Is debridement really the best we can do for periprosthetic joint infections following total ankle replacements? A systematic review and meta-analysis.

BACKGROUND: Ankle periprosthetic joint infections are rising in number, but an evidence-based gold standard treatment has not been defined yet. METHODS: We made a systematic review about the operative treatment of infections following total ankle arthroplasty. Proportional meta-analysis was used to summarize effects of the surgical techniques included. Primary outcome of this study was infection eradication, followed by complications, re-interventions, amputation rates and functions. RESULTS: We included six studies(113 patients) reporting 6 types of surgical interventions, mostly irrigation and debridement (35.4%) and two-stage revisions (24.8%). No differences among all analyzed techniques were found in the infection eradication outcome as well as in the secondary outcomes. Patients receiving a permanent spacer are most likely to end up with amputation. CONCLUSIONS: Literature dealing with infections after total ankle replacement is currently composed by few low-quality articles. The overlapping of confidence intervals related to all analyzed interventions showed no superiority of either technique. LEVEL OF EVIDENCE: III.

Medium-term patient's satisfaction after primary total knee arthroplasty: enhancing prediction for improved care.

BACKGROUND: Patient-reported satisfaction after total knee arthroplasty (TKA) is low compared to other orthopedic procedures. Although several factors have been reported to influence TKA outcomes, it is still challenging to identify patients who will experience dissatisfaction five years after surgery, thereby improving their management. Indeed, both perioperative information and follow-up questionnaires seem to lack statistical predictive power. HYPOTHESIS: This study aims to demonstrate that machine learning can improve the prediction of patient satisfaction, especially when classical statistics fail to identify complex patterns that lead to dissatisfaction. PATIENTS AND METHODS: Patients who underwent primary TKA were included in a Registry that collected baseline data and clinical outcomes at different follow-ups. The patients were divided into satisfied and dissatisfied groups based on a satisfaction questionnaire administered five years after surgery. Satisfaction was predicted using linear statistical models compared to machine learning algorithms. RESULTS: A total of 147 subjects were analyzed. Regarding statistics, significant differences between satisfaction levels started emerging only six months after the intervention, and the classification was close to random guessing. However, machine learning algorithms could improve the prediction by 72% soon after the intervention, and an improvement of 178% was possible when including follow-ups up to one year. DISCUSSION: This study demonstrates the feasibility of a registry-based approach for monitoring and predicting satisfaction using ML algorithms. LEVEL OF EVIDENCE: III.

Italian Translation, Adaptation, and Validation of the Novel Satisfaction Measure Assessment after Primary Total Joint Arthroplasty: The Goodman Score Questionnaire.

Patient satisfaction after total joint arthroplasties (TJA) represents a key element for the evaluation of surgery success in relation to subjects' needs and expectations. The assessment tools are applied inconsistently throughout the literature, and thus, it is difficult to compare results among different studies. Goodman et al. proposed a standardized questionnaire with strong psychometric properties for the assessment of satisfaction. The present study aims to translate, adapt, and validate the Goodman questionnaire for the Italian population. After translation and back translation, the questionnaire was administrated to 50 patients. Internal consistency, test-retest reliability, floor and ceiling effects, and construct validity were evaluated (correlation with KOOS/HOOS, SF-12 PCS/MCS, EQ-5D). Responsiveness was evaluated with respect to SF-12 PCS improvements. The Italian version of the Goodman score questionnaire demonstrated psychometric properties similar to those of the original version. The translated questionnaire showed good internal consistency (Cronbach's alpha = 0.836) and test-retest reliability (ICC: 0.507). Moderate/strong correlations were observed between the Italian version of the Goodman score and other scores. The score significantly discriminated patients who improved from those who did not improve in SF-12 PCS after treatment. This study provides an adapted and validated Italian version of the Goodman score questionnaire, with psychometric properties similar to those of its original counterpart.

Remote Management of Patients after Total Joint Arthroplasty via a Web-Based Registry during the COVID-19 Pandemic.

BACKGROUND: In 2020, due to the outbreak of the COVID-19 (Coronavirus Disease 2019) pandemic, patients who underwent total joint arthroplasty were not able to undergo the proper postoperative surgical and rehabilitative care. This study aims to evaluate the potential of a web-cloud-based database on patients' follow-up in extraordinary situations, when a traditional in-person follow-up cannot be warranted. METHODS: Patients who underwent joint arthroplasty at our Institute between 21 February and 16 March 2020 were included in the study group and were matched to a similar population undergoing joint arthroplasty in February/March 2019. All patients routinely complete questionnaires before and after treatment, including patient-reported outcome measures such as the Visual Analogues Scale (VAS), Knee/Hip Injury and Osteoarthritis Outcome Score Physical Function Short Form (KOOS-PS/HOOS-PS) and Short-Form Health Survey (SF-12) for the monitoring of clinical improvements. RESULTS: 56 (study group) and 144 (control group) patients were included in the study. Both groups demonstrated significant improvements at 3 months. HOOS-PS improvement was significantly reduced in the 2020 group compared to 2019 (21.7 vs. 33.9, p < 0.001). This reduction was related to intense physical activities. Similarly, the functional score improvement related to these activities was reduced for patients undergoing knee replacement (8 vs. 10, p < 0.05). CONCLUSIONS: The web-based Institute Registry emerged as a meaningful and sensitive tool during an extraordinary situation such as the COVID-19 pandemic to monitor patients' progression after total joint arthroplasties. Thanks to this tool, it was possible to observe that the prevention of usual postoperative care due to pandemic-related restrictions did not alter the benefits observed after joint replacement surgeries, even if this condition reduced the postoperative improvements in the most burdensome physical activities. A broader use of this kind of tool would improve and potentially reduce the burden and costs of postoperative patients' monitoring in standard and extraordinary conditions. In addition, the systematic remote collection of data would allow for the identification of relevant differences in clinical outcomes in specific conditions or following the modification of treatment and rehabilitation protocols.