Somatic and psychological predictors of chronic postsurgical pain in cancer patients: a machine learning approach in a longitudinal two-centre study
Authors' conclusion
This simple algorithm, requiring only three easily accessible inputs, offers a practical tool in routine preoperative settings, supporting timely, targeted interventions to improve pain management in cancer patients at risk of chronic postsurgical pain.
Study context
- Population
- Adults with breast or lung cancer undergoing surgery, two European centres
- Participants
- 255 adults
- Outcome
- Chronic postsurgical pain
- Follow-up
- 1 yr
Methodological detailsprediction model development, longitudinal, two-centre, machine learning, Preoperative data collected 2017–2018; 4 machine learning models trained and validated; re-evaluated by face-to-face interview 1 yr later
- Prediction model development
- Longitudinal
- Two-centre
- Machine learning
- Preoperative data collected 2017–2018; 4 machine learning models trained and validated; re-evaluated by face-to-face interview 1 yr later
Main finding
As reported in the abstract
Chronic postsurgical pain and predictors in the algorithm
- Chronic postsurgical pain
- 83 patients (32.5%)
- Neuropathic pain
- 72 (28% of the total sample)
- Younger age, OR
- 0.47 (95% CI 0.32-0.69)
- Preoperative pain outside the surgical area, OR
- 2.45 (95% CI 1.46-4.10)
- Overwhelming worries (anxiety symptom), OR
- 1.81 (95% CI 1.05-3.13)
Observational data: associations, not effects.
Original publication
Somatic and psychological predictors of chronic postsurgical pain in cancer patients: a machine learning approach in a longitudinal two-centre study. Br J Anaesth. Published 7 January 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →