ObjectiveTo compare the clinical outcomes of transcatheter aortic valve implantation (TAVI) in oncology and non-oncology patients with severe aortic stenosis (AS).MethodsA computer-based search in PubMed, The Cochrane Library, EMbase, CBM, CNKI and Wanfang databases from their date of inception to December 2021 was performed, together with reference screening, to identify eligible clinical trials. Two investigators screened the articles, extracted data, and evaluated quality independently. RevMan 5.3 and Stata 12.0 softwares were used for meta-analysis.ResultsThe selected 8 cohort studies contained 57 988 patients, including 12 335 cancer patients and 45 653 non-cancer patients. The results of meta-analysis showed that in patients with cancer, the 30-day mortality [OR=0.74, 95%CI (0.65, 0.84), I2=0%, P<0.000 01], stroke [OR=0.87, 95%CI (0.76, 0.99), I2=0%, P=0.04] and acute kidney injury [OR=0.81, 95%CI (0.76, 0.85), I2=49%, P<0.000 01] were lower than those in patients without cancer. The 1-year mortality [OR=1.46, 95%CI (1.15, 1.86), I2=62%, P=0.002] and late mortality [OR=1.51, 95%CI (1.24, 1.85), I2=61%, P<0.000 1] were higher in patients with cancer.ConclusionIt is effective and safe in cancer patients with severe AS undergoing TAVI. However, compared with patients without cancer, it is still high in long-term mortality, and further study of the role of TAVI in cancer patients with AS is necessary.
With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.
ObjectiveTo combine specific examples and R Studio language code, to apply the Bayesian quantile regression method in the analysis of clinical medicine data, and show the advantages of Bayesian quantile regression method, so as to provide references for improving the accuracy of medical research. Methods The clinical data of 250 patients with knee osteoarthritis from the capital special research on the application of clinical characteristics project were used. A Bayesian quantile regression model based on data set was constructed to explore the relationship between the level of serum IgG and the age of the patients. Results The Monte Carlo algorithm converge can judge the efficiency of parameter estimation based on Gibbs sampling which was used to draw samples from the posterior distribution of parameters in Bayesian quantile regression. By generating the parameter into the regression formula, we can obtain the regression under different quantiles: Y1=−6.022 063 47+2.026 913 73X−0.015 077 69X2……Y5=24.610 542 414−0.395 059 497X+0.004 205 064X2. It can be found that the serum level of IgG was obviously increased with age. Conclusion Bayesian quantile regression parameter estimation results are accurate and highly credible, and reliable parameter information can be obtained even under small sample conditions. It has great advantages in the research of clinical medicine data and has certain promotional value.