Personalized Prediction of Acute and Chronic Postsurgical Pain - the role of Multidomain Biosignatures, Machine Learning-Based Integration, and Standardized Outcome Definitions.

PURPOSE OF THE REVIEW: Acute postsurgical pain (APSP) and chronic postsurgical pain (CPSP) remain prevalent and insufficiently resolved challenges in perioperative medicine. This narrative review summarizes the current state of knowledge regarding personalized pain prediction, examines existing prognostic models and their limitations, and identifies the conceptual and methodological developments most likely to advance the field toward genuine individual prediction.

RECENT FINDINGS: Individual predictors, whether clinical, psychological, psychophysical, or surgical in nature, are, on their own, insufficiently informative to enable individual risk stratification. Existing multivariate prognostic models demonstrate at best moderate discriminatory power and are consistently rated as having a high risk of bias. To date, no externally validated predictive model exists for either outcome. Transitional Pain Services (TPS) and Shared Decision Making (SDM) represent promising organizational and communicative frameworks for translating risk stratification into clinical practice, although controlled trial evidence remains limited. Emerging approaches, including multidomain biosignatures, machine learning-based integration, and standardized outcome definitions, offer a particularly promising path toward genuinely personalized perioperative pain management. Personalizing perioperative pain prediction will require moving beyond individual risk factors toward the multidomain integration of biological, psychophysical, and psychosocial variables. Artificial intelligence could prove to be a catalyst for this integration, but the crucial step is conceptual in nature: recognizing postsurgical pain as a network phenomenon and establishing and implementing the clinical infrastructure needed to act on this insight.