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Background: Pyogenic spondylitis (PS) and Brucella spondylitis (BS) are common spinal infections with similar manifestations, making their differentiation challenging. This study aimed to explore the potential of CT-based radiomics features combined with machine learning algorithms to differentiate PS from BS. Methods: This retrospective study involved the collection of clinical and radiological information from 138 patients diagnosed with either PS or BS in our hospital between January 2017 and December 2022, based on histopathology examination and/or germ isolations. The region of interest (ROI) was defined by two radiologists using a 3D Slicer open-source platform, utilizing blind analysis of sagittal CT images against histopathological examination results. PyRadiomics, a Python package, was utilized to extract ROI features. Several methods were performed to reduce the dimensionality of the extracted features. Machine learning algorithms were trained and evaluated using techniques like the area under the receiver operating characteristic curve (AUC; confusion matrix-related metrics, calibration plot, and decision curve analysis to assess their ability to differentiate PS from BS. Additionally, permutation feature importance (PFI; local interpretable model-agnostic explanations (LIME; and Shapley additive explanation (SHAP) techniques were utilized to gain insights into the interpretabilities of the models that are otherwise considered opaque black-boxes. Results: A total of 15 radiomics features were screened during the analysis. The AUC value and Brier score of best the model were 0.88 and 0.13, respectively. The calibration plot and decision curve analysis displayed higher clinical efficiency in the differential diagnosis. According to the interpretation results, the most impactful features on the model output were wavelet LHL small dependence low gray-level emphasis (GLDN). Conclusion: The CT-based radiomics models that we developed have proven to be useful in reliably differentiating between PS and BS at an early stage and can provide a reliable explanation for the classification results.
RESUMO
OBJECTIVE: To evaluate the efficacy of antifibrinolytic agents in coronary artery bypass grafting (CABG) patients receiving antiplatelet. METHODS: We searched PubMed, EMbase, Highwire, CENTREN and its affiliated clinical trial registration data center, CBMdisc and CNKI databases from 2000 to 2010. Randomized controlled trials investigating the efficacy of anti-fibrinolytic agents (aprotinin and tranexamic acid) in CABG patients were identified. Study selection and meta-analysis were conducted according to the Cochrane Handbook for systematic reviews. Date were extracted from these trials by 2 reviewers independently and analyzed by RevMan 5.0 software. RESULT: Eleven RCT trials (n = 725) were included and data confirmed the efficacy of antifibrinolytic therapy in terms of reducing bleeding within 24 hours after operation (MD = -306.5 ml, 95%CI: -351.52 to -261.52, P < 0.01), number of patients who need blood transfusion (OR = 0.37, 95%CI: 0.26 to 0.51, P < 0.01), amount of blood transfusion (MD = -0.59 U, 95%CI: -0.69 to -0.50, P < 0.01), surgical re-exploration(OR = 0.27, 95%CI: 0.09 to 0.78, P = 0.02), and thrombotic events (OR = 0.49, 95%CI: 0.25 to 0.97, P = 0.04) in CABG patients receiving antiplatelet, while compared with blank treatment. CONCLUSION: This analysis showed that antifibrinolytic agents are effective for reducing bleeding within 24 hours after operation, amount of blood transfusion, surgical re-exploration and do not increase the incidence of thrombotic events in CABG patients receiving antiplatelet before operation.