Strobl Andreas N, Thompson Ian M, Vickers Andrew J, Ankerst Donna P
Department of Mathematics, Technical University Munich, Munich, Germany.
Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
J Urol. 2015 Jul;194(1):58-64. doi: 10.1016/j.juro.2015.01.092. Epub 2015 Jan 28.
We evaluate whether annual updating of the PCPT Risk Calculator would improve institutional validation compared to static use of the PCPT Risk Calculator alone.
Data from 5 international cohorts including SABOR, Cleveland Clinic, ProtecT, Tyrol and Durham VA, comprising 18,400 biopsies, were used to evaluate an institution specific annual recalibration of the PCPT Risk Calculator. Using all prior years as a training set and the current year as the test set, annual recalibrations of the PCPT Risk Calculator were compared to static use of the PCPT Risk Calculator in terms of AUC and the Hosmer-Lemeshow goodness of fit statistic.
For predicting high grade disease the median AUC (higher is better) of the recalibrated PCPT Risk Calculator (static PCPT Risk Calculator) across all test years for the 5 cohorts was 67.3 (67.5), 65.0 (60.4), 73.4 (73.4), 73.9 (74.1) and 69.6 (67.2), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated better fit for recalibration compared to the static PCPT Risk Calculator for Cleveland Clinic, ProtecT and the Durham VA but not for SABOR and Tyrol. For predicting overall cancer median AUC was 63.5 (62.7), 61.0 (57.3), 62.1 (62.5), 66.9 (67.3) and 68.5 (65.5), respectively, and median Hosmer-Lemeshow goodness of fit statistics indicated a better fit for recalibration in all cohorts except for Tyrol.
A simple method has been provided to tailor the PCPT Risk Calculator to individual hospitals to optimize its accuracy for the patient population at hand.
我们评估与单独静态使用前列腺癌预防试验(PCPT)风险计算器相比,每年更新该计算器是否会改善机构验证。
来自包括SABOR、克利夫兰诊所、前列腺癌检测与治疗试验(ProtecT)、蒂罗尔和达勒姆退伍军人事务部在内的5个国际队列的数据,共18400份活检样本,用于评估PCPT风险计算器的机构特定年度重新校准。以前所有年份的数据作为训练集,当前年份的数据作为测试集,将PCPT风险计算器的年度重新校准与静态使用该计算器在曲线下面积(AUC)和霍斯默-莱梅肖拟合优度统计方面进行比较。
对于预测高级别疾病,在所有测试年份中,5个队列重新校准的PCPT风险计算器(静态PCPT风险计算器)的中位AUC(越高越好)分别为67.3(67.5)、65.0(60.4)、73.4(73.4)、73.9(74.1)和69.6(67.2),中位霍斯默-莱梅肖拟合优度统计表明,与静态PCPT风险计算器相比,克利夫兰诊所、ProtecT和达勒姆退伍军人事务部重新校准的拟合更好,但SABOR和蒂罗尔并非如此。对于预测总体癌症中位AUC分别为63.5(62.7)、61.0(57.3)、62.1(62.5)、66.9(67.3)和68.5(65.5),中位霍斯默-莱梅肖拟合优度统计表明,除蒂罗尔外,所有队列重新校准的拟合更好。
已提供一种简单方法,可根据各个医院的情况调整PCPT风险计算器,以优化其针对手头患者群体的准确性。