Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial
Authors' conclusion
A GLASSO-PLS-SEM workflow provided an exploratory approach to identifying key features and mapping hypothesised multivariable associations in CPSP-related outcomes.
Study context
- Population
- Participants of the ENIGMA-II chronic post-surgical pain (CPSP) substudy with 12-month postoperative assessmentENIGMA-II CPSP substudy
- Participants
- 2,999 participants
- Primary endpoint
- Chronic post-surgical pain (CPSP) and related outcomes (EQ-5D, pain severity, pain interference)
- Follow-up
- 12 months
Methodological detailssecondary observational analysis, exploratory, grouped lasso (GLASSO) feature selection, partial least squares structural equation modelling (PLS-SEM)
- Secondary observational analysis
- Exploratory
- Grouped lasso (GLASSO) feature selection
- Partial least squares structural equation modelling (PLS-SEM)
Primary result
As reported in the abstract
Factors associated with CPSP and related outcomes
- GLASSO CPSP AUC
- 0.69 (95% CI 0.64-0.74)
- GLASSO EQ-5D R2
- 0.34
- GLASSO pain severity R2
- 0.29
- GLASSO pain interference R2
- 0.24
- PLS-SEM CPSP: presurgical pain (strongest association), B
- 0.348
- PLS-SEM CPSP: demographics, B
- 0.169
- PLS-SEM CPSP: surgical aspects, B
- 0.091
- PLS-SEM CPSP: lifestyle patterns (small inverse association), B
- −0.051
- PLS-SEM EQ-5D: lifestyle (negative association), B
- −0.248
- PLS-SEM EQ-5D: NSAID-related treatment patterns, B
- 0.079
Original publication
Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial. BJA Open. Published 17 June 2026. doi:10.
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