No abstract

BackgroundThe aim of this study was to investigate whether needles introduce skin plugs into joints during arthrocentesis.Material/MethodsIn the first part of this study, the arthrocentesis site was scrubbed with a fluorescein sodium swab, and 90 needles were inserted through the joint tissue and collected for examination under a fluorescence microscope. In the second part of this study, the joints were injected using 720 needles of different gauges. Two different randomly assigned needle insertion techniques were used: needle insertion straight through the joint capsule (subgroup 1) or insertion of the needle into the subcutaneous tissue followed by flushing of the needle with 0.5 mL of 0.9% normal saline prior to advancing the needle through the joint capsule (subgroup 2).ResultsOf the 90 needle tips examined in the first part of this study, 21 had high-grade fluorescein contamination. In the second part of this study, the incidence of tissue, epidermis, and dermis contamination in subgroup 1 was 57.2%, 43.1%, and 25.0%, respectively. There was no significant difference in the incidence among different gauge needles, except for a difference in epidermis contamination between the 21-gauge and 23-gauge needles. Compared to subgroup 1, subgroup 2 had a significantly lower OR for tissue contamination.ConclusionsIt is common to introduce tissue coring with epidermis and dermis into the joint during arthrocentesis, which poses a potential risk for septic arthritis. However, tissue contamination of the joint may be reduced by flushing saline through the needle into the subcutaneous tissues prior to entering the joint capsule.

BackgroundCurrently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma.MethodsClinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers.ResultsEight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients.ConclusionsThe clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

No abstract