Prediction and prevention of pain after surgery.

Authors

Esther Miriam Pogatzki-Zahn, Bruno Pradier, Daniel Segelcke

Published in

Pain. Volume 167. Issue 11S. Pages S40-S49. Nov 01, 2026. Epub Nov 02, 2026.

Abstract

Postoperative pain is common and has substantial clinical consequences for patients, their families, and society. Acute pain is not the only problem: a meaningful subset of patients develops chronic postsurgical pain, which is associated with considerable functional and psychosocial burden. Because established chronic postsurgical pain is difficult to treat, prevention is a key clinical objective, but it depends on the early identification of vulnerable patients and timely mechanism-informed interventions. This review examines mechanisms of postsurgical pain, as well as the prediction and prevention of unfavorable outcomes, from a translational perspective that integrates clinical evidence with mechanistic insights from preclinical models. After briefly summarizing recent developments, we outline current challenges and argue that progress requires alignment of outcome constructs across species, rigorous selection and reporting of experimental models, and identification of shared intermediate phenotypes linking mechanisms to patient trajectories. We then summarize the perioperative risk architecture underlying the transition from acute to chronic postsurgical pain, focusing on bio-psycho-social patient-related and surgical factors. Because current risk assessment tools do not yet provide sufficiently precise, reliable, and generalizable prediction, we discuss emerging evidence, including biological signatures that may refine prediction and risk stratification. We further argue that risk emerges dynamically through interactions across pre-, intra- and postsurgical phases. Building on this multi-stage, interactive framework, we propose a strategy integrating bio-psycho-social factors into clinically relevant prognostic models. Finally, we outline how this may not only help identify patients at risk, but also support mechanism-based, personalized prevention in vulnerable patients.

PMID:
42969614
Bibliographic data and abstract were imported from PubMed on 02 Nov 2026.

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