Single, Recurrent, Synchronous, and Metachronous Periprosthetic Joint Infections in Patients With Multiple Hip and Knee Arthroplasties.

BACKGROUND: The rate for periprosthetic joint infection (PJI) exceeds 1% for primary arthroplasties. Over 30% of patients who have a primary arthroplasty require an additional arthroplasty, and the impact of PJI on this population is understudied. Our objective was to assess the prevalence of recurrent, synchronous, and metachronous PJI in patients who had multiple arthroplasties and to identify risk factors for a subsequent PJI. METHODS: We identified 337 patients who had multiple arthroplasties and at least 1 PJI that presented between 2003 and 2021. The mean follow-up after revision arthroplasty was 3 years (range, 0 to 17.2). Patients who had multiple infected prostheses were categorized as synchronous (ie, presenting at the same time as the initial infection) or metachronous (ie, presenting at a different time as the initial infection). The PJI diagnosis was made using the MusculoSkeletal Infection Society (MSIS) criteria. RESULTS: There were 39 (12%) patients who experienced recurrent PJI in the same joint, while 31 (9%) patients developed PJI in another joint. Positive blood cultures were more likely in the second joint PJI (48%) compared to recurrent PJI (23%) or a single PJI (15%, P < .001). Synchronous PJI represented 42% of the second joint PJI cases (n = 13), while metachronous PJI represented 58% (n = 18). Tobacco users had 75% higher odds of metachronous PJI (odds ratio 1.75, 95% confidence interval: 1.1-2.9, P = .041). CONCLUSION: Over 20% of the patients with multiple arthroplasties and a single PJI will develop a subsequent PJI in another arthroplasty with 12% recurring in the initial arthroplasty and nearly 10% ocurring in another arthroplasty. Particular caution should be taken in patients who use tobacco, have bacteremia, or have Staphylococcus aureus isolation at time of their initial PJI. Optimizing the management of this high-risk patient population is necessary to reduce the additional burden of subsequent PJI. LEVEL OF EVIDENCE: Prognostic Level IV.

An External Acetabular Alignment Guide Decreases Positional Variance.

INTRODUCTION: Certain patient and operative factors limit accurate estimation of acetabular component positioning during total hip arthroplasty (THA). This study aimed to determine whether an intraoperative external alignment guide decreases variance in acetabular component positioning. MATERIALS AND METHODS: Adult patients who underwent primary THA from 2014-2018 were reviewed. Exclusion criteria were navigation, robot-assisted surgery, and inflammatory, post-traumatic, or avascular arthritis. One surgeon used an external guide while the second surgeon resected osteophytes and utilized available anatomical landmarks for positioning. Anteversion and inclination, variance, "safe zone" positioning, operative time, and hip instability were assessed. Multivariable regression models were used to examine effects on primary and secondary outcomes. RESULTS: 409 patients were included, of which 182 underwent component placement with landmarks only. Patients undergoing component placement with landmarks only were younger (p=0.002) and more often smokers (p=0.016). After multivariable risk adjustment, use of the external alignment guide was independently associated with 2.7° higher anteversion (CI: 1.6° to 3.8°) and smaller anteversion variance (-0.3, CI: -0.6 to 0.1) compared to landmarks only. It was independently associated with 3.2° higher inclination (CI: 2.0° to 4.4°), but there was no difference in inclination variance (-0.1, CI: -0.3 to 0.2). The external alignment guide was independently associated with a 14-minute shorter operative time (CI: 9.6 to 18.7) and smaller operative time variance (-0.9, CI: -1.2 to 0.6). DISCUSSION: Use of anatomical landmarks alone was associated with increased likelihood of safe zone positioning but lower precision and longer operative time. While this study was limited by lack of randomization and its retrospective nature, an acetabular positioner may be preferable to palpable or visible anatomy alone for acetabular component placement.

Pes Planovalgus Is Associated with Increased Comorbidities and Poor Outcomes After Total Knee Arthroplasty.

Pes planovalgus affects knee biomechanics but there are no studies describing its impact on total knee arthroplasty (TKA). We aim to characterize the demographics, medical, and surgical complications of patients with pes planovalgus undergoing TKA. A Medicare database was queried using ICD-9 codes to identify 5,750 patients with and 23,000 patients without pes planovalgus who underwent TKA from 2005 to 2014. Standard descriptive statistics were used to compare medical and surgical complications at 90 days and 2 years, with alpha < 0.003 after a Bonferroni Correction. Patients with pes planovalgus had an elevated incidence of hypertension (80%, p < 0.001), pulmonary disease (31%, p < 0.001), hypothyroidism (28%, p < 0.001), diabetes (30%, p < 0.001), vascular disease (20%, p < 0.001), obesity (26%, p < 0.001), and depression (23%, p < 0.001). They also had increased odds of deep vein thrombosis (DVT) (odds ratio [OR] 1.3, p < 0.001), stiffness (OR 1.3, p < 0.003) and revision (OR 1.59, p < 0.003) at 90 days. At 2 years, odds of stiffness had increased (OR 1.34, p < 0.001) with similar rates of revision and medical complications. Pes planovaglus is associated with increased medical comorbidities and this patient population may be at an increased risk for postoperative stiffness, early revisions, and DVT after TKA. Arthroplasty surgeons should be conscious of these risks when considering TKA in a patient with pes planovalgus and counsel them appropriately. (Journal of Surgical Orthopaedic Advances 32(3):202-206, 2023).

Detecting contamination events during robotic total joint arthroplasty.

BACKGROUND: Robot-assisted total joint arthroplasty (robotic-TJA) has become more widespread over the last 20 years due to higher patient satisfaction and reduced complications. However, robotic TJA may have longer operative times and increased operating room traffic, which are known risk factors for contamination events. Contamination of surgical instruments may be contact- or airborne-related with documented scalpel blade contamination rates up to 9%. The robot arm is a novel instrument that comes in and out of the surgical field, so our objective was to assess whether the robot arm is a source of contamination when used in robotic TJA compared to other surgical instruments. METHODS: This was a prospective, single-institution, single-surgeon pilot study involving 103 robotic TJAs. The robot arm was swabbed prior to incision and after closure. Pre- and postoperative control swabs were also collected from the suction tip and scalpel blade. Swabs were incubated for 24 hours on tryptic soy agar followed by inspection for growth of any contaminating bacteria. RESULTS: A contamination event was detected in 10 cases (10%). The scalpel blade was the most common site of contamination (8%) followed by the robot arm (2%) and suction tip (0%). DISCUSSION: Robotic TJA is contaminated with bacteria at a rate around 10%. Although the robot arm is an additional source of potential contamination, the robot arm accrues bacterial contamination infrequently compared to the scalpel blade. CONCLUSION: Contamination of the robot arm during robotic TJA is minimal when compared to contamination of the scalpel blade.

Effect of Comorbidity Burden on the Risk of Venous Thromboembolic Events After Total Knee Arthroplasty.

BACKGROUND: Venous thromboembolic events (VTEs) are common after total knee arthroplasty (TKA). The rate of VTEs has improved with early mobilization, mechanical prophylaxis, and appropriate chemoprophylaxis. The aim of this study was to analyze the contribution of medical comorbidities to the risk of VTE after TKA. METHOD: Medicare claims from 2005 to 2014 were queried. International Classification of Diseases, Ninth revision (ICD-9), and Current Procedural Terminology codes were used to identify the diagnoses, procedures, and complications. 157,200 primary TKAs were age, sex, and Elixhauser Comorbidity Index (ECI) matched with 157,200 osteoarthritis controls. First instances of deep venous thrombosis (DVT) and pulmonary embolism were tracked at 90 days and 2 years. Odds ratios (ORs), confidence intervals, and P-values (p) were calculated and used to investigate the contribution of comorbidities. RESULTS: 90 days after TKA or OA diagnosis, comorbidities were associated with 45% of the DVT risk, 38% of the PE risk. 1 in 92 patients would be expected to be diagnosed with VTE after TKA and 1 in 136 patients after only the diagnosis of osteoarthritis. After 90 days, medical comorbidities were associated with 70% of the DVT risk, 68% of the PE risk. CONCLUSION: Nearly 50% of DVTs and 40% of PEs within 90 days of TKA may be related to the baseline health of OA patients. Venous thromboembolic events after TKA are a "never" event according to Center of Medicare and services that appropriate VTE prophylaxis likely cannot be neutralized.

Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning.

OBJECTIVES: Accurately stratifying patients in the preoperative period according to mortality risk informs treatment considerations and guides adjustments to bundled reimbursements. We developed and compared three machine learning models to determine which best predicts 30-day mortality after hip fracture. METHODS: The 2016 to 2017 National Surgical Quality Improvement Program for hip fracture (AO/OTA 31-A-B-C) procedure-targeted data were analyzed. Three models-artificial neural network, naive Bayes, and logistic regression-were trained and tested using independent variables selected via backward variable selection. The data were split into 80% training and 20% test sets. Predictive accuracy between models was evaluated using area under the curve receiver operating characteristics. Odds ratios were determined using multivariate logistic regression with P < 0.05 for significance. RESULTS: The study cohort included 19,835 patients (69.3% women). The 30-day mortality rate was 5.3%. In total, 47 independent patient variables were identified to train the testing models. Area under the curve receiver operating characteristics for 30-day mortality was highest for artificial neural network (0.92), followed by the logistic regression (0.87) and naive Bayes models (0.83). DISCUSSION: Machine learning is an emerging approach to develop accurate risk calculators that account for the weighted interactions between variables. In this study, we developed and tested a neural network model that was highly accurate for predicting 30-day mortality after hip fracture. This was superior to the naive Bayes and logistic regression models. The role of machine learning models to predict orthopaedic outcomes merits further development and prospective validation but shows strong promise for positively impacting patient care.