To evaluate the association of community health indicators with outcomes for kidney transplant recipients.
Retrospective observational cohort study using multivariable Cox proportional hazards models.
Transplant recipients in the United States from the Scientific Registry of Transplant Recipients merged with health indicators compiled from several national databases and the Centers for Disease Control and Prevention, including the National Center for Health Statistics, the Behavioral Risk Factor Surveillance System, and the National Center for Chronic Disease Prevention and Health Promotion.
A total of 100 164 living and deceased donor adult (aged ≥18 years) kidney transplant recipients who underwent a transplant between January 1, 2004, and December 31, 2010.
Main Outcome Measures
Risk-adjusted time to post-transplant mortality and graft loss.
Multiple health indicators from recipients’ residence were independently associated with outcomes, in cluding low birth weight, preventable hospitalizations, inactivity rate, and smoking and obesity prevalence. Recipients in the highest-risk counties were more likely to be African American (adjusted odds ratio, 1.59, 95% CI, 1.51-1.68), to be younger (aged 18-39 years; 1.46; 1.32-1.60), to have lower educational attainment (
In a national cohort of patients undergoing complex medical procedures, health indicators from patients’ communities are strong independent predictors of all-cause mortality. Findings highlight the importance of community conditions for risk stratification of patients and development of individualized treatment protocols. Findings also demonstrate that standard risk adjustment does not capture important factors that may affect unbiased performance evaluations of transplant centers.
Treatment decisions on prostate cancer diagnosed by trans-urethral resection (TURP) of the prostate are difficult. The current TNM staging system for pT1 prostate cancer has not been re-evaluated for 25 years. Our objective was to optimise the predictive power of tumor extent measurements in TURP of the prostate specimens. A total of 914 patients diagnosed by TURP of the prostate between 1990 and 1996, managed conservatively were identified. The clinical end point was death from prostate cancer. Diagnostic serum prostate-specific antigen (PSA) and contemporary Gleason grading was available. Cancer extent was measured by the percentage of chips infiltrated by cancer. Death rates were compared by univariate and multivariate proportional hazards models, including baseline PSA and Gleason score. The percentage of positive chips was highly predictive of prostate cancer death when assessed as a continuous variable or as a grouped variable on the basis of and including the quintiles, quartiles, tertiles and median groups. In the univariate model, the most informative variable was a four group-split (≤ 10%, >10–25%, > 25–75% and > 75%); (HR = 2.08, 95% CI = 1.8–2.4, P < 0.0001). The same was true in a multivariate model (ΔX2 (1 d.f.) = 15.0, P = 0.0001). The current cutoff used by TNM (< = 5%) was sub-optimal (ΔX2 (1 d.f.) = 4.8, P = 0.023). The current TNM staging results in substantial loss of information. Staging by a four-group subdivision would substantially improve prognostication in patients with early stage disease and also may help to refine management decisions in patients who would do well with conservative treatments.
conservative treatment; prostate cancer; stage; trans-urethral resection of prostate; watchful waiting
The optimal method for measuring cancer extent in prostate biopsy specimens is unknown. Seven hundred forty-four patients diagnosed between 1990 and 1996 with prostate cancer and managed conservatively were identified. The clinical end point was death from prostate cancer. The extent of cancer was measured in terms of number of cancer cores (NCC), percentage of cores with cancer (PCC), total length of cancer (LCC) and percentage length of cancer in the cores (PLC). These were correlated with prostate cancer mortality, in univariate and multivariate analysis including Gleason score and prostate-specific antigen (PSA). All extent of cancer variables were significant predictors of prostate cancer death on univariate analysis: NCC, hazard ration (HR)=1.15, 95% confidence interval (CI)=1.04–1.28, P=0.011; PPC, HR=1.01, 95% CI=1.01–1.02, P<0.0001; LCC, HR=1.02, 95% CI=1.01–1.03, P=0.002; PLC, HR=1.01, 95% CI=1.01–1.02, P=0.0001. In multivariate analysis including Gleason score and baseline PSA, PCC and PLC were both independently significant P=0.004 and P=0.012, respectively, and added further information to that provided by PSA and Gleason score, whereas NNC and LCC were no longer significant (P=0.5 and P=0.3 respectively). In a final model, including both extent of cancer variables, PCC was the stronger, adding more value than PLC (χ2 (1df)=7.8, P=0.005, χ2 (1df)=0.5, P=0.48 respectively). Measurements of disease burden in needle biopsy specimens are significant predictors of prostate-cancer-related death. The percentage of positive cores appeared the strongest predictor and was stronger than percentage length of cancer in the cores.
Prostate biopsy; Prostate cancer; Biopsy; prognostic factors; Tumour extent
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.
Mortality; myocardial infarction; predictors; registry; revascularization
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.
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.
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.
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.
Background. Propensity score usage seems to be growing in popularity leading researchers to question the possible role of propensity scores in prediction modeling, despite the lack of a theoretical rationale. It is suspected that such requests are due to the lack of differentiation regarding the goals of predictive modeling versus causal inference modeling. Therefore, the purpose of this study is to formally examine the effect of propensity scores on predictive performance. Our hypothesis is that a multivariable regression model that adjusts for all covariates will perform as well as or better than those models utilizing propensity scores with respect to model discrimination and calibration.
Methods. The most commonly encountered statistical scenarios for medical prediction (logistic and proportional hazards regression) were used to investigate this research question. Random cross-validation was performed 500 times to correct for optimism. The multivariable regression models adjusting for all covariates were compared with models that included adjustment for or weighting with the propensity scores. The methods were compared based on three predictive performance measures: (1) concordance indices; (2) Brier scores; and (3) calibration curves.
Results. Multivariable models adjusting for all covariates had the highest average concordance index, the lowest average Brier score, and the best calibration. Propensity score adjustment and inverse probability weighting models without adjustment for all covariates performed worse than full models and failed to improve predictive performance with full covariate adjustment.
Conclusion. Propensity score techniques did not improve prediction performance measures beyond multivariable adjustment. Propensity scores are not recommended if the analytical goal is pure prediction modeling.
Prediction; Propensity score; Calibration curve; Concordance index; Multivariable regression
Purpose: An estimated 24%–45% of patients with cancer develop brain metastases. Individualized estimation of survival for patients with brain metastasis could be useful for counseling patients on clinical outcomes and prognosis. Methods: De-identified data for 2367 patients with brain metastasis from 7 Radiation Therapy Oncology Group randomized trials were used to develop and internally validate a prognostic nomogram for estimation of survival among patients with brain metastasis. The prognostic accuracy for survival from 3 statistical approaches (Cox proportional hazards regression, recursive partitioning analysis [RPA], and random survival forests) was calculated using the concordance index. A nomogram for 12-month, 6-month, and median survival was generated using the most parsimonious model. Results: The majority of patients had lung cancer, controlled primary disease, no surgery, Karnofsky performance score (KPS) ≥ 70, and multiple brain metastases and were in RPA class II or had a Diagnosis-Specific Graded Prognostic Assessment (DS-GPA) score of 1.25–2.5. The overall median survival was 136 days (95% confidence interval, 126–144 days). We built the nomogram using the model that included primary site and histology, status of primary disease, metastatic spread, age, KPS, and number of brain lesions. The potential use of individualized survival estimation is demonstrated by showing the heterogeneous distribution of the individual 12-month survival in each RPA class or DS-GPA score group. Conclusion: Our nomogram provides individualized estimates of survival, compared with current RPA and DS-GPA group estimates. This tool could be useful for counseling patients with respect to clinical outcomes and prognosis.
brain metastases; nomogram; prediction; prognosis; survival
Introduction. The objective of this study was to create a tool that accurately predicts the risk of morbidity and mortality in patients with type 2 diabetes according to an oral hypoglycemic agent.
Materials and Methods. The model was based on a cohort of 33,067 patients with type 2 diabetes who were prescribed a single oral hypoglycemic agent at the Cleveland Clinic between 1998 and 2006. Competing risk regression models were created for coronary heart disease (CHD), heart failure, and stroke, while a Cox regression model was created for mortality. Propensity scores were used to account for possible treatment bias. A prediction tool was created and internally validated using tenfold cross-validation. The results were compared to a Framingham model and a model based on the United Kingdom Prospective Diabetes Study (UKPDS) for CHD and stroke, respectively.
Results and Discussion. Median follow-up for the mortality outcome was 769 days. The numbers of patients experiencing events were as follows: CHD (3062), heart failure (1408), stroke (1451), and mortality (3661). The prediction tools demonstrated the following concordance indices (c-statistics) for the specific outcomes: CHD (0.730), heart failure (0.753), stroke (0.688), and mortality (0.719). The prediction tool was superior to the Framingham model at predicting CHD and was at least as accurate as the UKPDS model at predicting stroke.
Conclusions. We created an accurate tool for predicting the risk of stroke, coronary heart disease, heart failure, and death in patients with type 2 diabetes. The calculator is available online at http://rcalc.ccf.org under the heading “Type 2 Diabetes” and entitled, “Predicting 5-Year Morbidity and Mortality.” This may be a valuable tool to aid the clinician’s choice of an oral hypoglycemic, to better inform patients, and to motivate dialogue between physician and patient.
Type 2 diabetes mellitus; Prediction; Propensity; Coronary heart disease; Heart failure; Stroke; Mortality; Electronic health record; Hypoglycemic agents
• To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results.
• We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data.
• Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low-risk disease who are choosing between active surveillance and definitive therapy.
Patients and methods
• In total, 181 low-risk PCa patients (clinical stage T1c–T2a, prostate-specific antigen level < 10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery.
• For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa).
• Insignificant PCa was defined on surgical pathology.
• There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+.
• Prediction accuracy was assessed using areas under receiver–operator characteristic curves.
• At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients.
• For both readers, all magnetic resonance models performed significantly better than the base clinical model (P ≤ 0.05 for all) and similarly to the more comprehensive clinical model.
• Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated.
• All MR-inclusive models performed significantly better than the base clinical model.
magnetic resonance imaging; magnetic resonance spectroscopic imaging; nomograms; prostate neoplasms
To evaluate the discrimination, calibration and net benefit performance of the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) across five European Randomized study of Screening for Prostate Cancer (ERSPC), 1 United Kingdom, 1 Austrian and 3 US biopsy cohorts.
PCPTRC risks were calculated for 25,733 biopsies using prostate-specific antigen (PSA), digital rectal examination, family history and history of prior biopsy, and single imputation for missing covariates. Predictions were evaluated using the areas underneath the receiver operating characteristic curves (AUC), discrimination slopes, chi-square tests of goodness of fit, and net benefit decision curves.
AUCs of the PCPTRC ranged from a low of 56% in the ERSPC Goeteborg Rounds 2-6 cohort to a high of 72% in the ERSPC Goeteborg Round 1 cohort, and were statistically significantly higher than that of PSA in 6 out of the 10 cohorts. The PCPTRC was well-calibrated in the SABOR, Tyrol and Durham cohorts. There was limited to no net benefit to using the PCPTRC for biopsy referral compared to biopsying all or no men in all five ERSPC cohorts and benefit within a limited range of risk thresholds in all other cohorts.
External validation of the PCPTRC across ten cohorts revealed varying degree of success highly dependent on the cohort, most likely due to different criteria for and work-up before biopsy. Future validation studies of new calculators for prostate cancer should acknowledge the potential impact of the specific cohort studied when reporting successful versus failed validation.
receiver operating characteristic curve; risk; prostate cancer; calibration; net benefit
New markers may improve prediction of diagnostic and prognostic outcomes. We review various measures to quantify the incremental value of markers over standard, readily available characteristics. Widely used traditional measures include the improvement in model fit or in the area under the receiver operating characteristic (ROC) curve (AUC). New measures include the net reclassification index (NRI) and decision–analytic measures, such as the fraction of true positive classifications penalized for false positive classifications (‘net benefit’, NB).
For illustration we discuss a case study on the presence of residual tumor versus benign tissue in 544 patients with testicular cancer. We assessed 3 tumor markers (AFP, HCG, and LDH) for their incremental value over currently standard clinical predictors. AUC and R2 values suggested adding continuous LDH and AFP whereas NB only favored HCG as a potentially promising marker at a clinically defendable decision threshold of 20% risk. Results based on the NRI fell in the middle, suggesting reclassification potential of all three markers.
We conclude that improvement in standard discrimination measures, which focus on finding variables that might be promising across all decision thresholds, may not detect the most informative markers at a specific threshold of particular clinical relevance. When a marker is intended to support decision making, calculation of the improvement in a decision–analytic measure, such as NB, is preferable over an overall judgment as obtained from the AUC in ROC analysis.
prediction; logistic regression model; performance measures; incremental value
The performance of prediction models can be assessed using a variety of different methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration.
Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision–analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.
We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n=544 for model development, n=273 for external validation).
We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for making clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
We have previously demonstrated that there is a learning curve for open radical prostatectomy. In this study we sought to determine whether the effects of the learning curve are modified by patient risk as defined by preoperative tumor characteristics.
The study included 7,683 eligible prostate cancer patients treated with open radical prostatectomy by one of 72 surgeons. Surgeon experience was coded as the total prior number of radical prostatectomies conducted by the surgeon prior to a patient’s surgery. Multivariable survival-time regression models were used to evaluate the association between surgeon experience and biochemical recurrence, separately for each preoperative risk group.
We saw no evidence that patient risk affects the learning curve: there was a statistically significant association between biochemical recurrence and surgeon experience in all analyses. The absolute risk difference for a patient receiving treatment from a surgeon with 10 compared to 250 prior radical prostatectomies was 6.6% (95% C.I. 3.4%, 10.3%), 12.0% (6.9%, 18.2%) and 9.7% (1.2%, 18.2%) for patients at low, medium and high preoperative risk patients. Recurrence-free probability for patients with low risk disease approached 100% for the most experienced surgeons
Cancer control after radical prostatectomy improves with increasing surgeon experience irrespective of patient risk. Excellent rates of cancer control for patients with low risk disease treated by the most experienced surgeons suggests that the primary reason such patients recur is inadequate surgical technique. The results have significant implications for clinical care.
Radical prostatectomy; prostate cancer; surgery
Purpose: To identify and examine polymorphisms of genes associated with aggressive and clinical significant forms of prostate cancer among a screening cohort.
Experimental Design: We conducted a genome-wide association study among patients with aggressive forms of prostate cancer and biopsy-proven normal controls ascertained from a prostate cancer screening program. We then examined significant associations of specific polymorphisms among a prostate cancer screened cohort to examine their predictive ability in detecting prostate cancer.
Results: We found significant associations between aggressive prostate cancer and five single nucleotide polymorphisms (SNPs) in the 10q26 (rs10788165, rs10749408, and rs10788165, p value for association 1.3 × 10−10 to 3.2 × 10−11) and 15q21 (rs4775302 and rs1994198, p values for association 3.1 × 10−8 to 8.2 × 10−9) regions. Results of a replication study done in 3439 patients undergoing a prostate biopsy, revealed certain combinations of these SNPs to be significantly associated not only with prostate cancer but with aggressive forms of prostate cancer using an established classification criterion for prostate cancer progression (odds ratios for intermediate to high-risk disease 1.8–3.0, p value 0.003–0.001). These SNP combinations were also important clinical predictors for prostate cancer detection based on nomogram analysis that assesses prostate cancer risk.
Conclusions: Five SNPs were found to be associated with aggressive forms of prostate cancer. We demonstrated potential clinical applications of these associations.
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.
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.
Physicians often order periodic bone scans (BS) to check for metastases in patients with an increasing prostate-specific antigen (PSA; biochemical recurrence [BCR]) after radical prostatectomy (RP), but most scans are negative. We studied patient characteristics to build a predictive model for a positive scan.
Patients and Methods
From our prostate cancer database we identified all patients with detectable PSA after RP. We analyzed the following features at the time of each bone scan for association with a positive BS: preoperative PSA, time to BCR, pathologic findings of the RP, PSA before the BS (trigger PSA), PSA kinetics (PSA doubling time, PSA slope, and PSA velocity), and time from BCR to BS. The results were incorporated into a predictive model.
There were 414 BS performed in 239 patients with BCR and no history of androgen deprivation therapy. Only 60 (14.5%) were positive for metastases. In univariate analysis, preoperative PSA (P = .04), seminal vesicle invasion (P = .02), PSA velocity (P < .001), and trigger PSA (P < .001) predicted a positive BS. In multivariate analysis, only PSA slope (odds ratio [OR], 2.71; P = .03), PSA velocity (OR, 0.93; P = .003), and trigger PSA (OR, 1.022; P < .001) predicted a positive BS. A nomogram for predicting the bone scan result was constructed with an overfit-corrected concordance index of 0.93.
Trigger PSA, PSA velocity, and slope were associated with a positive BS. A highly discriminating nomogram can be used to select patients according to their risk for a positive scan. Omitting scans in low-risk patients could reduce substantially the number of scans ordered.
Accurate preoperative and postoperative risk assessment has been critical for counseling patients regarding radical prostatectomy for clinically localized prostate cancer. In addition to other treatment modalities, neoadjuvant or adjuvant therapies have been considered. The growing literature suggested that the experience of the surgeon may affect the risk of prostate cancer recurrence. The purpose of this study was to develop and internally validate nomograms to predict the probability of recurrence, both preoperatively and postoperatively, with adjustment for standard parameters plus surgeon experience.
The study cohort included 7724 eligible prostate cancer patients treated with radical prostatectomy by 1 of 72 surgeons. For each patient, surgeon experience was coded as the total number of cases conducted by the surgeon before the patient’s operation. Multivariable Cox proportional hazards regression models were developed to predict recurrence. Discrimination and calibration of the models was assessed following bootstrapping methods, and the models were presented as nomograms.
In this combined series, the 10-year probability of recurrence was 23.9%. The nomograms were quite discriminating (preoperative concordance index, 0.767; postoperative concordance index, 0.812). Calibration appeared to be very good for each. Surgeon experience seemed to have a quite modest effect, especially postoperatively.
Nomograms have been developed that consider the surgeon’s experience as a predictor. The tools appeared to predict reasonably well but were somewhat little improved with the addition of surgeon experience as a predictor variable.
prostate cancer; surgeon experience; recurrence; predictive value; nomogram
The prognosis of men with clinically localized prostate cancer is highly variable, and it is difficult to counsel a man who may be considering avoiding, or delaying, aggressive therapy. After collecting data on a large cohort of men who received no initial active prostate cancer therapy, we sought to develop, and to internally validate, a nomogram for prediction of disease-specific survival.
Working with 6 cancer registries within England and numerous hospitals in the region, we constructed a population-based cohort of men diagnosed with prostate cancer between 1990 and 1996. All men had baseline serum prostate specific antigen (PSA) measurements, centralized pathologic grading, and centralized review of clinical stage assignment. Based upon the clinical and pathological data from 1,911 men, we developed and validated a statistical model that served as the basis for the nomogram. The discrimination and calibration of the nomogram were assessed with use of one third of the men, who were omitted from modeling and used as a test sample.
The median age of the included men was 70.4 years. The 25th and 75th percentiles of PSA were 7.3 and 32.6 ng/ml respectively, and the median was 15.4 ng/ml. Forty-two percent of the men had high grade disease. The nomogram predicted well with a concordance index of 0.73 and had good calibration.
We have developed an accurate tool for predicting the probability that a man with clinically localized prostate cancer will survive his disease for 120 months if the cancer is not treated with curative intent immediately. The tool should be helpful for patient counseling and clinical trial design.
Greater understanding of the biology and epidemiology of prostate cancer in the last several decades have led to significant advances in its management. Prostate cancer is now detected in greater numbers at lower stages of disease and is amenable to multiple forms of efficacious treatment. However, there is a lack of conclusive data demonstrating a definitive mortality benefit from this earlier diagnosis and treatment of prostate cancer. It is likely due to the treatment of a large proportion of indolent cancers that would have had little adverse impact on health or lifespan if left alone. Due to this overtreatment phenomenon, active surveillance with delayed intervention is gaining traction as a viable management approach in contemporary practice. The ability to distinguish clinically insignificant cancers from those with a high risk of progression and/or lethality is critical to the appropriate selection of patients for surveillance protocols versus immediate intervention. This chapter will review the ability of various prediction models, including risk groupings and nomograms, to predict indolent disease and determine their role in the contemporary management of clinically localized prostate cancer.
prediction model; prostate cancer; prostatic neoplasms; screening
Statistical models predicting cancer recurrence after surgery are based on biologic variables. We have previously shown that prostate cancer recurrence is related both to tumor biology and to surgical technique. Here we evaluate the association between several biological predictors and biochemical recurrence across varying surgical experience. The study included two separate cohorts: 6091 patients treated by open radical prostatectomy and an independent replication set of 2298 patients treated laparoscopically. We calculated the odds ratios for biological predictors of biochemical recurrence– stage, Gleason grade and prostate-specific antigen (PSA) – and also the predictive accuracy (AUC) of a multivariable model, for subgroups of patients defined by the experience of their surgeon. In the open cohort, the odds ratio for Gleason score 8+ and advanced pathologic stage, though not PSA or Gleason score 7, increased dramatically when patients treated by surgeons with lower levels of experience were excluded (Gleason 8+: odds ratios 5.6 overall vs. 13.0 for patients treated by surgeons with 1000+ prior cases; locally advanced disease: odds ratios of 6.6 vs. 12.2 respectively). The AUC of the multivariable model was 0.750 for patients treated by surgeons with 50 or fewer cases compared to 0.849 for patients treated by surgeons with 500 or more. Although predictiveness was overall lower for the independent replication set cohort, the main findings were replicated. Surgery confounds biology. Although our findings have no direct clinical implications, studies investigating biological variables as predictors of outcome after curative resection of cancer should consider the impact of surgeon specific factors.
prostate cancer; prediction; molecular markers; outcome studies; surgeon
Although the American Board of Internal Medicine (ABIM) certification is valued as a reflection of physicians’ experience, education, and expertise, limited methods exist to predict performance in the examination.
The objective of this study was to develop and validate a predictive tool based on variables common to all residency programs, regarding the probability of an internal medicine graduate passing the ABIM certification examination.
The development cohort was obtained from the files of the Cleveland Clinic internal medicine residents who began training between 2004 and 2008. A multivariable logistic regression model was built to predict the ABIM passing rate. The model was represented as a nomogram, which was internally validated with bootstrap resamples. The external validation was done retrospectively on a cohort of residents who graduated from two other independent internal medicine residency programs between 2007 and 2011.
Of the 194 Cleveland Clinic graduates used for the nomogram development, 175 (90.2%) successfully passed the ABIM certification examination. The final nomogram included four predictors: In-Training Examination (ITE) scores in postgraduate year (PGY) 1, 2, and 3, and the number of months of overnight calls in the last 6 months of residency. The nomogram achieved a concordance index (CI) of 0.98 after correcting for over-fitting bias and allowed for the determination of an estimated probability of passing the ABIM exam. Of the 126 graduates from two other residency programs used for external validation, 116 (92.1%) passed the ABIM examination. The nomogram CI in the external validation cohort was 0.94, suggesting outstanding discrimination.
A simple user-friendly predictive tool, based on readily available data, was developed to predict the probability of passing the ABIM exam for internal medicine residents. This may guide program directors’ decision-making related to program curriculum and advice given to individual residents regarding board preparation.
board examination; in-training examination; internal medicine; residents; program directors