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

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.     

Diagnosis of isolated high-grade prostatic intra-epithelial neoplasia: proposal of a nomogram for the prediction of cancer detection at saturation re-biopsy

Study Type – Diagnostic (case series) Level of Evidence 4 What's known on the subject? and What does the study add? Multifocality, age, PSA values, and biopsy protocols regarding the predictive value of high grade PIN have been discussed extensively in the literature. Our study developed for the first time a predictive nomogram that could be helpful for patient counselling and to guide the urologist to perform rPBX after an initial diagnosis of isolated HGPIN.

OBJECTIVE

• ? To evaluate factors that may predict prostate cancer (PCa) detection after the initial diagnosis of high‐grade prostatic intra‐epithelial neoplasia (HGPIN) on prostate biopsy (PBx) with six to 24 random cores.

PATIENTS AND METHODS

• ? We retrospectively evaluated 262 patients submitted from 1998 to 2007 to prostate re‐biopsy (rPBx) after an initial HGPIN diagnosis in tertiary academic centres.
• ? HGPIN diagnosis was obtained on initial systematic PBx with six to 24 random cores.
• ? All patients were re‐biopsied with a ‘saturation’ rPBx with 20–26 cores, with a median time to rPBx of 12 months.
• ? All slides were reviewed by expert uropathologists.

RESULTS

• ? Plurifocal HGPIN (pHGPIN) was found in 115 patients and monofocal HGPIN (mHGPIN) was found in 147 patients.
• ? In total, 108 and 154 patients, respectively, were submitted to >12‐core initial PBx and ≤12‐core initial PBx.
• ? Overall PCa detection at rPBx was 31.7%. PSA level (7.7 vs 6.6 ng/mL; P= 0.031) and age (68 vs 64 years; P= 0.001) were significantly higher in patients with PCa at rPBx.
• ? PCa detection was significantly higher in patients with a ≤12‐core initial PBx than in those with a >12‐core initial PBx (37.6% vs 23.1%; P= 0.01), as well as in patients with pHGPIN than in those with mHGPIN (40% vs 25.1%; P= 0.013).
• ? At multivariable analysis, PSA level (P= 0.041; hazards ratio, HR, 1.08), age (P < 0.001; HR, 1.09), pHGPIN (P= 0.031; HR, 1.97) and ≤12‐core initial PBx (P= 0.012; HR, 1.95) were independent predictors of PCa detection.
• ? A nomogram including these four variables achieved 72% accuracy for predicting PCa detection after an initial HGPIN diagnosis.

CONCLUSIONS

• ? PCa detection on saturation rPBx after an initial diagnosis of HGPIN is significantly higher in patients with a ≤12‐core initial PBx than those with a >12‐core initial PBx and in patients with pHGPIN than in those with mHGPIN.
• ? We developed a simple prognostic tool for the prediction of PCa detection in patients with initial HGPIN diagnosis who were undergoing saturation rPBx.

     

腺病毒感染患儿急性胃肠炎表型列线图预测模型的建立及验证

摘要：目的　构建腺病毒感染患儿发生急性胃肠炎表型的列线图预测模型,并验证其可行性。方法　选取腺病毒感染患儿144例为训练集,其中40例（27.8%）为急性胃肠炎表型,104例（72.2%）为呼吸道表型。收集患儿年龄、性别、发热天数、扁桃体渗出情况、结膜充血状态、入院后丙氨酸转氨酶（ALT）、天冬氨酸转氨酶（AST）、γ-谷氨酰转肽酶（γ-GT）、白细胞介素（IL）-6、乳酸脱氢酶（LDH）、CD4＋T细胞百分比、CD8＋T细胞百分比及CD4＋T细胞/CD8＋T细胞比值。采用最小绝对值收敛和选择算子（LASSO）回归筛选急性胃肠炎表型的预测因子,多因素Logistic回归分析建立风险预测模型并绘制列线图。采用受试者工作特征（ROC）曲线、Brier评分分别评价模型的判别能力和校准能力。另选取35例腺病毒感染患儿对预测模型进行外部验证。结果　与呼吸道表型组相比,胃肠炎表型组患儿年龄偏小,女性比例、ALT水平较高,发热天数较短,IL-6、LDH水平较低（P＜0.05）。LASSO回归分析显示年龄、性别、发热天数、LDH是急性胃肠炎表型的预测因子。多因素Logistic回归分析显示女性、年龄小、发热时间短、LDH降低是儿童HAdV感染胃肠炎表型的危险因素。纳入上述因素构建的列线图模型的ROC曲线下面积（AUC）为0.951（95% CI：0.923~0.984）,敏感度为82.50%,特异度为95.19%。外部验证集中,35例患儿中8例为急性胃肠炎表型,AUC为0.925（95% CI：0.821~0.998）,敏感度为75.42%,特异度为95.90%。结论　本研究建立的预测模型适用于腺病毒感染患儿出现急性胃肠炎表型的早期诊断,预测效果较好,具有一定的临床价值。     

Predicting the extent of nodal involvement for node positive breast cancer patients: Development and validation of a novel tool

Background

This study aimed to develop an easy to use prediction model to predict the risk of having a total of 1 to 2, ≥3, or ≥4 positive axillary lymph nodes (LNs), for patients with sentinel lymph node (SLN) positive breast cancer.

Methods

Data of 911 SLN positive breast cancer patients were used for model development. The model was validated externally in an independent population of 180 patients with SLN positive breast cancer.

Results

Final pathology after ALND showed additional positive LN for 259 (28%) of the patients. A total of 726 (81%) out of 911 patients had a total of 1 to 2 positive nodes, whereas 175 (19%) had ≥3 positive LNs. The model included three predictors: the tumor size (in mm), the presence of a negative SLN, and the size of the SLN metastases (in mm). At external validation, the model showed a good discriminative ability (area under the curve = 0.82; 95% confidence interval = 0.74-0.90) and good calibration over the full range of predicted probabilities.

Conclusion

This new and validated model predicts the extent of nodal involvement in node-positive breast cancer and will be useful for counseling patients regarding their personalized axillary treatment.     

Can nomograms be superior to other prediction tools?

Accurate estimates of the likelihood of treatment success, complications and long-term morbidity are essential for counselling and informed decision-making in patients with urological malignancies. Accurate risk estimates are also required for clinical trial design, to ensure homogeneous patient distribution. Nomograms, risk groupings, artificial neural networks (ANNs), probability tables, and classification and regression tree (CART) analyses represent the available decision aids that can be used within these tasks. We critically reviewed available decision aids (nomograms, risk groupings, ANNs, probability tables and CART analyses) and compared their ability to predict the outcome of interest. Of the available decision aids, nomograms provide individualized evidence-based and highly accurate risk estimates that facilitate management-related decisions. We suggest the use of nomograms for the purpose of evidence-based, individualized decision-making.     

Bayesian Pharmacokinetic Analysis of a Gentamicin Nomogram in Neonates: A Retrospective Study

Background: Although gentamicin is used extensively within the first week of life for suspected sepsis in neonates, little is known about the performance of gentamicin dosing nomograms in this population.Objective: The goal of our study was to retrospectively assess the performance of a gentamicin dosing nomogram in neonates given gentamicin during the first week after birth.Methods: In this retrospective study, gentamicin therapeutic drug monitoring data were collected during routine clinical care for all neonates who were born in St. Boniface General Hospital (Winnipeg, Manitoba, Canada) between January 1999 and April 2001 and given gentamicin during the first week after birth. We used Bayesian pharmacokinetic analysis to retrospectively assess the performance of our gentamicin dosing nomogram in neonates born at gestation ages <32 weeks, between 32 and 34 weeks, and >34 weeks. Bayesian pharmacokinetic values for parameters within groups were compared and used to explore predicted peak and trough serum gentamicin concentrations based on the institutional dosing nomogram.Results: In a total of 58 neonates, those neonates born at ≤34 weeks' gestation had a weight-normalized apparent volume of gentamicin distribution 1.6 times larger than infants born after 34 weeks' gestation (P<0.001), as identified by Bayesian analysis. Weight-normalized gentamicin clearance was 22% lower in the youngest age category (P<0.01). Only 33% of predicted peak serum gentamicin concentrations were >6 mg/L for neonates born at ≤34 weeks' gestation, whereas 90% were therapeutic in neonates born at >34 weeks' gestation (P<0.001). With the present nomogram, the likelihood of an indication for adjustment of the dosing regimen was 12.4-fold higher (95% CI, 3.5-43.7) for those neonates born at ≤34 weeks' gestation.Conclusions: These results have important clinical implications with regard to the advisability of determining peak serum gentamicin concentrations in neonates born at ≤34 weeks' gestation. Sampling of peak serum concentrations is indicated in this population to avoid underdosing and potential loss of therapeutic efficacy.