Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY, USA.
BJU Int. 2011 May;107(10):1562-9. doi: 10.1111/j.1464-410X.2010.09692.x. Epub 2010 Sep 28.
• To develop a '2010 Partin Nomogram' with total prostate-specific antigen (tPSA) as a continuous biomarker, in light of the fact that the current 2007 Partin Tables restrict the application of tPSA as a non-continuous biomarker by creating 'groups' for risk stratification with tPSA levels (ng/mL) of 0-2.5, 2.6-4.0, 4.1-6.0, 6.1-10.0 and >10.0. • To use a 'predictiveness curve' to calculate the percentile risk of a patient among the cohort.
• In all, 5730 and 1646 patients were treated with radical prostatectomy (without neoadjuvant therapy) between 2000 and 2005 at the Johns Hopkins Hospital (JHH) and University Clinic Hamburg-Eppendorf (UCHE), respectively. • Multinomial logistic regression analysis was performed to create a model for predicting the risk of the four non-ordered pathological stages, i.e. organ-confined disease (OC), extraprostatic extension (EPE), and seminal vesicle (SV+) and lymph node (LN+) involvement. • Patient-specific risk was modelled as a function of the B-spline basis of tPSA (with knots at the first, second and third quartiles), clinical stage (T1c, T2a, and T2b/T2c) and biopsy Gleason score (5-6, 3 + 4 = 7, 4 + 3 = 7, 8-10).
• The '2010 Partin Nomogram' calculates patient-specific absolute risk for all four pathological outcomes (OC, EPE, SV+, LN+) given a patient's preoperative clinical stage, tPSA and biopsy Gleason score. • While having similar performance in terms of calibration and discriminatory power, this new model provides a more accurate prediction of patients' pathological stage than the 2007 Partin Tables model. • The use of 'predictiveness curves' has also made it possible to obtain the percentile risk of a patient among the cohort and to gauge the impact of risk thresholds for making decisions regarding radical prostatectomy.
• The '2010 Partin Nomogram' using tPSA as a continuous biomarker together with the corresponding 'predictiveness curve' will help clinicians and patients to make improved treatment decisions.
开发一种基于总前列腺特异性抗原(tPSA)的“2010 年 Partin 列线图”,因为当前的 2007 年 Partin 表通过为 tPSA 水平(ng/mL)为 0-2.5、2.6-4.0、4.1-6.0、6.1-10.0 和>10.0 的风险分层创建“组”,限制了 tPSA 作为非连续生物标志物的应用。
使用“预测曲线”计算队列中患者的百分位风险。
2000 年至 2005 年间,分别有 5730 名和 1646 名患者在约翰霍普金斯医院(JHH)和汉堡-埃彭多夫大学诊所(UCHE)接受根治性前列腺切除术(无新辅助治疗)。
进行多项逻辑回归分析,以创建预测四个非有序病理阶段(局限于器官疾病(OC)、前列腺外扩展(EPE)、精囊(SV+)和淋巴结(LN+)受累)的风险模型。
患者特异性风险作为 tPSA(在第一个、第二个和第三个四分位数处有节点)、临床分期(T1c、T2a 和 T2b/T2c)和活检 Gleason 评分(5-6、3+4=7、4+3=7、8-10)的 B 样条基函数的函数进行建模。
“2010 年 Partin 列线图”根据患者术前临床分期、tPSA 和活检 Gleason 评分,计算患者对所有四个病理结果(OC、EPE、SV+、LN+)的特定风险。
虽然在校准和区分能力方面表现相似,但这种新模型比 2007 年 Partin 表模型更准确地预测患者的病理分期。
使用“预测曲线”还可以获得队列中患者的百分位风险,并评估风险阈值对根治性前列腺切除术决策的影响。