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1.  Non-linear modeling was applied thoughtfully for risk prediction: the Prostate Biopsy Collaborative group 
Journal of clinical epidemiology  2014;68(4):426-434.
Objective
We aimed to compare non-linear modelling methods for handling continuous predictors for reproducibility and transportability of prediction models.
Study Design and Setting
We analyzed four cohorts of previously unscreened men who underwent prostate biopsy for diagnosing prostate cancer. Continuous predictors of prostate cancer included prostate-specific antigen and prostate volume. The logistic regression models included linear terms, logarithmic terms, fractional polynomials of degree 1 or 2 (FP1 and FP2) or restricted cubic splines with 3 or 5 knots (RCS3 and RCS5). The resulting models were internally validated by bootstrap resampling, and externally validated in the cohorts not used at model development. Performance was assessed with the area under the ROC curve (AUC) and the calibration component of the Brier score (CAL).
Results
At internal validation models with FP2 or RCS5 showed slightly better performance than the other models (typically 0.004 difference in AUC and 0.001 in CAL). At external validation models containing logarithms, FP1 or RCS3 showed better performance (differences 0.01 and 0.002).
Conclusion
Flexible non-linear modelling methods led to better model performance at internal validation. However, when application of the model is intended across a wide range of settings, less flexible functions may be more appropriate to maximize external validity.
doi:10.1016/j.jclinepi.2014.11.022
PMCID: PMC4474141  PMID: 25777297
prediction models; non-linear modelling; internal validation; external validation; discrimination; calibration
2.  Comparative Effectiveness of Screening Strategies for Lynch Syndrome 
Background:
Colorectal cancer is the second leading cause of cancer death in the United States. Approximately 3% of colorectal cancers are associated with Lynch Syndrome. Controversy exists regarding the optimal screening strategy for Lynch Syndrome.
Methods:
Using an individual level microsimulation of a population affected by Lynch syndrome over several years, effectiveness and cost-effectiveness of 21 screening strategies were compared. Modeling assumptions were based upon published literature, and sensitivity analyses were performed for key assumptions. In a two-step process, the number of Lynch syndrome diagnoses (Step 1) and life-years gained as a result of foreknowledge of Lynch syndrome in otherwise healthy carriers (Step 2) were measured.
Results:
The optimal strategy was sequential screening for probands starting with a predictive model, then immunohistochemistry for mismatch repair protein expression (IHC), followed by germline mutation testing (incremental cost-effectiveness ratio [ICER] of $35 143 per life-year gained). The strategies of IHC + BRAF, germline testing and universal germline testing of colon cancer probands had ICERs of $144 117 and $996 878, respectively.
Conclusions:
This analysis suggests that the initial step in screening for Lynch Syndrome should be the use of predictive models in probands. Universal tumor testing and general population screening strategies are not cost-effective. When family history is unavailable, alternate strategies are appropriate. Documentation of family history and screening for Lynch Syndrome using a predictive model may be considered a quality-of-care measure for patients with colorectal cancer.
doi:10.1093/jnci/djv005
PMCID: PMC4402362  PMID: 25794514
3.  Development of a Nomogram Model Predicting Current Bone Scan Positivity in Patients Treated with Androgen-Deprivation Therapy for Prostate Cancer 
Frontiers in Oncology  2014;4:296.
Purpose: To develop a nomogram predictive of current bone scan positivity in patients receiving androgen-deprivation therapy (ADT) for advanced prostate cancer; to augment clinical judgment and highlight patients in need of additional imaging investigations.
Materials and methods: A retrospective chart review of bone scan records (conventional 99mTc-scintigraphy) of 1,293 patients who received ADT at the Memorial Sloan-Kettering Cancer Center from 2000 to 2011. Multivariable logistic regression analysis was used to identify variables suitable for inclusion in the nomogram. The probability of current bone scan positivity was determined using these variables and the predictive accuracy of the nomogram was quantified by concordance index.
Results: In total, 2,681 bone scan records were analyzed and 636 patients had a positive result. Overall, the median pre-scan prostate-specific antigen (PSA) level was 2.4 ng/ml; median PSA doubling time (PSADT) was 5.8 months. At the time of a positive scan, median PSA level was 8.2 ng/ml; 53% of patients had PSA <10 ng/ml; median PSADT was 4.0 months. Five variables were included in the nomogram: number of previous negative bone scans after initiating ADT, PSA level, Gleason grade sum, and history of radical prostatectomy and radiotherapy. A concordance index value of 0.721 was calculated for the nomogram. This was a retrospective study based on limited data in patients treated in a large cancer center who underwent conventional 99mTc bone scans, which themselves have inherent limitations.
Conclusion: This is the first nomogram to predict current bone scan positivity in ADT-treated prostate cancer patients, providing high predictive accuracy.
doi:10.3389/fonc.2014.00296
PMCID: PMC4209823  PMID: 25386410
non-steroidal anti-androgens; radionuclide imaging; nomogram; prostatic neoplasms; androgen-deprivation therapy; bone scan positivity
4.  Clinical characteristics, complications, comorbidities and treatment patterns among patients with type 2 diabetes mellitus in a large integrated health system 
Purpose
To compare the prevalence of diabetes-related complications and comorbidities, clinical characteristics, glycemic control, and treatment patterns in patients with type 2 diabetes (T2D) within a large integrated healthcare system in 2008 vs 2013.
Methods
An electronic health record system was used to create a cross-sectional summary of all patients with T2D as on 1 July 2008 and 1 July 2013. Differences between the two data sets were assessed after adjusting for age, gender, race, and household income.
Results
In 2008 and 2013, 24 493 and 41 582 patients with T2D were identified, respectively, of which the majority were male (52.3% and 50.1%) and Caucasian (79% and 75.2%). The mean ages (years) were 64.8 and 64.3. The percentages of patients across the defined A1C categories were 64.3 and 66.7 for <7%, 21.1 and 18.8 for 7–7.9%, 7.8 and 7.5 for 8–8.9%, and 6.8 and 7.0 for ≥9% in 2008 and 2013, respectively. The most prevalent T2D-related comorbidities were hypertension (82.5% and 87.2%) and cardiovascular disease (26.9% and 22.3%) in 2008 and 2013, respectively. Thiazolidinedione and sulfonylurea use decreased, whereas metformin and dipeptidyl peptidase-4 inhibitor use increased in the 5-year period.
Conclusions
Patients with T2D are characterized by a high number of comorbidities. Over 85% of the patients had an A1C<8% within our integrated health delivery system in 2008 and 2013. In 2008 and 2013, metformin therapy was the most commonly utilized antidiabetic agent, and sulfonylureas were the most commonly utilized oral antidiabetic agent in combination with metformin. As integrated health systems assume greater shared financial risk in newer payment models, achieving glycemic targets (A1C) and the management of comorbidities will become ever-more important, for preventing diabetes-related complications, as well as to ensure reimbursement for the medical care that is rendered to patients with diabetes.
doi:10.1136/bmjdrc-2015-000093
PMCID: PMC4513350  PMID: 26217493
A1C; Chronic Diabetic Complications; Chronic Disease Epidemiology; Chronic Illness Management
5.  Temporal Trends in Percutaneous Coronary Intervention Associated Acute Cerebrovascular Accident (From the 1998–2008 Nationwide Inpatient Sample [NIS] Database) 
The American journal of cardiology  2014;114(2):206-213.
Acute cerebrovascular accident (CVA) after percutaneous coronary intervention (PCI) for acute coronary syndrome (ACS) and coronary artery disease (CAD) is associated with high morbidity and mortality. Nationwide Inpatient Sample from 1998 to 2008 was utilized to identify 1,552,602 PCIs performed for ACS and CAD. We assessed temporal trends in the incidence, predictors and prognostic impact of CVA in a broad range of patients undergoing PCI. The overall incidence of CVA was 0.56% (95% confidence interval (CI), 0.55%–0.57%). The incidence of CVA remained unchanged over the study period (adjusted p for trend = 0.2271). The overall mortality in the CVA group was 10.76% (95% CI, 10.1%–11.4%). The adjusted odds ratio (OR) of CVA for in-hospital mortality was 7.74 (95% CI, 7.00–8.57; p<0.0001); this remained high but decreased over the study period (adjusted p for trend <0.0001). Independent predictors of CVA included older age (OR, 1.03, 95% CI, 1.02–1.03; p<0.0001), disorder of lipid metabolism (OR, 1.31, 95% CI, 1.24–1.38; p<0.001), history of tobacco use (OR, 1.21, 95% CI, 1.10–1.34; p=0.0002), coronary atherosclerosis (OR 1.56, 95% CI, 1.43–1.71; p<0.0001), and IABP use (OR 1.39, 95% CI, 1.09–1.77; p=0.0073). A nomogram for predicting the probability of CVA achieved a concordance index of 0.73 and was well calibrated. In conclusion, the incidence of CVA associated with PCI has remained unchanged from 1998–2008 in face of improved equipment, techniques and adjunctive pharmacology. The risk of CVA associated in-hospital mortality is high; however, this risk has declined over the study period.
doi:10.1016/j.amjcard.2014.04.044
PMCID: PMC4089901  PMID: 24952927
Acute Cerebral Hemorrhage; Acute Cerebral Infarction; Percutaneous Coronary Intervention
6.  ClearCode34: A Prognostic Risk Predictor for Localized Clear Cell Renal Cell Carcinoma 
European urology  2014;66(1):77-84.
Background
Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.
Objective
To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.
Design, setting, and participants
A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.
Outcome measurements and statistical analysis
Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.
Results and limitations
The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.
Conclusions
The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.
Patient summary
We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.
doi:10.1016/j.eururo.2014.02.035
PMCID: PMC4058355  PMID: 24613583
Biomarker; ccRCC; Kidney cancer; Renal cell carcinoma; TCGA; Prognosis
7.  Predictive models for short- and long-term adverse outcomes following discharge in a contemporary population with acute coronary syndromes 
Although numerous risk-prediction models exist in patients presenting with acute coronary syndromes (ACS), they are subject to important short-comings, including lack of contemporary information. Short-term models are frequently biased by in-hospital events. Accordingly, we sought to create contemporary risk-prediction models for clinical outcomes following ACS up to 1 year following discharge. Models were constructed for death at 30 days and 1 year, death/myocardial infarction (MI)/revascularization at 30 days and death/MI at 1 year in consecutive patients presenting with ACS at our institution between 2006 and 2008, and discharged alive. Logistic regression was used to model the 30 day outcomes and Cox proportional hazards were used to model the 1 year outcomes. No linearity assumptions were made for continuous variables. The final model coefficients were used to create a prediction nomogram, which was incorporated into an online risk calculator. A total of 2,681 patients were included, of which about 9.5% presented with ST-elevation MI. All-cause mortality was 2.6% at 30 days and 13% at 1 year. Demographic, past medical history, laboratory, pharmacological and angiographic parameters were identified as being predictive of adverse ischemic outcomes at 30 days and 1 year. The c-indices for these models ranged from 0.73 to 0.82. Our study thus identified risk factors that are predictive of short- and long-term ischemic and revascularization outcomes in contemporary patients with ACS, and incorporated them into an easy-to-use online calculator, with equal or better discriminatory power than currently available models.
PMCID: PMC3584647  PMID: 23467552
Mortality; myocardial infarction; predictors; registry; revascularization
8.  Predicting Survival After Curative Colectomy for Cancer: Individualizing Colon Cancer Staging 
Journal of Clinical Oncology  2011;29(36):4796-4802.
Purpose
Cancer staging determines extent of disease, facilitating prognostication and treatment decision making. The American Joint Committee on Cancer (AJCC) TNM classification system is the most commonly used staging algorithm for colon cancer, categorizing patients on the basis of only these three variables (tumor, node, and metastasis). The purpose of this study was to extend the seventh edition of the AJCC staging system for colon cancer to incorporate additional information available from tumor registries, thereby improving prognostic accuracy.
Methods
Records from 128,853 patients with primary colon cancer reported to the Surveillance, Epidemiology and End Results Program from 1994 to 2005 were used to construct and validate three survival models for patients with primary curative-intent surgery. Independent training/test data sets were used to develop and test alternative models. The seventh edition TNM staging system was compared with models supplementing TNM staging with additional demographic and tumor variables available from the registry by calculating a concordance index, performing calibration, and identifying the area under receiver operating characteristic (ROC) curves.
Results
Inclusion of additional registry covariates improved prognostic estimates. The concordance index rose from 0.60 (95% CI, 0.59 to 0.61) for the AJCC model, with T- and N-stage variables, to 0.68 (95% CI, 0.67 to 0.68) for the model including tumor grade, number of collected metastatic lymph nodes, age, and sex. ROC curves for the extended model had higher sensitivity, at all values of specificity, than the TNM system; calibration curves indicated no deviation from the reference line.
Conclusion
Prognostic models incorporating readily available data elements outperform the current AJCC system. These models can assist in personalizing treatment and follow-up for patients with colon cancer.
doi:10.1200/JCO.2011.36.5080
PMCID: PMC3664036  PMID: 22084366
9.  Modeling Potential Time to Event Data with Competing Risks 
Lifetime data analysis  2013;20(2):316-334.
Patients receiving radical prostatectomy are at risk of metastasis or prostate cancer related death, and often need repeated clinical evaluations to determine whether additional adjuvant or salvage therapies are needed. Since the prostate cancer is a slowly progressing disease, and these additional therapies come with significant side effects, it is important for clinical decision making purposes to estimate a patient’s risk of cancer metastasis, in the presence of a competing risk by death, under the hypothetical condition that the patient does not receive any additional therapy. In observational studies, patients may receive additional therapy by choice; the time to metastasis without any therapy is often a potential outcome and not always observed. We study the competing risks model of Fine and Gray (1999) with adjustment for treatment choice by inverse probability censoring weighting (IPCW). The model can be fit using standard software for partial likelihood with double IPCW weights. The proposed methodology is used in a prostate cancer study to predict the post-prostatectomy cumulative incidence probability of cancer metastasis without additional adjuvant or salvage therapies.
doi:10.1007/s10985-013-9279-z
PMCID: PMC4197853  PMID: 24061908
Causal inference; Fine-Gray model; Inverse probability censoring weighting (IPCW); Landmark analysis; Potential outcomes; Prediction model
10.  The REDUCE metagram: a comprehensive prediction tool for determining the utility of dutasteride chemoprevention in men at risk for prostate cancer 
Frontiers in Oncology  2012;2:138.
Introduction: 5-alpha reductase inhibitors can reduce the risk of prostate cancer (PCa) but can be associated with significant side effects. A library of nomograms which predict the risk of clinical endpoints relevant to dutasteride treatment may help determine if chemoprevention is suited to the individual patient. Methods: Data from the REDUCE trial was used to identify predictive factors for 9 endpoints relevant to dutasteride treatment. Using the treatment and placebo groups from the biopsy cohort, Cox proportional hazards (PH) and competing risks regression (CRR) models were used to build 18 nomograms, whose predictive ability was measured by concordance index (CI) and calibration plots. Results: A total of 18 nomograms assessing the risks of cancer, high grade cancer, high grade prostatic intraepithelial neoplasia (HGPIN), atypical small acinar proliferation (ASAP), erectile dysfunction (ED), acute urinary retention (AUR), gynecomastia, urinary tract infection (UTI) and BPH-related surgery either on or off dutasteride were created. The nomograms for cancer, high grade cancer, ED, AUR, and BPH-related surgery demonstrated good discrimination and calibration while those for gynecomastia, UTI, HGPIN, and ASAP predicted no better than random chance. Conclusions: To aid patients in determining whether the benefits of dutasteride use outweigh the risks, we have developed a comprehensive metagram that can generate individualized risks of 9 outcomes relevant to men considering chemoprevention. Better models based on more predictive markers are needed for some of the endpoints but the current metagram demonstrates potential as a tool for patient counseling and decision-making that is accessible, intuitive, and clinically relevant.
doi:10.3389/fonc.2012.00138
PMCID: PMC3468831  PMID: 23087901
prostatic neoplasms; nomogram; chemoprevention; prediction
11.  Predicting an Optimal Outcome after Radical Prostatectomy: The “Trifecta” Nomogram 
The Journal of urology  2008;179(6):2207-2211.
Purpose
The optimal outcome after radical prostatectomy (RP) for clinically localized prostate cancer is freedom from biochemical recurrence (BCR) along with recovery of continence and erectile function, a so-called trifecta. We evaluated our series of open radical prostatectomy patients to determine the likelihood of this outcome and to develop a nomogram predicting the trifecta.
Material and Methods
We reviewed records of patients undergoing open RP for clinical stage T1c–T3a prostate cancer at our center during 2000–2006. Men were excluded if they received preoperative hormonal therapy, chemotherapy, or radiation therapy; if their pre-treatment PSA was >50 ng/ml; or if they were impotent or incontinent before RP; 1577 men were included in the study. Freedom from BCR was defined as post-RP PSA <0.2 ng/ml. Continence was defined as not having to wear any protective pads. Potency was defined as erections adequate for intercourse on the majority of attempts, with or without a phosphodiesterase-5 inhibitor.
Results
Mean patient age was 58 years and mean pretreatment PSA was 6.4 ng/ml. A trifecta outcome (cancer-free status with recovery of continence and potency) was achieved in 62% of patients. In a nomogram developed to predict the likelihood of the trifecta, baseline PSA was the major predictive factor. The area under the receiver operating characteristic curve for the nomogram was 0.773, and calibration appeared excellent.
Conclusions
A trifecta (optimal) outcome can be achieved in the majority of men undergoing RP. The nomogram will permit patients to estimate preoperatively their likelihood of an optimal outcome after RP.
doi:10.1016/j.juro.2008.01.106
PMCID: PMC4270351  PMID: 18423693
12.  ColoRectal Cancer Predicted Risk Online (CRC-PRO) Calculator Using Data from the Multi-Ethnic Cohort Study 
Background
Better risk predictions for colorectal cancer (CRC) could improve prevention strategies by allowing clinicians to more accurately identify high-risk individuals. The National Cancer Institute's CRC risk calculator was created by Freedman et al using case control data.
Methods
An online risk calculator was created using data from the Multi-Ethnic Cohort Study, which followed >180,000 patients for the development of CRC for up to 11.5 years through linkage with cancer registries. Forward stepwise regression tuned to the c statistic was used to select the most important variables for use in separate Cox survival models for men and women. Model accuracy was assessed using 10-fold cross-validation.
Results
Patients in the cohort experienced 2762 incident cases of CRC. The final model for men contained age, ethnicity, pack-years of smoking, alcoholic drinks per day, body mass index, years of education, regular use of aspirin, family history of colon cancer, regular use of multivitamins, ounces of red meat intake per day, history of diabetes, and hours of moderate physical activity per day. The final model for women included age, ethnicity, years of education, use of estrogen, history of diabetes, pack-years of smoking, family history of colon cancer, regular use of multivitamins, body mass index, regular use of nonsteroidal anti-inflammatory drugs, and alcoholic drinks per day. The calculator demonstrated good accuracy with a cross-validated c statistic of 0.681 in men and 0.679 in women, and it seems to be well calibrated graphically. An electronic version of the calculator is available at http://rcalc.ccf.org.
Conclusion
This calculator seems to be accurate, is user friendly, and has been internally validated in a diverse population.
doi:10.3122/jabfm.2014.01.130040
PMCID: PMC4219857  PMID: 24390885
Colorectal Cancer; Medical Decision Making; Prevention and Control; Risk
13.  Robot-assisted versus other types of radical prostatectomy: Population-based safety and cost comparison in Japan, 2012–2013 
Cancer Science  2014;105(11):1421-1426.
In 2012, Japanese national insurance started covering robot-assisted surgery. We carried out a population-based comparison between robot-assisted and three other types of radical prostatectomy to evaluate the safety of robot-assisted prostatectomy during its initial year. We abstracted data for 7202 open, 2483 laparoscopic, 1181 minimal incision endoscopic, and 2126 robot-assisted radical prostatectomies for oncological stage T3 or less from the Diagnosis Procedure Combination database (April 2012–March 2013). Complication rate, transfusion rate, anesthesia time, postoperative length of stay, and cost were evaluated by pairwise one-to-one propensity-score matching and multivariable analyses with covariants of age, comorbidity, oncological stage, hospital volume, and hospital academic status. The proportion of robot-assisted radical prostatectomies dramatically increased from 8.6% to 24.1% during the first year. Compared with open, laparoscopic, and minimal incision endoscopic surgery, robot-assisted surgery was generally associated with a significantly lower complication rate (odds ratios, 0.25, 0.20, 0.33, respectively), autologous transfusion rate (0.04, 0.31, 0.10), homologous transfusion rate (0.16, 0.48, 0.14), lower cost excluding operation (differences, −5.1%, −1.8% [not significant], −10.8%) and shorter postoperative length of stay (–9.1%, +0.9% [not significant], –18.5%, respectively). However, robot-assisted surgery also resulted in a + 42.6% increase in anesthesia time and +52.4% increase in total cost compared with open surgery (all P < 0.05). Introduction of robotic surgery led to a dynamic change in prostate cancer surgery. Even in its initial year, robot-assisted radical prostatectomy was carried out with several favorable safety aspects compared to the conventional surgeries despite its having the longest anesthesia time and the highest cost.
doi:10.1111/cas.12523
PMCID: PMC4462377  PMID: 25183452
Laparoscopy; minimally invasive; prostatic neoplasm; robot technology; surgical procedures
14.  Mortality After Prostate Cancer Treatment with Radical Prostatectomy, External-Beam Radiation Therapy, or Brachytherapy in Men Without Comorbidity 
European urology  2013;64(3):372-378.
Background
Medical comorbidity is a confounding factor in prostate cancer (PCa) treatment selection and mortality. Large-scale comparative evaluation of PCa mortality (PCM) and overall mortality (OM) restricted to men without comorbidity at the time of treatment has not been performed.
Objective
To evaluate PCM and OM in men with no recorded comorbidity treated with radical prostatectomy (RP), external-beam radiation therapy (EBRT), or brachytherapy (BT).
Design, setting, and participants
Data from 10 361 men with localized PCa treated from 1995 to 2007 at two academic centers in the United States were prospectively obtained at diagnosis and retrospectively reviewed. We identified 6692 men with no recorded comorbidity on a validated comorbidity index. Median follow-up after treatment was 7.2 yr.
Intervention
Treatment with RP in 4459 men, EBRT in 1261 men, or BT in 972 men.
Outcome measurements and statistical analysis
Univariate and multivariate Cox proportional hazards regression analysis, including propensity score adjustment, compared PCM and OM for EBRT and BT relative to RP as reference treatment category. PCM was also evaluated by competing risks analysis.
Results and limitations
Using Cox analysis, EBRT was associated with an increase in PCM compared with RP (hazard ratio [HR]: 1.66; 95% confidence interval [CI], 1.05–2.63), while there was no statistically significant increase with BT (HR: 1.83; 95% CI, 0.88–3.82). Using competing risks analysis, the benefit of RP remained but was no longer statistically significant for EBRT (HR: 1.55; 95% CI, 0.92–2.60) or BT (HR: 1.66; 95% CI, 0.79–3.46). In comparison with RP, both EBRT (HR: 1.71; 95% CI, 1.40–2.08) and BT (HR: 1.78; 95% CI, 1.37–2.31) were associated with increased OM.
Conclusions
In a large multicenter series of men without recorded comorbidity, both forms of radiation therapy were associated with an increase in OM compared with surgery, but there were no differences in PCM when evaluated by competing risks analysis. These findings may result from an imbalance of confounders or differences in mortality related to primary or salvage therapy.
doi:10.1016/j.eururo.2013.03.005
PMCID: PMC3930076  PMID: 23506834
Prostatic neoplasms; Prostatectomy; Radiation therapy; Comorbidity; Comparative effectiveness research
15.  Adjuvant Leuprolide With or Without Docetaxel in Patients With High-Risk Prostate Cancer After Radical Prostatectomy (TAX-3501) 
Cancer  2013;119(20):3610-3618.
BACKGROUND
The current trial evaluated 2 common therapies for patients with advanced prostate cancer, docetaxel and hormonal therapy (HT), in the surgical adjuvant setting.
METHODS
TAX-3501 was a randomized, phase 3, adjuvant study post-radical prostatectomy (RP) in high-risk patients with prostate cancer (n = 228) comparing 18 months of HT with (CHT) without docetaxel chemotherapy either immediately (I) or deferred (D). High-risk disease was defined as a 5-year freedom-from-disease-progression rate of ≤60% as predicted by a post-RP nomogram. Progression-free survival (PFS), including prostate-specific antigen disease recurrence, was the primary endpoint. The authors also assessed the accuracy of the nomogram and analyzed testosterone recovery in 108 patients treated with HT who had at least 1 posttreatment testosterone value.
RESULTS
Between December 2005 and September 2007, 228 patients were randomized between the treatment cohorts. TAX-3501 was terminated prematurely because of enrollment challenges, leaving it underpowered to detect differences in PFS. After a median follow-up of 3.4 years (interquartile range, 2.3–3.8 years), 39 of 228 patients (17%) demonstrated PSA disease progression, and metastatic disease progression occurred in 1 patient. The median time to baseline testosterone recovery after the completion of treatment was prolonged at 487 days (95% confidence interval, 457–546 days). The nomogram’s predicted versus observed freedom from disease progression was significantly different for the combination D(HT) and D(CHT) group (P < .00001).
CONCLUSIONS
TAX-3501 illustrated several difficulties involved in conducting postoperative adjuvant systemic trials in men with high-risk prostate cancer: the lack of consensus regarding patient selection and treatment, the need for long follow-up time, nonvalidated intermediate endpoints, evolving standard approaches, and the need for long-term research support. Except for selected patients at very high-risk of disease recurrence and death, surgical adjuvant trials in patients with prostate cancer may not be feasible.
doi:10.1002/cncr.28270
PMCID: PMC4124610  PMID: 23943299
prostate cancer; adjuvant therapy; docetaxel; leuprolide; testosterone recovery
16.  Predictive and Prognostic Models in Radical Prostatectomy Candidates: A Critical Analysis of the Literature 
European urology  2010;58(5):687-700.
Context
Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).
Objective
To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.
Evidence acquisition
A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.
Evidence synthesis
We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools’ accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.
Conclusions
Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.
doi:10.1016/j.eururo.2010.07.034
PMCID: PMC4119802  PMID: 20727668
Prostate cancer; Radical prostatectomy; Prediction tools; Nomograms
17.  Predicting 6-Year Mortality Risk in Patients With Type 2 Diabetes  
Diabetes Care  2008;31(12):2301-2306.
OBJECTIVE—The objective of this study was to create a tool that predicts the risk of mortality in patients with type 2 diabetes.
RESEARCH DESIGN AND METHODS—This study was based on a cohort of 33,067 patients with type 2 diabetes identified in the Cleveland Clinic electronic health record (EHR) who were initially prescribed a single oral hypoglycemic agent between 1998 and 2006. Mortality was determined in the EHR and the Social Security Death Index. A Cox proportional hazards regression model was created using medication class and 20 other predictor variables chosen for their association with mortality. A prediction tool was created using the Cox model coefficients. The tool was internally validated using repeated, random subsets of the cohort, which were not used to create the prediction model.
RESULTS—Follow-up in the cohort ranged from 1 day to 8.2 years (median 28.6 months), and 3,661 deaths were observed. The prediction tool had a concordance index (i.e., c statistic) of 0.752.
CONCLUSIONS—We successfully created a tool that accurately predicts mortality risk in patients with type 2 diabetes. The incorporation of medications into mortality predictions in patients with type 2 diabetes should improve treatment decisions.
doi:10.2337/dc08-1047
PMCID: PMC2584185  PMID: 18809629
18.  Conditional Probability of Survival Nomogram for 1-, 2-, and 3-Year Survivors After an R0 Resection for Gastric Cancer 
Annals of surgical oncology  2012;20(5):1623-1630.
Background
Survival estimates after curative surgery for gastric cancer are based on AJCC staging, or on more accurate multivariable nomograms. However, the risk of dying of gastric cancer is not constant over time, with most deaths occurring in the first 2 years after resection. Therefore, the prognosis for a patient who survives this critical period improves. This improvement over time is termed conditional probability of survival (CPS). Objectives of this study were to develop a CPS nomogram predicting 5-year disease-specific survival (DSS) from the day of surgery for patients surviving a specified period of time after a curative gastrectomy and to explore whether variables available with follow-up improve the nomogram in the follow-up setting.
Methods
A CPS nomogram was developed from a combined US-Dutch dataset, containing 1,642 patients who underwent an R0 resection with or without chemotherapy/ radiotherapy for gastric cancer. Weight loss, performance status, hemoglobin, and albumin 1 year after resection were added to the baseline variables of this nomogram.
Results
The CPS nomogram was highly discriminating (concordance index: 0.772). Surviving 1, 2, or 3 years gives a median improvement of 5-year DSS from surgery of 7.2, 19.1, and 31.6 %, compared with the baseline prediction directly after surgery. Introduction of variables available at 1-year follow-up did not improve the nomogram.
Conclusions
A robust gastric cancer nomogram was developed to predict survival for patients alive at time points after surgery. Introduction of additional variables available after 1 year of follow-up did not further improve this nomogram.
doi:10.1245/s10434-012-2723-6
PMCID: PMC4091759  PMID: 23143591
19.  PREDICTING FIFTEEN-YEAR CANCER-SPECIFIC MORTALITY BASED ON THE PATHOLOGICAL FEATURES OF PROSTATE CANCER 
The Journal of urology  2011;185(3):869-875.
Purpose
Long-term prostate cancer-specific mortality (PCSM) after radical prostatectomy is poorly defined in the era of widespread screening. An understanding of the treated natural history of screen-detected cancers and the pathological risk factors for PCSM are needed for treatment decision-making.
Methods
Using Fine and Gray competing risk regression analysis, the clinical and pathological data and follow-up information of 11,521 patients treated by radical prostatectomy at four academic centers from 1987 to 2005 were modeled to predict PCSM. The model was validated on 12,389 patients treated at a separate institution during the same period.
Results
The overall 15-year PCSM was 7%. Primary and secondary pathological Gleason grade 4–5 (P < 0.001 for both), seminal vesicle invasion (P < 0.001), and year of surgery (P = 0.002) were significant predictors of PCSM. A nomogram predicting 15-year PCSM based on standard pathological parameters was accurate and discriminating with an externally-validated concordance index of 0.92. Stratified by patient age, 15-year PCSM for Gleason score ≤ 6, 3+4, 4+3, and 8–10 ranged from 0.2–1.2%, 4.2–6.5%, 6.6–11%, and 26–37%, respectively. The 15-year PCSM risks ranged from 0.8–1.5%, 2.9–10%, 15–27%, and 22–30% for organ-confined cancer, extraprostatic extension, seminal vesicle invasion, and lymph node metastasis, respectively. Only 3 of 9557 patients with organ-confined, Gleason score ≤ 6 cancers have died from prostate cancer.
Conclusions
The presence of poorly differentiated cancer and seminal vesicle invasion are the prime determinants of PCSM after radical prostatectomy. The risk of PCSM can be predicted with unprecedented accuracy once the pathological features of prostate cancer are known.
doi:10.1016/j.juro.2010.10.057
PMCID: PMC4058776  PMID: 21239008
prostatic neoplasms; prostatectomy; models; statistical; treatment outcome
20.  Human kallikrein-2 gene and protein expression predicts prostate cancer at repeat biopsy 
SpringerPlus  2014;3:295.
Purpose
The human kallikrein-2 (hK2) protein and two single nucleotide polymorphism (SNPs) (rs2664155, rs198977) of the gene are associated with prostate cancer risk. We examined whether hK2 protein and gene SNPs predict prostate cancer at the time of repeat biopsy.
Methods
We prospectively offered a repeat biopsy to men with a negative prostate biopsy performed for a PSA >4.0 ng/mL or abnormal Digital Rectal Exam (DRE) between 2001–2005. We genotyped and measured serum hK2 levels in 941 men who underwent a repeat prostate biopsy. Logistic regression analyses were conducted to determine the significance of KLK2 SNPs and hK2 levels for predicting cancer at repeat biopsy.
Results
Of the 941 patients, 180 (19.1%) were found to have cancer. The rs198977 SNP was positively associated with cancer at repeat biopsy (OR variant T allele = 1.8, 95% CI: 1.04-3.13, p = 0.049). When combined, the odds ratio for prostate cancer for patients with high hK2 levels and the variant T-allele of rs198977 was 3.77 (95% CI: 1.94-7.32, p < 0.0001), compared to patients with low hK2 levels and the C-allele. The addition of hK2 levels and KLK2 rs198977 to the baseline predictive model did not significantly increase the area under the curve from a baseline model of 0.67 to 0.69 (p = 0.6).
Conclusions
The KLK2 SNP rs198977 was positively associated with hK2 levels and predicts prostate cancer at the time of repeat prostate biopsy. Further characterization of the KLK2 gene will be needed to determine its clinical utility.
doi:10.1186/2193-1801-3-295
PMCID: PMC4162525  PMID: 25279276
Human Kallikrein-2; Nomogram; Prostate cancer; Single nucleotide polymorphisms
21.  Unmet Needs in the Prediction and Detection of Metastases in Prostate Cancer 
The Oncologist  2013;18(5):549-557.
Despite advances in therapy options, few guidelines or reviews address the optimal timing or methodology for the radiographic detection of metastatic disease in patients with advanced prostate cancer. This review discusses the current status of predicting the presence of metastatic disease, with a particular emphasis on the detection of the M0 to M1 transition, and reviews current data on newer imaging technologies that are changing the way metastases are detected.
The therapeutic landscape for the treatment of advanced prostate cancer is rapidly evolving, especially for those patients with metastatic castration-resistant prostate cancer (CPRC). Despite advances in therapy options, the diagnostic landscape has remained relatively static, with few guidelines or reviews addressing the optimal timing or methodology for the radiographic detection of metastatic disease. Given recent reports indicating a substantial proportion of patients with CRPC thought to be nonmetastatic (M0) are in fact metastatic (M1), there is now a clear opportunity and need for improvement in detection practices. Herein, we discuss the current status of predicting the presence of metastatic disease, with a particular emphasis on the detection of the M0 to M1 transition. In addition, we review current data on newer imaging technologies that are changing the way metastases are detected. Whether earlier detection of metastatic disease will ultimately improve patient outcomes is unknown, but given that the therapeutic options for those with metastatic and nonmetastatic CPRC vary, there are considerable implications of how and when metastases are detected.
doi:10.1634/theoncologist.2013-0027
PMCID: PMC3662846  PMID: 23650019
Imaging; Lymph nodes; Magnetic resonance imaging; Neoplasm metastasis; Prostatic neoplasms; Radionuclide imaging
22.  Pre-operative nomogram predicting 12-year probability of metastatic renal cancer 
The Journal of urology  2008;179(6):2146-2151.
Purpose
For patients with renal masses localized to the kidney, there is currently no pre-operative tool to predict the likelihood of metastatic recurrence following surgical intervention. The primary goal of this study was to develop a predictive model that could be used in the pre-operative setting.
Methods
We pooled institutional databases from Memorial Sloan-Kettering and Mayo Clinic and identified 2,517 patients with renal masses and no concurrent evidence of metatases, who underwent radical or partial nephrectomy and with complete data. Cox proportional hazard regression analyses were used to model pre-operative clinical and radiographic characteristics as predictors for development of metastases following nephrectomy. Internal validation was performed with a statistical bootstrapping technique.
Results
Metastatic recurrence developed in 340 of the 2517 patients. Median follow-up for patients without metastatic recurrence was 4.7 years. A nomogram was developed using pre-operative characteristics to predict the 12-year likelihood of post-operative metastatic recurrence, with a concordance index (CI) of 0.80. In contrast, the concordance index of pre-operative TNM staging was 0.71. Size of the primary renal mass, evidence of lymphadenopathy or necrosis on pre-operative imaging and the mode of presentation were important predictors for the subsequent development of metastases.
Conclusions
We present a pre-operative nomogram that accurately predicts the development of metastatic recurrence following nephrectomy. This nomogram may be potentially useful to identify high-risk patients for clinical trials in neoadjuvant setting.
doi:10.1016/j.juro.2008.01.101
PMCID: PMC3985125  PMID: 18423735
nomogram; renal masses; nephrectomy; metastasis
23.  Preoperative Nomogram Predicting the 10-Year Probability of Prostate Cancer Recurrence After Radical Prostatectomy 
An existing preoperative nomogram predicts the probability of prostate cancer recurrence, defined by prostate-specific antigen (PSA), at 5 years after radical prostatectomy based on clinical stage, serum PSA, and biopsy Gleason grade. In an updated and enhanced nomogram, we have extended the predictions to 10 years, added the prognostic information of systematic biopsy results, and enabled the predictions to be adjusted for the year of surgery. Cox regression analysis was used to model the clinical information for 1978 patients treated by two high-volume surgeons from our institution. The nomogram was externally validated on an independent cohort of 1545 patients with a concordance index of 0.79 and was well calibrated with respect to observed outcome. The inclusion of the number of positive and negative biopsy cores enhanced the predictive accuracy of the model. Thus, a new preoperative nomogram provides robust predictions of prostate cancer recurrence up to 10 years after radical prostatectomy.
doi:10.1093/jnci/djj190
PMCID: PMC2242430  PMID: 16705126
24.  Postoperative Nomogram Predicting the 10-Year Probability of Prostate Cancer Recurrence After Radical Prostatectomy 
Purpose
A postoperative nomogram for prostate cancer recurrence after radical prostatectomy (RP) has been independently validated as accurate and discriminating. We have updated the nomogram by extending the predictions to 10 years after RP and have enabled the nomogram predictions to be adjusted for the disease-free interval that a patient has maintained after RP.
Methods
Cox regression analysis was used to model the clinical information for 1,881 patients who underwent RP for clinically-localized prostate cancer by two high-volume surgeons. The model was externally validated separately on two independent cohorts of 1,782 patients and 1,357 patients, respectively. Disease progression was defined as a rising prostate-specific antigen (PSA) level, clinical progression, radiotherapy more than 12 months postoperatively, or initiation of systemic therapy.
Results
The 10-year progression-free probability for the modeling set was 79% (95% CI, 75% to 82%). Significant variables in the multivariable model included PSA (P = .002), primary (P < .0001) and secondary Gleason grade (P = .0006), extracapsular extension (P < .0001), positive surgical margins (P = .028), seminal vesicle invasion (P < .0001), lymph node involvement (P = .030), treatment year (P = .008), and adjuvant radiotherapy (P = .046). The concordance index of the nomogram when applied to the independent validation sets was 0.81 and 0.79.
Conclusion
We have developed and validated as a robust predictive model an enhanced postoperative nomogram for prostate cancer recurrence after RP. Unique to predictive models, the nomogram predictions can be adjusted for the disease-free interval that a patient has achieved after RP.
doi:10.1200/JCO.2005.01.867
PMCID: PMC2231088  PMID: 16192588
25.  Body Mass Index Is Weakly Associated with, and not a Helpful Predictor of, Disease Progression in Men with Clinically Localized Prostate Carcinoma Treated with Radical Prostatectomy 
Cancer  2005;103(10):2030-2034.
BACKGROUND
Several studies have recently suggested an association between body mass index (BMI) and disease progression after radical prostatectomy. In the current study, the authors examined this association and that between the reciprocal of BMI (INVBMI, 1/BMI) and progression-free probability in men treated with radical retropubic prostatectomy (RRP) for clinically localized prostate carcinoma.
METHODS
The authors retrospectively studied 2210 patients who underwent RRP at Memorial Sloan-Kettering Cancer Center between September 1986 and May 2003. Clinicopathologic variables analyzed included BMI (kg/m2), preoperative serum prostate-specific antigen level (ng/mL), clinical T classification, year of surgery, race, biopsy-derived primary and secondary Gleason grades, and INVBMI, known to better correlate with percent body fat than BMI. Cox regression analysis was used to examine the possible association between BMI or its reciprocal with disease progression after controlling for the effects of common prognostic factors. The areas under the receiver operating curve (AUC) for models with and without INVBMI were calculated
RESULTS
Of the 2210 patients analyzed, 251 experienced disease progression in a median follow-up time of 25.9 months (range, 0-143 months). After adjusting for all clinical variables, both BMI (P = 0.071; hazards ratio [HR] = 1.027) and INVBMI (P = 0.041; HR < 0.001) were associated with disease progression. However, the areas under AUC for models with and without INVBMI were similar (range, 0.794 - 0.798).
CONCLUSIONS
Although conflicting evidence has been reported regarding the link between obesity and an increased risk of developing prostate carcinoma, as well as an increased risk of developing aggressive disease and prostate carcinoma-related mortality, the authors found weak associations with disease progression for both BMI and INVBMI. These variables were of negligible prognostic value in men who received surgery. Studies with longer follow-up, that examine alternative end points, and that follow treatment(s) besides surgery are needed. Cancer 2005;103: 2030-4.
doi:10.1002/cncr.20991
PMCID: PMC1852497  PMID: 15822118
prostate carcinoma; body mass index; obesity; disease progression; reciprocal of body mass index

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