Objectives

A preoperative nomogram is an effective tool for assessing the risk of disease progression following radical prostatectomy for localized prostate cancer. To better understand the performance of nomograms for patients with a low PSA, we examined whether patients with PSA < 2.5 had different outcomes versus that predicted by a validated preoperative nomogram.

Methods

A cohort of 6130 patients from two referral centers was analyzed. Kaplan-Meier methods were used to estimate the recurrence-free probabilities based on PSA grouping (< 2.5 vs ≥ 2.5 ng/mL). Cox proportional hazards regression was used to evaluate whether PSA grouping was associated with biochemical recurrence controlling for preoperative nomogram probability.

Results

A total of 399/6130 (6.5%) patients had PSA < 2.5. Patients with PSA ≤ 0.5 had a high rate of non-organ confined disease (33% vs. 15% for PSA 0.6 – 2.5). The median follow-up for recurrence-free patients was 2.4 years, and 10 patients with PSA < 2.5 and 597 patients with PSA > 2.5 recurred (total 607/6130). With adjustment for the preoperative nomogram probability, there was no significant difference in recurrence by PSA grouping (hazard ratio 0.78 for PSA <2.5 vs ≥2.5; 95% C.I. 0.42, 1.48; p=0.5).

Conclusions

Patients with a low PSA comprise a small proportion of those treated, and the majority have palpable disease. Patients with especially low PSA values (≤ 0.5) have a high rate of non-organ confined disease. We saw no evidence that patients with low PSA have worse outcomes, after stage and grade were taken into account.

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.

Background

Although case-control studies have identified numerous single nucleotide polymorphisms (SNPs) associated with prostate cancer, the clinical role of these SNPs remains unclear.

Objective

Evaluate previously identified SNPs for association with prostate cancer and accuracy in predicting prostate cancer in a large prospective population-based cohort of unscreened men.

Design, setting, and participants

This study used a nested case-control design based on the Malmö Diet and Cancer cohort with 943 men diagnosed with prostate cancer and 2829 matched controls. Blood samples were collected between 1991 and 1996, and follow-up lasted through 2005.

Measurements

We genotyped 50 SNPs, analyzed prostate-specific antigen (PSA) in blood from baseline, and tested for association with prostate cancer using the Cochran-Mantel-Haenszel test. We further developed a predictive model using SNPs nominally significant in univariate analysis and determined its accuracy to predict prostate cancer.

Results and limitations

Eighteen SNPs at 10 independent loci were associated with prostate cancer. Four independent SNPs at four independent loci remained significant after multiple test correction (p < 0.001). Seven SNPs at five independent loci were associated with advanced prostate cancer defined as clinical stage ≥T3 or evidence of metastasis at diagnosis. Four independent SNPs were associated with advanced or aggressive cancer defined as stage ≥T3, metastasis, Gleason score ≥8, or World Health Organization grade 3 at diagnosis. Prostate cancer risk prediction with SNPs alone was less accurate than with PSA at baseline (area under the curve of 0.57 vs 0.79), with no benefit from combining SNPs with PSA. This study is limited by our reliance on clinical diagnosis of prostate cancer; there are likely undiagnosed cases among our control group.

Conclusions

Only a few previously reported SNPs were associated with prostate cancer risk in the large prospective Diet and Cancer cohort in Malmö, Sweden. SNPs were less useful in predicting prostate cancer risk than PSA at baseline.

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.

Objective

To assess variation of total prostate-specific antigen (tPSA), free PSA (fPSA), percent fPSA, human glandular kallikrein 2 (hK2), and intact PSA measured three times within two weeks. Knowledge of the variation in an individual’s PSA level is important for clinical decision-making.

Patients and Methods

Study participants were 149 patients referred for prostate biopsy, of which 97 had benign disease and 52 had prostate cancer. Three blood samples were drawn with a median of four hours between first and second samples and 12 days between first and third samples. Variability was described by absolute differences, ratios and intra-individual coefficients of variation. Total PSA, fPSA, hK2, and intact PSA were measured in anti-coagulated blood plasma.

Results

At baseline, the median tPSA was 6.8 (IQR 4.5, 9.6) ng/mL. The intra-individual variation was low for all biomarkers, and lowest for tPSA. For 80% of participants, the ratio between first and second time points for tPSA was between 0.91 and 1.09 and the ratio for percent fPSA was between 0.89 and 1.15. Total coefficients of variation between time 1 and 2 for tPSA, fPSA, percent fPSA, hK2 and intact PSA were 4.0%, 6.6%, 6.0%, 9.2%, and 9.5%, respectively. The measurements taken several days apart varied more than those taken on the same day, but the variation between both time points were not large.

Conclusion

The intra-individual variation for all the kallikrein-like markers studied was relatively small, especially for samples drawn the same day. Few cases are reclassified between the time points. This indicates high short-term biological and technical reproducibility of the tests in clinical use.

INTRODUCTION

The introduction of total prostate specific antigen (total PSA) testing in blood has revolutionized the detection and management of men with prostate cancer (PCa). The objective of this review was to discuss the challenges of PCa biomarker research, definition of the type of PCa biomarkers, the statistical considerations for biomarker discovery and validation, and to review the literature regarding total PSA velocity and novel blood-based biomarkers.

METHODS

An English-language literature review of the Medline database (1990 to August 2010) of published data on blood-based biomarkers and PCa was undertaken.

RESULTS

The inherent biological variability of total PSA levels affects the interpretation of any single result. Men who will eventually develop PCa have increased total PSA levels years or decades before the cancer is diagnosed. Total PSA velocity improves predictiveness of total PSA only marginally, limiting its value for PCa screening and prognostication. The combination of PSA molecular forms and other biomarkers improve PCa detection substantially. Several novel blood-based biomarkers such as human glandular kallikrein 2 (hK2), urokinase plasminogen activator (uPA) and its receptor (uPAR), transforming growth factor-beta 1 (TGF-β1); interleukin-6 (IL-6) and its receptor (IL-6R) may help PCa diagnosis, staging, prognostication, and monitoring. Panels of biomarkers that capture the biologic potential of PCa are in the process of being validated for PCa prognostication.

CONCLUSIONS

PSA is a strong prognostic marker for long-term risk of clinically relevant cancer. However, there is a need for novel biomarkers that aid clinical decision making about biopsy and initial treatment. There is no doubt that progress will continue based on the integrated collaboration of researchers, clinicians and biomedical firms.

Background

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.

Methods

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.

Results

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

Conclusions

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.

Purpose

Women with localized breast cancer face difficult decisions about adjuvant therapy. Several decision aids are available to help women choose between treatment options. Decision aids are known to affect treatment choices and may therefore affect patient survival. The authors aimed to model the effects of the Adjuvant! decision aid on expected survival in women with early stage breast cancer.

Patients and Methods

Data were obtained from a randomized trial of Adjuvant! (n =395). To calculate the effects of the decision aid on survival, the authors used the Adjuvant! survival predictions as a surrogate endpoint. Data from each arm were entered separately into statistical models to estimate change in survival associated with receiving the Adjuvant! decision aid.

Results

Most women (~85%) chose a treatment option that maximized predicted survival. The effects of the decision aid on outcome could not be modeled because a small number of women (n =12, 3%) chose treatment options associated with a large (5%–14%) loss in survival. These women—most typically estrogen receptor positive but refusing hormonal therapy—were equally divided between Adjuvant! and control groups and were not distinguished by medical or demographic factors.

Conclusions

Expected benefit from treatment is a key variable in understanding patient behavior. A small number of women refuse adjuvant treatment associated with large increases in predicted survival, even when they are explicitly informed about the degree of benefit they would forgo. Investigation of the effects of decision aids on cancer survival is unlikely to be fruitful due to power considerations.

Evidence of reduced prostate cancer mortality from randomized trials in Europe supports early detection of prostate cancer with prostate-specific antigen (PSA). Yet PSA screening has generated considerable controversy: it is far from clear that the benefits outweigh risks, in terms of overdiagnosis and overtreatment. One way to shift the ratio of benefits to harms is to focus on men at highest risk, who have more to benefit than average. Neither family history nor any of the currently identified genomic markers offer sufficient risk stratification for practical use. However, there is considerable evidence that the levels of PSA in blood are strongly prognostic of the long-term risk of aggressive prostate cancer. Specifically, it is difficult to justify continuing to screen men age 60 or older if they have a PSA less than 1 or 2 ng/ml; for men 45 – 60, intervals between PSA tests can be based on PSA levels, with 2 to 4 year re-testing interval for men with PSA of 1 ng/ml or higher, and tests every 6 to 8 years for men with PSA < 1 ng/ml. Men with the top 10% of PSAs at a young age (PSA ~1.5 ng / ml or higher below 50) are at particularly high risk and should be subject to intensive monitoring.

Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem: many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings; can incorporate novel predictors such as genomic data; can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision making. Key issues will be integration of models into the electronic health record, and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.

Background

The National Comprehensive Cancer Network and American Urological Association guidelines on early detection of prostate cancer recommend biopsy on the basis of high prostate-specific antigen (PSA) velocity, even in the absence of other indications such as an elevated PSA or a positive digital rectal exam (DRE).

Methods

To evaluate the current guideline, we compared the area under the curve of a multivariable model for prostate cancer including age, PSA, DRE, family history, and prior biopsy, with and without PSA velocity, in 5519 men undergoing biopsy, regardless of clinical indication, in the control arm of the Prostate Cancer Prevention Trial. We also evaluated the clinical implications of using PSA velocity cut points to determine biopsy in men with low PSA and negative DRE in terms of additional cancers found and unnecessary biopsies conducted. All statistical tests were two-sided.

Results

Incorporation of PSA velocity led to a very small increase in area under the curve from 0.702 to 0.709. Improvements in predictive accuracy were smaller for the endpoints of high-grade cancer (Gleason score of 7 or greater) and clinically significant cancer (Epstein criteria). Biopsying men with high PSA velocity but no other indication would lead to a large number of additional biopsies, with close to one in seven men being biopsied. PSA cut points with a comparable specificity to PSA velocity cut points had a higher sensitivity (23% vs 19%), particularly for high-grade (41% vs 25%) and clinically significant (32% vs 22%) disease. These findings were robust to the method of calculating PSA velocity.

Conclusions

We found no evidence to support the recommendation that men with high PSA velocity should be biopsied in the absence of other indications; this measure should not be included in practice guidelines.

A sophisticated reading of the randomized trial evidence suggests that, although screening for prostate cancer with prostate-specific antigen (PSA) can reduce cancer-specific mortality, it does so at considerable cost in terms of the number of men who need to be screened, biopsied, and treated to prevent one death. The challenge is to design screening programs that maximize benefits (reducing prostate cancer mortality) and minimize costs (overtreatment). Recent research has suggested that this can be achieved by risk-stratifying screening and biopsy; increasing reliance on active surveillance for low-risk cancer; restricting radical prostatectomy to high-volume surgeons; and using appropriately high-dose radiotherapy. In current U.S. practice, however, many men who are screened are unlikely to benefit, most men found to have low-risk cancers are referred for unnecessary curative treatment, and much treatment is given at low-volume centers.

Purpose

Prostate specific antigen (PSA) velocity has been proposed as a marker to aid detection of prostate cancer. We sought to determine whether PSA velocity could predict the results of repeat biopsy in men with persistently elevated PSA after initial negative biopsy.

Materials and Methods

We identified 1,837 men who participated in the Göteborg or Rotterdam section of the European Randomized Screening study of Prostate Cancer (ERSPC), and who had one or more subsequent prostate biopsies after an initial negative finding. We evaluated whether PSA velocity improved predictive accuracy beyond that of PSA alone.

Results

There were a total of 2579 repeat biopsies, of which 363 (14%) were positive for prostate cancer, and 44 (1.7%) were high grade (Gleason score ≥7). Although PSA velocity was statistically associated with cancer risk (p<0.001), it had very low predictive accuracy (area-under-the-curve [AUC] of 0.55). There was some evidence that PSA velocity improved AUC compared to PSA for high grade cancer. However, the small increase in risk associated with high PSA velocity – from 1.7 % to 2.8% as velocity increased from 0 to 1 ng / ml / year - is of questionable clinical relevance.

Conclusions

Men with a prior negative biopsy have a lower risk for prostate cancer at subsequent biopsies, with high grade disease particularly rare. We found little evidence to support the use of PSA velocity to aid decisions about repeat biopsy for prostate cancer.

Background

We previously reported that a single prostate-specific antigen (PSA) measured at age 44–50 was highly predictive of subsequent prostate cancer diagnosis in an unscreened population. Here we report an additional seven years of follow-up. This provides a replication on an independent data set, and allows estimates of the association between early PSA and subsequent advanced cancer (clinical stage ≥T3 or metastases at diagnosis).

Methods

Blood was collected from 21,277 men in a Swedish city (74% participation rate) during 1974–1986 at age 33–50. Through 2006, prostate cancer was diagnosed in 1408 participants; we measured PSA in archived plasma for 1312 (93%) of these cases and for 3728 controls.

Results

At a median follow-up of 23 years, baseline PSA was strongly associated with subsequent prostate cancer (area-under-the-curve 0.72; 95% CI 0.70, 0.74; for advanced cancer 0.75; 95% CI 0.72, 0.78). Associations between PSA and prostate cancer were virtually identical for the initial and replication data sets with 81% (95% CI 77%, 86%) of advanced cases found in men with PSA above the median (0.63 ng/ml at age 44 – 50).

Conclusion

A single PSA at or before age 50 predicts advanced prostate cancer diagnosed up to 30 years later. Use of early PSA to stratify risk would allow a large group of men to be screened less often but increase frequency of testing on a more limited number of high-risk men. This is likely to improve the ratio of benefits to harms for screening.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Background

Several studies have shown that abnormal levels of nuclear matrix protein 22 (NMP22) are associated with bladder cancer, leading to NMP22 being approved as a urinary biomarker by the FDA. Nonetheless, the clinical significance of NMP22 remains unclear.

Objective

To use decision analysis to determine whether NMP22 improves medical decision-making.

Design, Setting, and Participants

The study included 2,222 patients with a history of non–muscle-invasive bladder cancer and current negative cytology. We developed models to predict cancer recurrence or progression to muscle-invasive disease using NMP22 levels, age, and gender.

Intervention(s)

Voided NMP22 and cystoscopy.

Measurements

Clinical net benefit was calculated by summing the benefits (true positives) and subtracting the harms (false positives) and weighting these by the threshold probability at which a patient or clinician would opt for cytoscopy.

Results and limitations

After cystoscopy, 581 (26%) patients were found to have cancer. NMP22 level was significantly associated with bladder cancer recurrence and progression (p<0.001 for both). Using NMP22 in a model with age and gender was associated with better patient outcomes than performing cystoscopy on everyone for threshold probabilities above 8% for recurrence and above 3% for progression. Only offering cystoscopy to those with a 15% or greater risk would reduce the number of cystoscopies by 229, while missing only 25 cancer recurrences per 1000 men with a negative cytology. The study was limited by its multicenter design.

Conclusions

For clinicians who would perform a cystoscopy at a threshold of 5% for recurrence or 1% for progression, NMP22 will not aid clinical decision-making. For less risk-averse clinicians who would only perform a cystoscopy at a threshold probability >8% for recurrence or >3% for progression, NMP22 can help determine which patients require cystoscopy and which can be spared this procedure.

Purpose

Finasteride has been shown to reduce the incidence of prostate cancer. Yet the use of finasteride remains low, likely because of the risk of adverse effects. We sought to determine whether prostate-specific antigen (PSA) levels could identify a high-risk subgroup for which the benefits of finasteride treatment outweigh the potential harms.

Patients and Methods

Raw data from the Prostate Cancer Prevention Trial were used to model chemopreventive treatment strategies: treat all men, treat no men, or treat a high-risk subgroup based on PSA level. We weighted the benefits (reduction in cancer rate) and harms (treatment rate) of each strategy using numbers-needed-to-treat thresholds—the maximum number of men a clinician would treat with finasteride to prevent one cancer.

Results

Of 9,058 men, 1,957 were diagnosed with prostate cancer during the 7-year study. For the end point of all cancers, including both for-cause and end-of-study biopsies, the optimal strategy is to treat all or nearly all men. To reduce risk of cancers detected through routine care, treating men with PSA > 1.3 or > 2 ng/mL is optimal. For example, treating only men with PSA > 2 ng/mL reduced the treatment rate by 83% and resulted in a cancer rate only 1.1% higher than treating all men.

Conclusion

Clinicians wishing to reduce the risk of any biopsy-detectable prostate cancer should recommend finasteride to all men. Clinicians who believe that it is unnecessary to prevent all cancers, but that preventing those readily detectable by screening would be desirable, would be best off recommending finasteride only to a high-risk subgroup.

Prostate-specific antigen (PSA) dynamics have been proposed to predict outcome in men with prostate cancer. We assessed the value of PSA velocity (PSAV) and doubling time (PSADT) for predicting prostate-cancer–specific mortality (PCSM) in men with clinically localized prostate cancer undergoing conservative management or early hormonal therapy. From 1990 to 1996, 2333 patients were identified, of whom 594 had two or more PSA values before diagnosis. We examined 12 definitions for PSADT and 10 for PSAV. Because each definition required PSA measurements at particular intervals, the number of patients eligible for each definition varied from 40 to 594 and number of events from 10 to 119. Four PSAV definitions, but no PSADT, were significantly associated with PCSM after adjustment for PSA in multivariable Cox proportional hazards regression. All 4 could be calculated only for a proportion of events, and the enhancements in predictive accuracy associated with PSAV had very wide confidence intervals. There was no clear benefit of PSAV in men with low PSA and Gleason grade 6 or less. Although evidence that certain PSAV definitions help predict PCSM in the cohort exist, the value of incorporating PSAV in predictive models to assist in determining eligibility for conservative management is, at best, uncertain.

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.

Abstract

Background

Numerous technical modifications to radical prostatectomy have been proposed. Such modifications are likely to lead to only slight improvements in outcomes. Although small differences would be worthwhile, an appropriately powered randomized trial would need to be very large, and thus of doubtful feasibility given the expense, complexity and regulatory burden of contemporary clinical trials. We have proposed a novel methodology, the clinically-integrated randomized trial, which dramatically streamlines trial procedures in order to reduce the marginal cost of an additional patient towards zero. We aimed to determine the feasibility of implementing such a trial for radical prostatectomy.

Methods

Patients undergoing radical prostatectomy as initial treatment for prostate cancer were randomized in a factorial design to involvement of the fascia during placement of the anastomotic sutures, urethral irrigation, both or neither. Endpoint data were obtained from routine clinical documentation. Accrual and compliance rates were monitored to determine the feasibility of the trial.

Results

From a total of 260 eligible patients, 154 (59%) consented; 56 patients declined to participate, 20 were not approached on recommendation of the treating surgeon, and 30 were not approached for logistical reasons. Although recording by surgeons of the procedure used was incomplete (~80%), compliance with randomization was excellent when it was recorded, with only 6% of procedures inconsistent with allocation. Outcomes data was received from 71% of patients at one year. This improved to 83% as the trial progressed.

Conclusions

A clinically-integrated randomized trial was conducted at low cost, with excellent accrual, and acceptable compliance with treatment allocation and outcomes reporting. This demonstrates the feasibility of the methodology. Improved methods to ensure documentation of surgical procedures would be required before wider implementation.

Trial registration

ClinicalTrials.gov NCT00928850