Tseng Joyee, Oladipo Victoria, Dandamudi Siddhartha, Jones Conor M, Levine Brett R
Rush University Medical Center, Department of Orthopaedic Surgery, Chicago, IL 60612, USA.
Antibiotics (Basel). 2024 Jan 4;13(1):48. doi: 10.3390/antibiotics13010048.
Periprosthetic joint infection (PJI) remains a serious complication after total knee arthroplasty (TKA). While debridement, antibiotics, and implant retention (DAIR) are considered for acute PJI, success rates vary. This study aims to assess a new scoring system's accuracy in predicting DAIR success.
119 TKA patients (2008-2019) diagnosed with PJI who underwent DAIR were included for analysis. Data were collected on demographics, laboratory values, and clinical outcomes. This was used for validation of the novel classification system consisting of PJI acuteness, microorganism classification, and host health for DAIR indication. Statistical analysis was carried out using SPSS programming.
Mean follow-up was 2.5 years with an average age of 65.5 ± 9.1 years, BMI of 31.9 ± 6.2 kg/m, and CCI of 3.04 ± 1.8. Successful infection eradication occurred in 75.6% of patients. The classification system demonstrated 61.1% sensitivity, 72.4% specificity, and 87.3% positive predictive value (PPV) when the DAIR cutoff was a score less than 6. For a cutoff of less than 8, sensitivity was 100%, specificity was 37.9%, and PPV was 83.3%.
To date, no consensus exists on a classification system predicting DAIR success. This novel scoring system, with high PPV, shows promise. Further refinement is essential for enhanced predictive accuracy.
人工关节周围感染（PJI）仍然是全膝关节置换术（TKA）后一种严重的并发症。对于急性PJI，虽然考虑进行清创、使用抗生素和保留植入物（DAIR），但其成功率各不相同。本研究旨在评估一种新的评分系统在预测DAIR成功率方面的准确性。
纳入119例在2008年至2019年间被诊断为PJI并接受DAIR治疗的TKA患者进行分析。收集了人口统计学、实验室检查值和临床结果等数据。这些数据用于验证由PJI的急性程度、微生物分类和宿主健康状况组成的用于DAIR指征的新型分类系统。使用SPSS程序进行统计分析。
平均随访时间为2.5年，平均年龄为65.5±9.1岁，体重指数为31.9±6.2kg/m，Charlson合并症指数为3.04±1.8。75.6%的患者成功根除感染。当DAIR临界值为小于6分时，分类系统的灵敏度为61.1%，特异度为72.4%，阳性预测值（PPV）为87.3%。当临界值小于8分时，灵敏度为100%，特异度为37.9%，PPV为83.3%。
迄今为止，对于预测DAIR成功率的分类系统尚无共识。这种具有高PPV的新型评分系统显示出前景。进一步完善对于提高预测准确性至关重要。
