” published in the Asian Spine Journal [1]. The authors addressed a clinically significant issue, as vertebral fractures are a major source of morbidity, and early prediction of chronic pain is essential in guiding rehabilitation. By identifying high-risk individuals early, the study adds to well-timed interventions that may improve long-term quality of life.

However, a few aspects could be strengthened in future studies. The inclusion of radiological parameters such as fracture morphology, the extent of vertebral collapse, sagittal alignment changes, and the presence of adjacent fractures would have provided valuable structural connects to the observed pain trajectories. Authors from previous research has shown that the mechanical and anatomical consequences of vertebral fractures can significantly influence both pain persistence and functional limitations [2,3]. A combined clinical–radiological model could therefore improve predictive accuracy.

Next, no priori sample-size calculation was reported. Using conventional guidance for logistic regression, an events-per-variable (EPV) approach suggests that, a model with eight candidate predictors would require approximately 400 patients if the chronic pain incidence is 20% (EPV=10), and about more patients if a more conservative EPV=20 is applied. Without a formal sample-size calculation and given the observed number of events, the study may therefore be underpowered to detect smaller effects or interactions [4,5]. Authors from previous study recommended that observational studies that involve logistic regression in the analysis, a minimum sample size of 500 to derive statistics that can represent the parameters in the targeted population. The other recommended rules of thumb are EPV of 50 and formula; n=100+50i where i refers to number of independent variables in the final model [6].

Furthermore, although the study commendably examined psychological and functional variables, a more comprehensive approach could integrate socioeconomic indicators (educational level, occupational status, and economic status), comorbid conditions (osteoporosis, diabetes, cardiovascular disease), and lifestyle influences (physical activity, smoking, and nutrition). These factors are increasingly recognized as important determinants of both pain perception and recovery trajectories [7,8]. Their inclusion would have supported a more collective biopsychosocial model, well reflecting the complexity of chronic pain development.

Lastly, the external validity of the findings may be limited as this was a single-center study. Patient populations frequently vary in terms of demographics, cultural perceptions of pain, and access to health services, all of which can influence chronic pain development.

In conclusion, this study adds significant evidence to the literature on early predictors of chronic pain after vertebral fractures. Future research with a larger, multi-center cohort and broader biopsychosocial and radiological assessments may further refine our ability to identify and prevent chronic pain in this vulnerable patient population.