Department of Orthopedic Surgery, Mayo Clinic-Arizona, Phoenix, Arizona.
J Arthroplasty. 2018 Sep;33(9):2993-2996. doi: 10.1016/j.arth.2018.04.047. Epub 2018 May 9.
The diagnosis of a periprosthetic joint infection (PJI) remains a clinical challenge, as there is no uniformly accepted gold standard. In 2011, the Musculoskeletal Infection Society (MSIS) convened a work group to create a standardized definition for a PJI that could be universally adopted. Based on the MSIS criteria, the diagnosis of a PJI can be made with 1 of the 2 major criteria, or 3 of the 5 minor criteria. The purpose of this study was to determine the likelihood of having a PJI based on the number of positive minor criteria and thereby develop a prediction algorithm for differentiating between a chronic PJI and a non-PJI based on the number of positive MSIS minor criteria.
We retrospectively reviewed 297 patients who presented to a tertiary care center between 2004 and 2014 with a failed total joint arthroplasty and subsequently underwent a PJI workup to exclude chronic PJI. Patients were divided into 2 groups: (1) PJI group and (2) non-PJI group. Patients who had a positive PJI workup and subsequently underwent a 2-stage revision for infection were included in the PJI group. Patients who had a negative clinical and diagnostic workup were included in the non-PJI group. One hundred eighty-two patients met the criteria for inclusion in the study, 91 in each group. Univariate and multiple logistic regression analyses were used to evaluate 21 independent variables in each of the 2 groups. A prediction algorithm for differentiating between a chronic PJI and a non-PJI based on independent multivariate variables was created.
Patients who had a PJI differed significantly (P < .05) from those who did not have a PJI with regard to 10 independent variables, which included all the MSIS minor criteria we evaluated. Five independent multivariate variables were identified to differentiate between the 2 groups: positive cultures, elevated synovial white blood cell count, elevated synovial polymorphonuclear neutrophil percentage, elevated erythrocyte sedimentation rate, and elevated C-reactive protein. The predictive probability of a PJI for all 32 combinations of these 5 variables was: 3.6% for 1 positive variable, 19.3% for 2, 58.7% for 3, 83.8% for 4, and 97.8% for 5. The chi-squared test for trend and the area under the receiver-operating characteristic curve (0.977) suggest that the model is highly predictive, with an excellent diagnostic performance in identifying a PJI.
Diagnosing a PJI remains a clinical challenge as there is no gold standard for diagnosis. The development of the MSIS criteria, which is based on a consensus of over 400 of the world's experts in musculoskeletal infection, was a major step forward in defining the diagnosis of a PJI. However, to our knowledge, the likelihood of having a PJI based on the number of positive minor criteria has yet to be validated or quantified. Of the 20 independent variables that were evaluated, 10 were found to be significantly associated with a PJI, including all the MSIS minor criteria evaluated. In addition, a diagnostic prediction algorithm was constructed to determine the likelihood of a PJI based on 5 binary independent multivariate variables. The relationship was also examined with a receiver-operating characteristic curve analysis. The area under the curve was 0.98, indicating excellent diagnostic performance for the MSIS minor criteria in identifying a PJI.
III.
假体周围关节感染（PJI）的诊断仍然是一个临床挑战，因为目前还没有普遍接受的金标准。2011 年，肌肉骨骼感染学会（MSIS）召集了一个工作组，制定了一个可以普遍采用的假体周围关节感染的标准化定义。根据 MSIS 标准，PJI 的诊断可以通过 2 项主要标准中的 1 项，或 5 项次要标准中的 3 项来确定。本研究的目的是根据阳性的次要标准数量来确定发生 PJI 的可能性，从而基于 MSIS 次要标准的阳性数量开发一种区分慢性 PJI 和非 PJI 的预测算法。
我们回顾性分析了 2004 年至 2014 年间在一家三级医疗中心就诊的 297 例全关节置换术后失败的患者，并随后进行了 PJI 检查以排除慢性 PJI。将患者分为 2 组：（1）PJI 组和（2）非 PJI 组。在 PJI 检查中发现阳性结果并随后接受 2 期感染翻修的患者被纳入 PJI 组。在临床和诊断检查中发现阴性结果的患者被纳入非 PJI 组。182 例患者符合纳入本研究的标准，每组 91 例。我们使用单变量和多变量逻辑回归分析评估了每组中的 21 个独立变量。根据独立的多变量变量，创建了一种区分慢性 PJI 和非 PJI 的预测算法。
与没有 PJI 的患者相比，患有 PJI 的患者在 10 个独立变量方面存在显著差异（P<.05），这 10 个独立变量包括我们评估的所有 MSIS 次要标准。确定了 5 个独立的多变量变量来区分这 2 组：阳性培养物、升高的滑膜白细胞计数、升高的滑膜多形核中性粒细胞百分比、升高的红细胞沉降率和升高的 C 反应蛋白。所有 32 种这 5 个变量组合的 PJI 预测概率分别为：1 个阳性变量为 3.6%，2 个为 19.3%，3 个为 58.7%，4 个为 83.8%，5 个为 97.8%。卡方检验趋势和受试者工作特征曲线下面积（0.977）表明该模型具有高度预测性，在识别 PJI 方面具有出色的诊断性能。
诊断 PJI 仍然是一个临床挑战，因为目前还没有诊断的金标准。MSIS 标准的制定是基于对肌肉骨骼感染领域的 400 多名世界专家的共识，这是在定义 PJI 诊断方面迈出的重要一步。然而，据我们所知，基于阳性次要标准数量的发生 PJI 的可能性尚未得到验证或量化。在评估的 20 个独立变量中，有 10 个与 PJI 显著相关，包括评估的所有 MSIS 次要标准。此外，还构建了一个诊断预测算法，以根据 5 个二元独立多变量变量确定 PJI 的可能性。还通过受试者工作特征曲线分析进行了检查。曲线下面积为 0.98，表明 MSIS 次要标准在识别 PJI 方面具有出色的诊断性能。
III。
