Prognostic prediction models for chronic postsurgical pain in adults: a systematic review

Abstract

Chronic postsurgical pain (CPSP) affects an estimated 10% to 50% of adults depending on the type of surgical procedure. Clinical prediction models can help clinicians target preventive strategies towards patients at high risk for CPSP. Therefore, the objective of this systematic review was to identify and describe existing prediction models for CPSP in adults. A systematic search was performed in MEDLINE, Embase, PsychINFO, and the Cochrane Database of Systematic Reviews in March 2020 for English peer-reviewed studies that used data collected between 2000 and 2020. Studies that developed, validated, or updated a prediction model in adult patients who underwent any surgical procedure were included. Two reviewers independently screened titles, abstracts, and full texts for eligibility; extracted data; and assessed risk of bias using the Prediction model Risk of Bias Assessment Tool. The search identified 2037 records; 28 articles were reviewed in full text. Fifteen studies reporting on 19 prediction models were included; all were at high risk of bias. Model discrimination, measured by the area under receiver operating curves or c-statistic, ranged from 0.690 to 0.816. The most common predictors identified in final prediction models included preoperative pain in the surgical area, preoperative pain in other areas, age, sex or gender, and acute postsurgical pain. Clinical prediction models may support prevention and management of CPSP, but existing models are at high risk of bias that affects their reliability to inform practice and generalizability to wider populations. Adherence to standardized guidelines for clinical prediction model development is necessary to derive a prediction model of value to clinicians.