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Background:Men with persistently elevated and/or rising PSA levels after negative prostate biopsy often undergo multiple repeat biopsies. Prostate cancer antigen 3 (PCA3) has emerged as a predictor of prostate cancer.Methods:We sought to define the utility of PCA3 in combination with other clinical data in predicting the risk of prostate cancer on repeat biopsy. We retrospectively obtained PCA3, PSA, PSA density (PSAD), digital rectal examination (DRE) and transrectal ultrasound (TRUS) findings from 103 patients at a single institution who had at least one prior negative prostate biopsy. The sensitivity and specificity of PCA3 in detecting prostate cancer was determined. Receiver operating characteristics curves were produced for each variable individually and in multivariable analysis, controlling for PCA3, PSAD, TRUS, PSA and DRE. A nomogram was created, internally validated and compared to another recently published nomogram.Results:Of the 103 patients, 37 (31%) had prostate cancer on repeat biopsy. The sensitivity and specificity of PCA3 (using a cut point of 25) was 0.67 and 0.64, respectively. In multivariable analyses, PCA3 was independently associated with prostate cancer (odds ratio: 1.02, 95% confidence interval: 1.01-1.04), with area under the curve (AUC) of 0.64. A multivariable model containing PCA3, PSAD, PSA, DRE and TRUS findings showed the most diagnostic accuracy (AUC: 0.82).Conclusions:In the setting of prior negative biopsies, PCA3 was independently associated with prostate cancer in a multivariable model. In combination with other clinical data, PCA3 is a valuable tool in assessing the risk of prostate cancer on repeat biopsy. [ABSTRACT FROM AUTHOR]

Commonly used measures of comorbidity assess comorbidity number and type but not severity. We sought to evaluate the impact of comorbidity severity on longitudinal health-related quality of life (HRQOL) in men treated with radical prostatectomy (RP) or radiation therapy (RT) using the Total Illness Burden Index for prostate cancer (TIBI-CaP). We sampled 738 men with non-metastatic prostate cancer treated with RP or RT from the Cancer of the Prostate Strategic Urologic Research Endeavor registry. We examined the impact of comorbidity severity on generic and disease-specific HRQOL at baseline and at 6, 12, 18 and 24 months post-treatment. Men with worse TIBI-CaP comorbidity had significantly lower baseline and post-treatment HRQOL in all domains at all time points. In a multivariate model, men with moderate or severe TIBI-CaP comorbidity had significantly worse HRQOL scores at 12 and 24 months after treatment in all domains except sexual and urinary function (P<0.05); in these domains, severe comorbidity was predictive of lower HRQOL (P<0.05). Comorbidity groups had similar absolute declines in HRQOL from baseline to 6 and 24 months after treatment. Although comorbidity groups experienced similar long-term declines from baseline HRQOL after treatment, men with more severe comorbidity had significantly lower baseline scores and therefore poorer long-term HRQOL. [ABSTRACT FROM AUTHOR]

With growing number of older adults in the United States and complexity of issues related to Medicare and other insurances more research is needed to evaluate an effectiveness of the different insurance types in prevention, screening and treatment of cancer. With prostate cancer being highly prevalent disease in older men, the importance of appropriate treatment and favorable outcomes is imperative. In this study we examine whether prostate cancer outcomes, such as risk category at diagnosis, treatment and survival differ in relationship to insurance status in older patients in CaPSURE. Data were abstracted from CaPSURE, a longitudinal observational database of 13 124 men with prostate cancer. Men were selected for the study if they were older than 65 years old at diagnosis, newly diagnosed between 1995 and 2005 at entry to CaPSURE with localized disease and received radical prostatectomy (RP), external beam radiation (EBRT), brachytherapy (BT), hormonal therapy or expectant management (EM). Insurance status was summarized by eight categories: Medicare only, Medicare+supplement, Medicare+HMO, Medicare+PPO, Medicare+FFS, health maintenance organization (HMO), preferred provider organization (PPO) and Veteran's Administration (VA). A total of 2983 men met the inclusion criteria. Odds ratios (OR) for the likelihood of receiving each type of therapy compared to RP by insurance status and likelihood of presenting with high-risk classification at diagnosis were derived using multinomial logistic regression, adjusting for clinical and demographic characteristics. Difference in survival between insurance groups was evaluated by Cox's multivariate regression. Multivariate analysis demonstrated a strong association between initial treatment and insurance status. Compared to Medicare patients, men in the CaPSURE database treated at HMO, PPO and VA systems were more likely to receive BT than RP (OR, 1.71–1.92) and less likely to receive this treatment if they were in Medicare+FFS and Medicare+PPO (OR, 0.18–0.38). Hormonal treatment demonstrated similar pattern, however OR did not reached statistical significance for HMO and PPO. Use of EM was much more predominant for patients in VA system (OR, 4.74; 95% CI, 1.94–11.55). Use of EBRT was significantly associated with type of insurance. Men with VA, Medicare+FFS and Medicare+PPO insurance were less likely to receive this treatment compared to RP. Survival and clinical risk at diagnosis was associated with insurance status in univariate analysis but this association diminished after adjusting for possible covariates. This study provides important information on relationship between insurance status and several outcomes in patients with prostate cancer. Even after controlling for important clinical and sociodemographic factors we found marked differences in prostate cancer treatment according to type of insurance. Future explorations of associations between health care delivery system, cancer care and outcomes are needed.Prostate Cancer and Prostatic Diseases (2008) 11, 280–287; doi:10.1038/sj.pcan.4501015; published online 25 September 2007 [ABSTRACT FROM AUTHOR]