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Is the GPSM scoring algorithm for patients with prostate cancer valid in the contemporary era?

机译:在当代,前列腺癌患者的GPSM评分算法是否有效?

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PURPOSE: The GPSM (Gleason, prostate specific antigen, seminal vesicle and margin status) scoring algorithm is a user friendly model for predicting biochemical recurrence following radical retropubic prostatectomy. It was developed from patients who underwent radical retropubic prostatectomy from 1990 to 1993. We investigated the predictive ability of GPSM in the contemporary era. MATERIALS AND METHODS: We identified 2,728 patients who underwent radical retropubic prostatectomy for prostate cancer from 1997 to 2000 at our institution. Cox proportional hazard regression models were used to develop multivariate scoring algorithms. Harrell's measure of concordance was used to compare the competing models. RESULTS: In the contemporary era each GPSM feature remained significantly associated with biochemical recurrence in a multivariate model (each p <0.001). Harrell's measure of concordance for the algorithm was 0.706 vs 0.718 in the original study. After adjusting for GPSM on multivariate analysis Gleason primary 4/5 (p <0.001), DNA ploidy (p = 0.018) and tumor size (p <0.001) were associated with biochemical recurrence. However, none of these features increased Harrell's measure of concordance greater than 0.01 when added to the GPSM model. In addition, using the original 1990 to 1993 cohort, 495 patients with a GPSM score of 10 or greater were significantly more likely to die of prostate cancer compared with 2,169 with a GPSM score of less than 10 (at 15 years 13% vs 2%, HR 6.5, p <0.001). CONCLUSIONS: The GPSM scoring algorithm is a simple predictive model that remains associated with biochemical recurrence in the contemporary era. In addition, to our knowledge the GPSM algorithm is the first nomogram associated with survival in patients with prostate cancer.
机译:目的:GPSM(格里森,前列腺特异抗原,精囊和边缘状态)评分算法是一种用户友好的模型,用于预测根治性耻骨后前列腺切除术后的生化复发。它是从1990年至1993年接受根治性耻骨后前列腺切除术的患者开发的。我们调查了GPSM在当代的预测能力。材料与方法:我们确定了从1997年至2000年在我院接受过2728例行前列腺癌根治性前列腺切除术的患者。使用Cox比例风险回归模型开发多元评分算法。 Harrell的一致性度量用于比较竞争模型。结果:在现代时代,在多变量模型中,每个GPSM功能仍与生化复发显着相关(每个p <0.001)。在原始研究中,Harrell对算法的一致性度量为0.706对0.718。在多变量分析中对GPSM进行调整后,Gleason primary 4/5(p <0.001),DNA倍性(p = 0.018)和肿瘤大小(p <0.001)与生化复发相关。但是,添加到GPSM模型后,这些功能均无法使Harrell的一致性度量值大于0.01。此外,使用最初的1990年至1993年队列,与GPSM得分小于10的2169人相比,495例GPSM得分为10或更高的患者更有可能死于前列腺癌(15岁时13%比2% ,HR 6.5,p <0.001)。结论:GPSM评分算法是一个简单的预测模型,在当代仍与生化复发相关。另外,据我们所知,GPSM算法是与前列腺癌患者生存率相关的第一个诺模图。

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