Department of Anaesthesiology, Intensive Care and Pain Medicine, University Hospital Muenster, Muenster, Germany.
Curr Opin Anaesthesiol. 2023 Oct 1;36(5):580-588. doi: 10.1097/ACO.0000000000001299. Epub 2023 Jul 27.
Prognostic models for chronic postsurgical pain (CPSP) aim to predict the likelihood for development and severity of CPSP in individual patients undergoing surgical procedures. Such models might provide valuable information for healthcare providers, allowing them to identify patients at higher risk and implement targeted interventions to prevent or manage CPSP effectively. This review discusses the latest developments of prognostic models for CPSP, their challenges, limitations, and future directions.
Numerous studies have been conducted aiming to develop prognostic models for CPSP using various perioperative factors. These include patient-related factors like demographic variables, preexisting pain conditions, psychosocial aspects, procedure-specific characteristics, perioperative analgesic strategies, postoperative complications and, as indicated most recently, biomarkers. Model generation, however, varies and performance and accuracy differ between prognostic models for several reasons and validation of models is rather scarce.
Precise methodology of prognostic model development needs advancements in the field of CPSP. Development of more accurate, validated and refined models in large-scale cohorts is needed to improve reliability and applicability in clinical practice and validation studies are necessary to further refine and improve the performance of prognostic models for CPSP.
慢性术后疼痛（CPSP）的预后模型旨在预测接受手术治疗的个体患者发生 CPSP 的可能性和严重程度。这些模型可能为医疗保健提供者提供有价值的信息，使他们能够识别出风险较高的患者，并采取有针对性的干预措施，有效地预防或管理 CPSP。本综述讨论了 CPSP 预后模型的最新进展、它们的挑战、局限性和未来方向。
已经进行了许多研究，旨在使用各种围手术期因素来开发 CPSP 的预后模型。这些因素包括患者相关因素，如人口统计学变量、先前存在的疼痛状况、社会心理方面、手术特异性特征、围手术期镇痛策略、术后并发症，以及最近表明的生物标志物。然而，模型生成方式各不相同，并且由于多种原因，预后模型的性能和准确性存在差异，而且模型的验证也相当缺乏。
需要在 CPSP 领域改进预后模型开发的精确方法。需要在大规模队列中开发更准确、经过验证和改进的模型，以提高在临床实践中的可靠性和适用性，并且需要验证研究来进一步改进和提高 CPSP 预后模型的性能。
