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.

中文翻译：

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成人慢性术后疼痛的预后预测模型：系统评价。

据估计，根据手术类型，慢性术后疼痛 (CPSP) 影响 10% 至 50% 的成年人。临床预测模型可以帮助临床医生针对 CPSP 高风险患者制定预防策略。因此，本系统评价的目的是确定和描述成人 CPSP 的现有预测模型。2020 年 3 月，我们在 MEDLINE、Embase、PsychINFO 和 Cochrane 系统评价数据库中对使用 2000 年至 2020 年期间收集的数据的英语同行评审研究进行了系统检索。开发、验证或更新成人预测模型的研究接受过任何外科手术的患者都被包括在内。两名审稿人独立筛选标题、摘要和全文的资格；提取的数据；并使用预测模型偏倚风险评估工具评估偏倚风险。搜索确定了 2037 条记录；全文审阅了28篇文章。纳入了 15 项报告 19 个预测模型的研究；所有这些都存在很高的偏见风险。模型辨别力通过接受者操作曲线下面积或 c 统计量来衡量，范围为 0.690 至 0.816。最终预测模型中确定的最常见预测因素包括手术区域的术前疼痛、其他区域的术前疼痛、年龄、性别以及术后急性疼痛。临床预测模型可能支持 CPSP 的预防和管理，但现有模型存在很高的偏差风险，影响其为实践提供信息的可靠性以及对更广泛人群的推广性。为了获得对临床医生有价值的预测模型，必须遵守临床预测模型开发的标准化指南。