Park HJ, et al. Clin Orthop Surg. 2023 Apr;15(2):327-337. https://doi.org/10.4055/cios22177

Background: Effective perioperative blood glucose control is crucial for reducing postoperative complications in patients with diabetes mellitus (DM) who are undergoing total knee arthroplasty (TKA). The aim of this study was to assess the impact of intravenous (IV) dexamethasone on blood glucose levels, insulin requirements, postoperative pain, and postoperative nausea and vomiting (PONV) in patients with well-controlled type-2 DM. Methods: A total of 83 Asian patients with well-controlled type-2 DM (defined as a preoperative glycated hemoglobin level of ≤7.0%) undergoing primary TKA were randomized to receive either IV dexamethasone or normal saline solution. Blood glucose and insulin requirements were monitored postoperatively up to day 5, and pain and PONV were assessed using a numeric rating scale. Results: Compared with the control, IV dexamethasone transiently elevated blood glucose levels on the day of surgery and on postoperative day 1, with the levels returning to baseline by day 3. Insulin requirements were higher in the intervention group on postoperative day 1 (p = 0.004). While IV dexamethasone did not significantly reduce PONV, it effectively alleviated postoperative pain up to day 3. Conclusions: In patients with DM who underwent TKA, IV dexamethasone administration transiently increased blood glucose on the day of surgery and on postoperative day 1 and elevated insulin requirements on postoperative day 1. Despite having no impact on PONV, IV dexamethasone provided clinical benefits by reducing early postoperative pain. These findings suggest the potential benefits of IV dexamethasone in enhancing perioperative management strategies for patients with DM who are undergoing TKA. Level of Evidence: Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

The purposes were to analyze correlations between the frequency of beverage drinking (coffee, green tea, milk, and soft drinks) and the presence of radiographic knee osteoarthritis (OA) in relation to sex. We performed this study using the Korea National Health and Nutrition Examination Survey (KHANES V-1, 2). We examined data from 5503 subjects after exclusion. We utilized the food frequency questionnaires from KHANES, and reorganized them into 2 or 3 groups according to the frequency of beverage consumption. We analyzed the relationship between radiographic knee OA and beverage consumption statistically after adjusting confounding factors with multivariable logistic regression analysis. Knee OA was inversely associated with coffee consumption only in women (P < .05). The odds ratio of knee OA was lower in those who drank at least a cup of coffee than in those who did not drink coffee in women (P for trend < .05). However, there was no significant linear trend of the odds ratio of each group in both sexes for drinking other beverages. As the coffee consumption increased, the radiographic knee OA group showed decreasing linear trend only in women. However, other beverages did not show a significant relation to the radiographic knee OA in both sexes.

Background: Meniscal ramp lesions can impact knee stability, particularly when associated with anterior cruciate ligament (ACL) injuries. Although magnetic resonance imaging (MRI) is the primary diagnostic tool, its diagnostic accuracy remains suboptimal. We aimed to determine whether deep learning technology could enhance MRI-based ramp lesion detection. Methods: We reviewed the records of 236 patients who underwent arthroscopic procedures documenting ACL injuries and the status of the medial meniscal posterior horn. A deep learning model was developed using MRI data for ramp lesion detection. Ramp lesion risk factors among patients who underwent ACL reconstruction were analyzed using logistic regression, extreme gradient boosting (XGBoost), and random forest models and were integrated into a final prediction model using Swin Transformer Large architecture. Results: The deep learning model using MRI data demonstrated superior overall diagnostic performance to the clinicians’ assessment (accuracy of 73.3% compared with 68.1%, specificity of 78.0% compared with 62.9%, and sensitivity of 64.7% compared with 76.4%). Incorporating risk factors (age, posteromedial tibial bone marrow edema, and lateral meniscal tears) improved the model’s accuracy to 80.7%, with a sensitivity of 81.8% and a specificity of 80.9%. Conclusions: Integrating deep learning with MRI data and risk factors significantly enhanced diagnostic accuracy for ramp lesions, surpassing that of the model using MRI alone and that of clinicians. This study highlights the potential of artificial intelligence to provide clinicians with more accurate diagnostic tools for detecting ramp lesions, potentially enhancing treatment and patient outcomes. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Background: Septic arthritis is an orthopedic emergency. However, optimal biomarkers and diagnostic criteria remain unclear. The study aimed to evaluate the diagnostic performance of routinely used and novel biomarkers, including serum C-reactive protein (CRP), synovial white blood cells (WBC), pentraxin-3 (PTX3), interleukin-6 (IL-6), and presepsin, in distinguishing septic from non-septic arthritis. Methods: Thirty-one patients undergoing arthrocentesis were included. Patients were categorized into septic and non-septic arthritis groups. Synovial fluid and serum samples were analyzed for five biomarkers. Diagnostic performance was assessed by calculating the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results: Synovial WBC demonstrated the highest diagnostic performance among single biomarkers (AUC = 0.837, p = 0.012). Among novel biomarkers, PTX3 showed the highest accuracy and sensitivity. The serum CRP and synovial WBC combination yielded an AUC of 0.853, with 100% sensitivity, 68.0% specificity, 42.9% PPV, and 100% NPV. Adding all three novel biomarkers to this combination increased the AUC to 0.887 (p = 0.004), maintaining 100% sensitivity and NPV. When individually added, PTX3 achieved 100% sensitivity and NPV, while presepsin showed the highest specificity (96.0%), PPV (75.0%), and accuracy (87.1%). Conclusions: Serum CRP and synovial WBC remain essential biomarkers for diagnosing septic arthritis; however, combining them with PTX3, IL-6, and presepsin improved diagnostic accuracy. PTX3 is best suited for ruling out septic arthritis due to its high sensitivity and NPV, whereas presepsin is more useful for confirmation, given its specificity and PPV. These results support a tailored biomarker approach aligned with diagnostic intent.

Background: Because populations in many countries are aging, a number of large database studies have reported more patients with Parkinson disease undergoing TKA, and the risk of complications after surgery is greater in this population. Because Parkinson disease–related autonomic dysfunction, medication changes, mobility loss, and frailty may evolve after diagnosis, the diagnosis-to-TKA interval could identify clinically different vascular and periprosthetic risk profiles and help guide counseling and selective preoperative assessment; however, 5-year endpoint-specific risks remain insufficiently characterized. Questions/purposes: (1) Is Parkinson disease diagnosed before TKA associated with higher 5-year risks of vascular events and periprosthetic complications after TKA? (2) How do these associations appear when patients with Parkinson disease diagnosed before TKA are grouped by a pragmatic 5-year diagnosis-to-TKA interval? Methods: Using the National Health Insurance Service (NHIS) database of South Korea, we identified patients who underwent unilateral TKA between January 2010 and December 2018, with follow-up available through December 2023 to allow assessment of 5-year postoperative outcomes. The database links procedure, diagnosis, prescription, and health-screening data at the individual level, allowing assessment of 5-year vascular and periprosthetic outcomes. Patients with < 5 years of follow-up or preexisting cardiovascular or cerebrovascular disease were excluded to focus on incident postoperative vascular events. The unmatched analytic cohort included 287,075 patients without Parkinson disease and 20,728 patients with Parkinson disease diagnosed before TKA, including 16,877 diagnosed ≤ 5 years before TKA and 3851 diagnosed > 5 years before TKA. All patients in these unmatched groups had at least 5 years of follow-up by design. Parkinson disease was defined by diagnostic codes and prescriptions for Parkinson disease–related medications. Each group of patients diagnosed with Parkinson disease before TKA was propensity score–matched 1:1 to the group of patients without Parkinson disease, yielding 20,707 matched pairs for all patients with Parkinson disease before TKA, 16,860 matched pairs for those diagnosed ≤ 5 years before TKA, and 3848 matched pairs for those diagnosed > 5 years before TKA. After matching, age, sex, comorbidity, health behaviors, dyslipidemia, and year of surgery were balanced. Outcomes were analyzed using Cox regression models, with prespecified time-window analyses to describe temporal patterns in adjusted associations. Results: After adjustment for age, sex, Charlson comorbidity index, smoking, alcohol use, physical activity, dyslipidemia, and year of surgery, Parkinson disease diagnosed before TKA was associated with a slightly higher 5-year adjusted hazard of cerebrovascular events (adjusted HR 1.11 [95% confidence interval (CI) 1.07 to 1.16]; p < 0.001) but not with cardiovascular events, periprosthetic fracture, or PJI. Among patients with Parkinson disease diagnosed ≤ 5 years before TKA, cardiovascular events (adjusted HR 1.05 [95% CI 1.01 to 1.10]; p = 0.02) and cerebrovascular events (adjusted HR 1.14 [95% CI 1.09 to 1.18]; p < 0.001) were associated with slightly higher adjusted hazards than in matched patients without Parkinson disease. These associations were not observed in patients with Parkinson disease diagnosed > 5 years before TKA. Time-window analyses in patients diagnosed ≤ 5 years before TKA showed a small early inverse association for cardiovascular events at 6 months and a small positive association by 5 years. Periprosthetic fracture and periprosthetic joint infection were not increased across interval subgroups. Conclusion: These findings should not be interpreted as supporting routine cardiovascular or cerebrovascular screening solely because a patient has Parkinson disease. Rather, Parkinson disease status and the diagnosis-to-TKA interval may help surgeons counsel patients and identify selected individuals who warrant multidisciplinary perioperative assessment of vascular risk factors, autonomic symptoms, blood pressure instability, and Parkinson disease medication management. Future studies should incorporate clinical Parkinson disease severity, frailty, mobility, and competing mortality to improve absolute risk prediction and identify patients who may benefit from additional preoperative medical review. Level of Evidence: Level III, therapeutic study.