A calculator for musculoskeletal injuries prediction in surgeons: a machine learning approach

Abstract

Background Surgical specialists experience significant musculoskeletal strain as a consequence of their profession, a domain within the healthcare system often recognized for the pronounced impact of such issues. The aim of this study is to calculate the risk of presenting musculoskeletal injuries in surgeons after surgical practice. Methods Cross-sectional study carried out using an online form (12/2021–03/2022) aimed at members of the Spanish Association of Surgeons. Demographic variables on physical and professional activity were recorded, as well as musculoskeletal pain (MSP) associated with surgical activity. Univariate and multivariate analysis were conducted to identify risk factors associated with the development of MSP based on personalized surgical activity. To achieve this, a risk algorithm was computed and an online machine learning calculator was created to predict them. Physiotherapeutic recommendations were generated to address and alleviate each MSP. Results A total of 651 surgeons (112 trainees, 539 specialists). 90.6% reported MSP related to surgical practice, 60% needed any therapeutic measure and 11.7% required a medical leave. In the long term, MSP was most common in the cervical and lumbar regions (52.4, 58.5%, respectively). Statistically significant risk factors (OR CI 95%) were for trunk pain, long interventions without breaks (3.02, 1.65–5.54). Obesity, indicated by BMI, to lumbar pain (4.36, 1.84–12.1), while an inappropriate laparoscopic screen location was associated with cervical and trunk pain (1.95, 1.28–2.98 and 2.16, 1.37–3.44, respectively). A predictive model and an online calculator were developed to assess MSP risk. Furthermore, a need for enhanced ergonomics training was identified by 89.6% of surgeons. Conclusions The prevalence of MSP among surgeons is a prevalent but often overlooked health concern. Implementing a risk calculator could enable tailored prevention strategies, addressing modifiable factors like ergonomics