New tricks with old dogs: personalised medicine and clinical trials

We provide a didactic example of how clinical trials can accommodate individualised patient information relative to design and analysis.     

Nomogram to predict insignificant prostate cancer at radical prostatectomy in Korean men: a multi-center study

Purpose

Due to the availability of serum prostate specific antigen (PSA) testing, the detection rate of insignificant prostate cancer (IPC) is increasing. To ensure better treatment decisions, we developed a nomogram to predict the probability of IPC.

Materials and Methods

The study population consisted of 1,471 patients who were treated at multiple institutions by radical prostatectomy without neoadjuvant therapy from 1995 to 2008. We obtained nonrandom samples of n = 1,031 for nomogram development, leaving n = 440 for nomogram validation. IPC was defined as pathologic organ-confined disease and a tumor volume of 0.5 cc or less without Gleason grade 4 or 5. Multivariate logistic regression model (MLRM) coefficients were used to construct a nomogram to predict IPC from five variables, including serum prostate specific antigen, clinical stage, biopsy Gleason score, positive cores ratio and maximum % of tumor in any core. The performance characteristics were internally validated from 200 bootstrap resamples to reduce overfit bias. External validation was also performed in another cohort.

Results

Overall, 67 (6.5%) patients had a so-called "insignificant" tumor in nomogram development cohort. PSA, clinical stage, biopsy Gleason score, positive core ratio and maximum % of biopsy tumor represented significant predictors of the presence of IPC. The resulting nomogram had excellent discrimination accuracy, with a bootstrapped concordance index of 0.827.

Conclusion

Our current nomogram provides sufficiently accurate information in clinical practice that may be useful to patients and clinicians when various treatment options for screen-detected prostate cancer are considered.     

南昌市西湖区结直肠癌筛查结果及进展期结直肠腺瘤列线图预测模型的构建#br#

目的　统计南昌市西湖区结直肠癌筛查情况,同时构建列线图预测模型,评价其对进展期结直肠腺瘤的预测价值。方法　选取进行结直肠癌筛查的40～74岁人群,共计10 618例。所有研究对象经2次粪便隐血试验（FOBT）及调查问卷确定高危人群。高危人群接受结肠镜检查,统计5年间结直肠疾病的检出情况。根据筛检结果,比较进展期和非进展期结直肠腺瘤患者的临床特征差异,Logistic回归分析罹患进展期结直肠腺瘤的危险因素,根据筛选出的危险因素,构建列线图预测模型,并评价其预测价值。结果　10 168例参与筛查者中,初步筛查确定高危人群2 078例,其中1 573例（75.7%）完成结肠镜检查。1 573例中共检出结直肠疾病584例（37.1%）,包括结直肠癌18例（1.1%）,进展期腺瘤79例（5.0%）,非进展期腺瘤224例（14.2%）,非腺瘤性息肉254例（16.2%）,炎症性肠病9例（0.6%）。单因素分析及多因素Logistic回归分析结果显示,男性、高龄、肥胖、有结肠癌家族史、饮酒、吸烟、喜食红肉、腺瘤大体形态呈有蒂或无蒂是进展期结直肠腺瘤的主要危险因素。纳入上述指标绘制列线图,预测受试者工作特征（ROC）曲线下面积（AUC）为0.798（95%CI：0.737~0.859）,内部验证C指数为0.797。校准曲线显示,该列线图预后模型预测的发生率与实际观察的发生率一致性良好（χ2=5.169,P=0.739）。结论　南昌市西湖区40~74岁人群结肠癌和进展期结直肠腺瘤检出率均较低。高龄、男性、肥胖、饮酒、吸烟、有结直肠肿瘤家族史对出现进展期结直肠腺瘤的风险明显升高,根据上述因素构建的列线图模型预测价值较好。     

心功能不全合并下肢动脉硬化闭塞症的危险因素及列线图模型的建立

目的 探讨心功能不全患者发生下肢动脉硬化闭塞症（ASO）的危险因素,构建及验证列线图风险预测模型。方法 选取心功能不全患者319例,依据下肢动脉彩超结果是否存在下肢动脉狭窄或闭塞分为ASO阳性组（161例）和ASO阴性组（158例）。收集患者性别、年龄、吸烟史、饮酒史、高血压史、美国纽约心脏病协会（NYHA）心功能分级...