Background: Managing periprosthetic joint infections are variable in practices. Debridement, antibiotics, and implant retention (DAIR) is one of the favorable interventions. Given that the success rate of the two-stage revision total knee arthroplasty (rTKA) might be overestimated. The purpose of this study is to compare the success rate between DAIR and standard two-stage rTKA with a comparable intervention time. Methods: We retrospectively reviewed the consecutive knee periprosthetic joint infection cases which underwent DAIR or two-stage rTKA (all procedures were performed by the senior author) within 12 weeks since their primary TKA between July 2009 and October 2019. Average follow-up was 72.20 ± 40.70 months (range 29–148 months) in the DAIR group compared to 89.14 ± 43.06 months after spacer insertion (range 29–163 months) in the two-stage revision group (P = 0.156). According to different interventions, demographic data; timing of surgical intervention; hospital for special surgery knee score; and success rate were collected and compared between the DAIR group and two-stage revision group. Failure of treatment was based on the Delphi consensus and the fate of spacers. The pathogen types and failure cases were also recorded and analyzed. Results: Average follow-up was 72.20 ± 40.70 months (range 29–148 months) in the DAIR group compared to 89.14 ± 43.06 months after spacer insertion (range 29–163 months) in the two-stage revision group. Time from index surgery was 3.90 ± 2.92 weeks (range 0–12 weeks) in the DAIR group, and 5.11 ± 2.86 weeks (range 0–12 weeks) in the 2-stage exchange group, respectively. The success rate was 70.0% and 75.0% in the DAIR group and two-stage revision group, respectively. But no significant differences were observed between the two groups. Conclusion: DAIR demonstrated comparable effectiveness with two-stage rTKA. We recommended DAIR as a choice for patients with current infection within 12 weeks after primary TKA. For methicillin-resistant staphylococcal infections and fungal infections, two-stage rTKA might be preferred.

Among the elderly, depression is one of the most common mental disorders, which seriously affects their physical and mental health and quality of life, and their suicide rate is particularly high. Depression in the elderly is strongly associated with surgery. In this study, we aimed to explore the risk factors and establish a predictive model of depressive symptoms 1 month after video-assisted thoracoscopic surgery (VATS) in elderly patients. The study participants included 272 elderly patients (age > 65 years) undergoing VATS from April 2020 to May 2021 at 1 of 18 medical centers in China. The patients were divided into a depression group and a nondepression group according to the Chinese version of the nine-item Patient Health Questionnaire (PHQ-9). The patients’ pre- and postoperative characteristics and questionnaires were collected and compared. Then, binary logistic regression was used to determine the risk factors that affect postoperative depressive symptoms, and the predictive model was constructed. The prediction efficiency of the model was evaluated by drawing the receiver operating characteristic curve (ROC), and the area under the curve (AUC) was calculated to evaluate the value of the predictive model. Among all of the included patients, 16.54% (45/272) suffered from depressive symptoms after VATS. The results of the univariate analysis showed that body mass index (BMI), chronic pain, leukocyte count, fibrinogen levels, prothrombin time, ASA physical status, infusion volume, anxiety, sleep quality, and postoperative pain were related to postoperative depressive symptoms (all p < 0.05). The results of multivariate logistic regression analysis showed that a high fibrinogen level (OR = 2.42), postoperative anxiety (OR = 12.05), poor sleep quality (OR = 0.61), and pain (OR = 2.85) were risk factors of postoperative depressive symptoms. A predictive model was constructed according to the regression coefficient of each variable, the ROC curve was drawn, and the AUC value was calculated to be 0.889. The prediction model may help medical personnel identify older patients at risk of developing depressive disorders associated with VATS and may be useful for clinical purposes.