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INTRUDUCTON: The most accurate method for detecting the pathogen of orthopedic implant-associated infections (OIAIs) is sonication fluid (SF). However, the frequency and duration of ultrasound significantly influence the number and activity of microorganisms. Currently, there is no consensus on the selection of these two parameters. Through this study, the choice of these two parameters is clarified. METHODS: We established five ultrasonic groups (40kHz/10min, 40kHz/5min, 40 kHz/1min, 20kHz/5min, and 10kHz/5min) based on previous literature. OIAIs models were then developed and applied to ultrasound group treatment. Subsequently, we evaluated the efficiency of bacteria removal by conducting SEM and crystal violet staining. The number of live bacteria in the SF was determined using plate colony count and live/dead bacteria staining. RESULTS: The results of crystal violet staining revealed that both the 40kHz/5min group and the 40kHz/10min group exhibited a significantly higher bacterial clearance rate compared to the other groups. However, there was no significant difference between the two groups. Additionally, the results of plate colony count and fluorescence staining of live and dead bacteria indicated that the number of live bacteria in the 40kHz/5min SF group was significantly higher than in the other groups. CONCLUSION: 40kHz/5min ultrasound is the most beneficial for the detection of pathogenic bacteria on the surface of orthopedic implants.
RESUMO
Objective: To explore risk factors for defective non-union of bone and develop a nomogram-based prediction model for such an outcome. Methods: This retrospective study analysed the case data of patients with defective bony non-unions who were treated at the authors' hospital between January 2010 and December 2020. Patients were divided into the union and non-union groups according to their Radiographic Union Score for Tibia scores 1 year after surgery. Univariate analysis was performed to assess factors related to demographic characteristics, laboratory investigations, surgery, and trauma in both groups. Subsequently, statistically significant factors were included in the multivariate logistic regression analysis to identify independent risk factors. A nomogram-based prediction model was established using statistically significant variables in the multivariate analysis. The accuracy and stability of the model were evaluated using receiver operating characteristic (ROC) and calibration curves. The clinical applicability of the nomogram model was evaluated using decision curve analysis. Results: In total, 204 patients (171 male, 33 female; mean [±SD] age, 39.75 ± 13.00 years) were included. The mean body mass index was 22.95 ± 3.64 kg/m2. Among the included patients, 29 were smokers, 18 were alcohol drinkers, and 21 had a previous comorbid systemic disease (PCSD). Univariate analysis revealed that age, occupation, PCSD, smoking, drinking, interleukin-6, C-reactive protein (CRP), procalcitonin, alkaline phosphatase, glucose, and uric acid levels; blood calcium ion concentration; and bone defect size (BDS) were correlated with defective bone union (all P < 0.05). Multivariate logistic regression analysis revealed that PCSD, smoking, interleukin-6, CRP, and glucose levels; and BDS were associated with defective bone union (all P < 0.05), and the variables in the multivariate analysis were included in the nomogram-based prediction model. The value of the area under the ROC curve for the predictive model for bone defects was 0.95. Conclusion: PCSD, smoking, interleukin-6, CRP, and glucose levels; and BDS were independent risk factors for defective bony non-union, and the incidence of such non-union was predicted using the nomogram. These findings are important for clinical interventions and decision-making.
