目的:探讨≥2次行前列腺特异性抗原(prostate specific antigen,PSA)检测且每次PSA检测结果均在4～10 ng/mL的患者行前列腺穿刺活检阳性的危险因素,构建列线图预测模型并进行评估.方法:收集苏北人民医院2019年1月15日-2022年9月26日期间≥2次行PSA检测且每次PSA检测结果均在4～10 ng/mL患者的相关临床资料,穿刺方法均结合运用认知融合靶向+系统穿刺,采用单因素分析和多因素logistic回归分析筛选影响这类患者行前列腺穿刺活检阳性的危险因素,将筛选出的独立危险因素放入列线图构建预测模型,并通过受试者工作特征(receiver operating characteristic,ROC)曲线分析、校准图和决策曲线分析评估列线图模型的区分度与准确度.结果:共纳入480例患者,其中213例(44.37％)前列腺穿刺活检结果为阳性,多因素logistic回归分析显示,年龄、前列腺体积、PSA速度、PSA倍增时间、PSA波动率是该类患者行前列腺穿刺活检阳性的危险因素(P＜0.05);基于多因素logistic回归分析构建列线图预测模型,ROC曲线下面积为0.859,内部验证校准良好,列线图校准曲线斜率接近1,Hosmer-Lemeshow拟合优度检验为20.869,P=0.171.结论:基于本次研究构建的列线图模型,具有良好的区分度与一致性,可以为≥2次行PSA检测且每次PSA检测结果均在4～10 ng/mL的患者了解患病风险及泌尿科医生做出临床决策提供有效帮助.
Construction and verification of nomogram prediction model for positive risk of prostate biopsy in patients with PSA fluctuation
Objective:To explore the risk factors of positive prostate biopsy in patients with two or more prostate specific antigen(PSA)tests and each PSA test result in 4-10 ng/mL,and to construct a nomogram pre-diction model and evaluate it.Methods:We collected clinical data related to the population of patients who under-went two or more PSA tests at Subei People's Hospital from January 15th,2019 to September 26th,2022,with each PSA test result ranging from 4 to 10 ng/mL.Univariate analysis and multivariate logistic regression analysis were used to screen for risk factors that affected two or more PSA tests and each PSA test result ranging from 4 to 10 ng/mL,then the screened independent risk factors were put into the nomogram to build a prediction model.The differentiation and accuracy of the nomogram model were evaluated through receiver operating characteristic(ROC)analysis,calibration chart and decision curve analysis.Results:A total of 480 patients with two or more PSA tests and each PSA test 4-10 ng/mL were included in this retrospective study.Among them,213(44.37％)prostate biopsy results were positive.Multivariate logistic regression analysis showed that age,pros-tate volume,PSA velocity,PSA doubling time,and PSA fluctuation rate were risk factors for positive prostate bi-opsy in patients with two or more PSA tests and each PSA test 4-10 ng/mL(P＜0.05).Based on multivariate logistic regression analysis,a nomogram prediction model was constructed.The area under the ROC curve was 0.859,and the internal validation calibration was good.The slope of the nomogram calibration curve was close to 1,and the Hosmer-Lemeshow goodness of fit test=20.869,P=0.171.Conclusion:The nomogram model con-structed based on this study has good discrimination and consistency.It can provide effective help for patients who have two or more PSA tests and each PSA test result 4-10 ng/mL to understand the risk of disease and for urolo-gists to make clinical decisions.
prostatic neoplasmsprostate punctureprostate specific antigen kineticsprediction model
NETL
NSTL
万方数据
