BACKGROUND:Gram-negative (GN) periprosthetic joint infections (PJIs) are still less frequent than gram-positive (GP) PJIs. There is only little data available on a direct comparison of GP- and GN-associated PJIs. Therefore, we investigated the outcome of GN and GP hip and knee PJI according to the virulence of the pathogens in a matched cohort. METHODS:We retrospectively evaluated 1,695 culture-positive revision hip and knee arthroplasties from a single institution performed between 2008 and 2024. Infection-confirmed PJIs, according to the European Bone and Joint Infection Society criteria, were included. Polymicrobial cases with mixed GN and GP pathogens and anaerobic infections were excluded. A 1:3 propensity score matching for Charlson Comorbidity Index, age, sex, hip, and knee was performed using logistic regressions. The GP PJIs were divided into low (150 of 252; 59.5%) and high-virulent pathogens (102 of 252; 40.5%). Outcomes were determined using the Tier classification by the Musculoskeletal Infection Society. RESULTS:The GN PJIs demonstrated a significantly worse outcome than GP PJIs (P = 0.032) and a 1.64-fold higher risk of reinfection. High-virulent GN/GP PJIs have shown a significantly worse outcome than low-virulent PJIs. There was no statistical significance identified when comparing high-virulent GP PJIs and GN PJIs (P = 0.73). In GN PJIs, single-stage procedures showed the highest success rate (62.5%), whereas debridement, antibiotics, and implant retention showed the lowest (30.8%). Resistance to fluoroquinolones was observed in up to 24% of GN pathogens. However, treatment with fluoroquinolones did not result in a higher success rate (P = 0.214). CONCLUSIONS:To our knowledge, this is the first study comparing GN PJIs and GP PJIs based on their virulence, demonstrating that GN PJIs have a significantly worse outcome than GP PJIs. However, high-virulent GP PJIs have shown the same outcome as GN PJIs, suggesting that virulence plays an equally important role in determining the outcome of PJIs.
BACKGROUND:Limited data exist on the effects of total knee arthroplasty (TKA) on ankle alignment. This large-scale study aimed to assess preoperative and postoperative ankle alignment in patients undergoing TKA using long-leg radiographs. METHODS:This retrospective single-center study analyzed 4,698 radiographs from patients undergoing mechanical alignment TKA. Included patients had a median age of 71 years (range, 31.8 to 95.0) and a mean body mass index of 29.9. Patients were grouped by preoperative and postoperative hip-knee-ankle (HKA) angles. Outcomes were ground-to-tibial plafond (GP), ground-to-talar dome (GT), and talar tilt (TT) angles. Propensity score matching, followed by weighted linear regressions, was used to assess the influence of HKA on ankle angles. RESULTS:Preoperative angles showed a similar pattern, from 10.8 ± 3.8, 10.1 ± 4.3, and 10.5 ± 5.3 degrees in the severe valgus group to -12.5 ± 2.3, -5.2 ± 3.5, and -5.6 ± 4.3 in the severe varus group for HKA, GP, and GT, respectively. A strong correlation was observed between preoperative HKA and GP and GT across all groups (P < 0.001). Preoperative HKA significantly predicted postoperative GP and GT in most groups (P < 0.001). Among patients who had normal postoperative HKA, those who had preoperative valgus HKA showed higher postoperative GP and GT angles than those who had preoperative varus HKA. Postoperative HKA was also a strong predictor of postoperative GP and GT (P < 0.001). Talar tilt was largely unaffected by HKA, and neither body mass index nor age significantly influenced the angles. CONCLUSIONS:Ankle alignment, defined by GP and GT, is strongly influenced by both preoperative and postoperative knee alignment, with preoperative HKA affecting postoperative ankle alignment even when normal postoperative HKA is achieved. The unaffected TT may indicate that alterations in GP and GT represent coordinated shifts of the entire distal ankle.
Posterior shoulder instability (PSI) is rare, accounting for 2-10% of all glenohumeral instabilities, and primarily affects young, physically active patients. Etiologically, traumatic, chronic-repetitive, and atraumatic-habitual forms are distinguished. Clinically, load-dependent posterior shoulder pain predominates, while a subjective feeling of instability is rarely reported. The Jerk and Kim tests are diagnostically decisive, demonstrating high sensitivity and specificity for posteroinferior labral lesions; the modified O'Brien test can provide additional insight. The Stanmore and ABC classifications categorize PSI according to etiology and structural lesions, supporting treatment planning. Imaging diagnostics include radiographs to exclude major structural changes, CT to analyze bony factors such as glenoid retroversion, dysplasia, or bone defects, and MRI to detect typical posterior labral, capsular, and cartilage lesions.
Human gait analysis quantifies locomotion and assesses gait performance, particularly for patients with musculoskeletal disorders. While instrumented 3D gait analysis is the gold standard, advancements in physics based musculoskeletal modeling offer deeper insights into body mechanics. However, its complexity and resource demands limit clinical use, prompting interest in machine learning (ML) as a surrogate for traditional simulations. This scoping review synthesizes ML approaches for estimating joint contact forces in the lower extremities. A systematic search was conducted according to PRISMA-ScR guidelines, covering English language publications from January 2014 to August 2024 across PubMed, IEEE Xplore, Scopus, and SpringerLink. Studies were eligible if they applied ML techniques to estimate lower extremity joint contact forces in human participants and provided sufficient methodological details. Data extraction used a standardized charting form capturing study populations, movement types, input data, ML methods, validation procedures, and performance metrics. 27 studies met the inclusion criteria. The studies showed variability in populations, movement types, input data, ML methods, validation procedures, and performance metrics. Small datasets, often underrepresenting females, limit model generalizability. Inconsistencies in validation approaches and performance metrics, along with the lack of published data and code, hinder reproducibility and comparability. Despite challenges, ML models show potential in accurately predicting joint contact loads and forces. Future research should focus on expanding and diversifying datasets, standardizing methodologies, embracing open science practices, and integrating physics-informed approaches to enhance clinical applicability.
