目的比较拉米呋定与乙肝疫苗方案预防乙型肝炎核心抗体(hepatitis B core antibody,HBcAb)阳性供肝儿童肝移植术后新发乙型肝炎病毒(hepatitis B virus,HBV)感染效果。方法对天津市第一中心医院自2013年5月—2019年6月251例接受HBcAb阳...目的比较拉米呋定与乙肝疫苗方案预防乙型肝炎核心抗体(hepatitis B core antibody,HBcAb)阳性供肝儿童肝移植术后新发乙型肝炎病毒(hepatitis B virus,HBV)感染效果。方法对天津市第一中心医院自2013年5月—2019年6月251例接受HBcAb阳性供肝儿童肝移植的资料进行回顾性分析,依据采用预防方案的不同分为拉米呋定组和乙肝疫苗组,对两组患儿的新发乙肝病毒感染情况以及临床资料进行比较分析。结果拉米呋定组45例和乙肝疫苗组206例,两组在供受者的临床特征方面无显著差异,两组的新发乙肝病毒感染例数分别为5例(11.1%)和10例(4.9%),发生率无显著统计意义(P=0.075),停用拉米呋定与新发乙肝存在关系。结论单用拉米呋定和乙肝疫苗均是有效预防HBcAb阳性供肝术后新发乙肝的方案,停用拉米呋定会增加新发乙肝的风险。展开更多
Background:Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma,accounting for 20% of all urinary system tumors.Approximately 70% of cases are localized at diagnosis,and 30%are me...Background:Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma,accounting for 20% of all urinary system tumors.Approximately 70% of cases are localized at diagnosis,and 30%are metastatic.Most localized kidney cancers can be cured by surgery,but most metastatic patients relapse after surgery and eventually die of kidney cancer.Therefore,accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis.Methods:This study used the data of 12,394 kidney cancer patients from the surveillance,epidemiology,and end results database to construct a research cohort related to kidney cancer survival and metastasis.Eight machine learning models(including support vector machines,logistic regression,decision tree,random forest,XGBoost,AdaBoost,K-nearest neighbors,and multilayer perceptron)were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators(accuracy,precision,sensitivity,specificity,F1 score,and area under the receiver operating characteristic[AUROC])were used to verify,evaluate,and optimize the models.Results:Among the eight machine learning models,Logistic Regression has the highest AUROC in both prediction scenarios.For 3-year survival prediction,the Logistic Regression model had an accuracy of 0.684,a sensitivity of 0.702,a specificity of 0.670,a precision of 0.686,an F1 score of 0.683,and an AUROC of 0.741.For tumor metastasis prediction,the Logistic Regression model had an accuracy of 0.800,a sensitivity of 0.540,a specificity of 0.830,a precision of 0.769,an F1 score of 0.772,and an AUROC of 0.804.Conclusion:In this study,we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer.The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients.展开更多
文摘目的比较拉米呋定与乙肝疫苗方案预防乙型肝炎核心抗体(hepatitis B core antibody,HBcAb)阳性供肝儿童肝移植术后新发乙型肝炎病毒(hepatitis B virus,HBV)感染效果。方法对天津市第一中心医院自2013年5月—2019年6月251例接受HBcAb阳性供肝儿童肝移植的资料进行回顾性分析,依据采用预防方案的不同分为拉米呋定组和乙肝疫苗组,对两组患儿的新发乙肝病毒感染情况以及临床资料进行比较分析。结果拉米呋定组45例和乙肝疫苗组206例,两组在供受者的临床特征方面无显著差异,两组的新发乙肝病毒感染例数分别为5例(11.1%)和10例(4.9%),发生率无显著统计意义(P=0.075),停用拉米呋定与新发乙肝存在关系。结论单用拉米呋定和乙肝疫苗均是有效预防HBcAb阳性供肝术后新发乙肝的方案,停用拉米呋定会增加新发乙肝的风险。
基金CAMS Innovation Fund for Medical Sciences(CIFMS),Grant/Award Number:2021-I2M-1-066Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences,Grant/Award Number:2019PT320027+1 种基金Beijing Hope Run Special Fund of Cancer Foundation of China,Grant/Award Number:LC2019A04Fundamental Research Funds for the Central Universities,Grant/Award Number:3332020023。
文摘Background:Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma,accounting for 20% of all urinary system tumors.Approximately 70% of cases are localized at diagnosis,and 30%are metastatic.Most localized kidney cancers can be cured by surgery,but most metastatic patients relapse after surgery and eventually die of kidney cancer.Therefore,accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis.Methods:This study used the data of 12,394 kidney cancer patients from the surveillance,epidemiology,and end results database to construct a research cohort related to kidney cancer survival and metastasis.Eight machine learning models(including support vector machines,logistic regression,decision tree,random forest,XGBoost,AdaBoost,K-nearest neighbors,and multilayer perceptron)were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators(accuracy,precision,sensitivity,specificity,F1 score,and area under the receiver operating characteristic[AUROC])were used to verify,evaluate,and optimize the models.Results:Among the eight machine learning models,Logistic Regression has the highest AUROC in both prediction scenarios.For 3-year survival prediction,the Logistic Regression model had an accuracy of 0.684,a sensitivity of 0.702,a specificity of 0.670,a precision of 0.686,an F1 score of 0.683,and an AUROC of 0.741.For tumor metastasis prediction,the Logistic Regression model had an accuracy of 0.800,a sensitivity of 0.540,a specificity of 0.830,a precision of 0.769,an F1 score of 0.772,and an AUROC of 0.804.Conclusion:In this study,we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer.The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients.
