Rocchi Caterina, Di Maio Marco, Bulgarelli Alberto, Chiappetta Katia, La Camera Francesco, Grappiolo Guido, Loppini Mattia
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20090 Milan, Italy.
Dipartimento di Scienze Mediche, Chirurgiche e della Salute, Università degli Studi di Trieste, Piazzale Europa, 1, 34127 Trieste, Italy.
Diagnostics (Basel). 2025 May 4;15(9):1172. doi: 10.3390/diagnostics15091172.
: A missed periprosthetic joint infection (PJI) diagnosis can lead to implant failure. However, to date, no gold standard for PJI diagnosis exists, although several classification scores have been developed in the past years. The primary objective of the study was the evaluation of inter-rater reliability between five PJI classification systems when defining a patient who is infected. Two secondary outcomes were further examined: the inter-rater reliability assessed by comparing the classifications in pairs, and the evaluation of each classification system within the subcategories defined by the World Association against Infection in Orthopaedics and Trauma (WAIOT) definition. : Retrospectively collected data on patients with knee and hip PJIs were used to assess the agreement among five PJI scoring systems: the Musculoskeletal Infection Society (MSIS) 2013 definition, the Infection Consensus Group (ICG) 2018 definition, the European Bones and Joints Infection Society (EBJIS) 2018 definition, the WAIOT definition, and the EBJIS 2021 definition. : In total, 203 patients with PJI were included in the study, and the agreement among the examined scores was 0.90 (Krippendorff's alpha = 0.81; -value < 0.001), with the MSIS 2013 and ICG 2018 classification systems showing the highest agreement (Cohen's Kappa = 0.91; -value < 0.001). : There is a strong agreement between the major PJI classification systems. However, a subset of patients ( = 11, 5.42%) still falls into a diagnostic grey zone, especially in cases of low-grade infections. This highlights the need for enhanced diagnostic criteria that incorporate tools that are available even with limited resources, and the potential of artificial intelligence-based techniques in improving early detection and management of PJIs.
假体周围关节感染(PJI)漏诊可导致植入物失效。然而,尽管过去几年已开发出多种分类评分系统,但目前尚无PJI诊断的金标准。本研究的主要目的是评估五种PJI分类系统在定义感染患者时的评分者间信度。另外还进一步考察了两个次要结果:通过成对比较分类来评估评分者间信度,以及在由世界骨科与创伤感染协会(WAIOT)定义的子类别内对每个分类系统进行评估。:回顾性收集膝关节和髋关节PJI患者的数据,以评估五种PJI评分系统之间的一致性:肌肉骨骼感染学会(MSIS)2013年定义、感染共识小组(ICG)2018年定义、欧洲骨关节感染学会(EBJIS)2018年定义、WAIOT定义和EBJIS 2021年定义。:本研究共纳入203例PJI患者,所检查评分之间的一致性为0.90(Krippendorff's alpha = 0.81;P值<0.001),其中MSIS 2013和ICG 2018分类系统显示出最高的一致性(Cohen's Kappa = 0.91;P值<0.001)。:主要的PJI分类系统之间存在很强的一致性。然而,仍有一部分患者(n = 11,5.42%)处于诊断灰色地带,尤其是在低度感染的情况下。这凸显了需要加强诊断标准,纳入即使在资源有限的情况下也可用的工具,以及基于人工智能的技术在改善PJI早期检测和管理方面的潜力。