A single early prostate-specific antigen (PSA) level has been correlated with a higher likelihood of prostate cancer diagnosis and death in younger men. PSA testing in older men has been considered of limited utility. We evaluated prostate cancer death in relation to age and PSA level immediately prior to prostate cancer diagnosis. Using the Veterans Affairs database, we identified 230,081 men aged 50-89 years diagnosed with prostate cancer and at least one prior PSA test between 1999 and 2009. Prostate cancer-specific death over time was calculated for patients stratified by age group (e.g., 50-59 years, through 80-89 years) and PSA range at diagnosis (10 ranges) using Kaplan-Meier methods. Risk of 10-year prostate cancer mortality across age and PSA was compared using log-rank tests with a Bonferroni adjustment for multiple testing. 10.5% of men diagnosed with prostate cancer died of cancer during the 10-year study period (mean follow-up = 3.7 years). Higher PSA values prior to diagnosis predict a higher risk of death in all age groups (p < 0.0001). Within the same PSA range, older age groups are at increased risk for death from prostate cancer (p < 0.0001). For PSA of 7-10 ng/mL, cancer-specific death, 10 years after diagnosis, increased from 7% for age 50-59 years to 51% for age 80-89 years. Men older than 70 years are more likely to die of prostate cancer at any PSA level than younger men, suggesting prostate cancer remains a significant problem among older men (even those aged 80+) and deserves additional study.

Purpose

The Kattan preoperative nomogram combines preoperative prostate specific antigen (PSA), biopsy Gleason grade and clinical stage to estimate disease recurrence after radical prostatectomy. Several studies using patient data from academic centers have validated the nomogram. We assessed the performance of the Kattan nomogram using the Cancer of the Prostate Strategic Urological Research Endeavor database, a national, largely community based observational disease registry.

Materials and methods

From the Cancer of the Prostate Strategic Urological Research Endeavor database we identified 1701 men with clinically localized prostate cancer undergoing radical prostatectomy with sufficient pretreatment information and PSA followup after surgery. Disease recurrence was defined as 2 consecutive PSA values 0.2 ng/ml or greater, or a second cancer treatment more than 6 months after prostatectomy. A concordance index was used to evaluate the performance of the nomogram compared to observed 5-year recurrence-free survival (Kaplan-Meier). Kattan nomogram scores were calculated for each patient and stratified into 6 groups for analysis.

Results

In our cohort of 1701 men 413 (24%) had evidence of disease recurrence. Median followup in these patients was 2.3 years. Kattan nomogram scores were 17% to 99% (mean 79%). The overall concordance index was 0.68. Varying the definition of recurrent disease and excluding patients with imputed data did not substantially alter nomogram performance (concordance index 0.65 to 0.70). The Kattan nomogram tended to overestimate 5-year freedom from recurrence in patients with scores of 65% and higher.

Conclusions

We noted the reasonable performance of the Kattan nomogram for predicting cancer outcomes after radical prostatectomy using a community based population. Although concordance is lower than in previous validation studies and the nomogram overestimates recurrence-free survival in patients at lower risk, the model is fairly robust and it provides important information when counseling patients regarding treatment options in the community setting. Further refinements in pretreatment estimation of disease-free survival and ultimately overall survival are needed.

Purpose

Nomograms are tools used in clinical practice to predict cancer outcomes and to help make decisions regarding management of disease. Since its conception, utility of the prostate cancer nomogram has more than tripled. Limited information is available on the relation between the nomograms' predicted probabilities and obesity. The purpose of this study was to examine whether the predictions from a validated postoperative prostate cancer nomogram were associated with obesity.

Methods

We carried out a cross-sectional analysis of 1220 patients who underwent radical prostatectomy (RP) in southern California from 2000 to 2008. Progression-free probabilities (PFPs) were ascertained from the 10-year Kattan postoperative nomogram. Multivariable logistic regression models estimated odds ratios (ORs) and 95% confidence intervals (CIs).

Results

In the present study, aggressive prostate cancer (Gleason ≥7), but not advanced stage, was associated with obesity (p = 0.01). After adjusting for age, black race, family history of prostate cancer and current smoking, an inverse association was observed for 10-year progression-free predictions (OR = 0.50; 95% CI = 0.28–0.90) and positive associations were observed for preoperative PSA levels (OR = 1.23; 95% CI = 1.01–1.50) and Gleason >7 (OR = 1.45; 95% CI = 1.11–1.90).

Conclusion

Obese RP patients were more likely to have lower PFP values than non-obese patients, suggesting a higher risk of experiencing prostate cancer progression. Identifying men with potentially higher risks due to obesity may improve disease prognosis and treatment decision-making.

Purpose

To explore whether disparities in outcomes exist between African American (AA) and Caucasian (CS) men with low-grade prostate cancer and similar cancer of the prostate risk assessment-postsurgery (CAPRA-S) features following prostatectomy (RP).

Methods

The overall cohort consisted of 1,265 men (234 AA and 1,031 CS) who met the National comprehensive cancer network criteria for low- to intermediate-risk prostate cancer and underwent RP between 1990 and 2012. We first evaluated whether clinical factors were associated with adverse pathologic outcomes and freedom from biochemical failure (FFbF) using the entire cohort. Next, we studied a subset of 705 men (112 AA and 593 CS) who had pathologic Gleason score≤6 (low-grade disease). Using this cohort, we determined whether race affected FFbF in men with RP-proven low-grade disease and similar CAPRA-S scores.

Results

With a median follow-up time of 27 months, the overall 7-year FFbF rate was 86% vs. 79% in CS and AA men, respectively (P = 0.035). There was no significant difference in one or more adverse pathologic features between CS vs. AA men (27% vs. 31%; P = 0.35) or CAPRA-S score (P = 0.28). In the subset analysis of patients with low-grade disease, AA race was associated with worse FFbF outcomes (P = 0.002). Furthermore, AA race was a significant predictor of FFbF in men with low-grade disease (hazard ratio = 2.01, 95% CI: 1.08-3.72; P = 0.029).

Conclusions

AA race is a predictor of worse FFbF outcomes in men with low-grade disease after RP. These results suggest that a subset of AA men with low-grade disease may benefit from more aggressive treatment.

OBJECTIVE: With each recurrence the prognosis of bipolar disorder (BD) worsens, indicating the need to identify the factors associated with increased recurrence risk. The course of BD is heterogenous and although risk factors for recurrence for the group as a whole have been reported in the literature, identification of risk factors for a specific individual are crucial for developing personalized treatments. METHOD: A total of 363 recovered BD youths/young adults from the Course and Outcome of Bipolar Youth (COBY) study were included. Participants were evaluated on average every 7 months for a median of 12.5 years and interviewed with standard instruments. Risk factors of recurrence from the literature were used to build a risk calculator (RC) to predict recurrence risk at different time intervals. RESULTS: Approximately 80% of participants had at least one syndromal recurrence and 60% had ≥2 recurrences, particularly depressions. The 6-month and 1-, 2-, 3-, and 5-year RC showed an accuracy between 72% and 82% for predicting any mood recurrences, and up to 80% for depression and 89% for hypo/mania (sensitivity/specificity both 0.74). The most influential recurrence risk factors were shorter recovery lengths, younger age at assessment, earlier mood onset, and more severe prior depression. Although important, other factors associated with recurrence risk, such as interepisodic subsyndromal mood symptoms and comorbidities, did not influence the RC score beyond factors noted above. CONCLUSION: The RC provides a useful tool for predicting an individuals recurrence risk of depression and/or hypo/mania in BD youths and for developing personalized interventions and informing research. Replication studies are warranted.

Objective

With each recurrence the prognosis of bipolar disorder (BD) worsens, indicating the need to identify the factors associated with increased recurrence risk. The course of BD is heterogenous and although risk factors for recurrence for the group as a whole have been reported in the literature, identification of risk factors for a specific individual are crucial for developing personalized treatments.

Method

A total of 363 recovered BD youths/young adults from the Course and Outcome of Bipolar Youth (COBY) study were included. Participants were evaluated on average every 7 months for a median of 12.5 years and interviewed with standard instruments. Risk factors of recurrence from the literature were used to build a risk calculator (RC) to predict recurrence risk at different time intervals.

Results

Approximately 80% of participants had at least one syndromal recurrence and 60% had ≥2 recurrences, particularly depressions. The 6-month and 1-, 2-, 3-, and 5-year RC showed an accuracy between 72% and 82% for predicting any mood recurrences, and up to 80% for depression and 89% for hypo/mania (sensitivity/specificity both 0.74). The most influential recurrence risk factors were shorter recovery lengths, younger age at assessment, earlier mood onset, and more severe prior depression. Although important, other factors associated with recurrence risk, such as interepisodic subsyndromal mood symptoms and comorbidities, did not influence the RC score beyond factors noted above.

Conclusion

The RC provides a useful tool for predicting an individual's recurrence risk of depression and/or hypo/mania in BD youths and for developing personalized interventions and informing research. Replication studies are warranted.

Objective

To develop statistical models predicting recurrent pelvic organ prolapse, surgical complications, and change in health status 12 months after apical prolapse surgery.

Methods

Logistic regression models were developed using a combined cohort from three randomized trials and two prospective cohort studies from 1,301 participants enrolled in surgical studies conducted by the Pelvic Floor Disorders Network. Composite recurrent prolapse was defined as prolapse beyond the hymen; the presence of bothersome bulge symptoms; or prolapse reoperation or retreatment within 12 months after surgery. Complications were defined as any serious adverse event or Dindo grade III complication within 12 months of surgery. Significant change in health status was defined as a minimum important change of SF-6D utility score (±0.035 points) from baseline. Thirty-two candidate risk factors were considered for each model and model accuracy was measured using concordance indices. All indices were internally validated using 1,000 bootstrap resamples to correct for bias.

Results

The models accurately predicted composite recurrent prolapse (concordance index=0.72, 95% CI 0.69-0.76), bothersome vaginal bulge (concordance index=0.73, 95% CI 0.68-0.77), prolapse beyond the hymen (concordance index=0.74, 95% CI 0.70-0.77), serious adverse event (concordance index=0.60, 95% CI 0.56-0.64), Dindo grade III or greater complication (concordance index=0.62, 95% CI 0.58-0.66), and health status improvement (concordance index=0.64, 95% CI 0.62-0.67) or worsening (concordance index=0.63, 95% CI 0.60-0.67). Calibration curves demonstrated all models were accurate through clinically useful predicted probabilities.

Conclusion

These prediction models are able to provide accurate and discriminating estimates of prolapse recurrence, complications, and health status 12 months after prolapse surgery.

It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.

Background

Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems.

Objective

We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool.

Design, setting, and participants

We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006-2017 at eight North American institutions for model-building and three European institutions for validation.

Outcome measurements and statistical analysis

We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts.

Results and limitations

Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2-76.8), a small improvement over the AUC of 72.3% (70.9-73.7) for the PCPTRC (p<0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies.

Conclusions

The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome.

Patient summary

A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.

Objective

Approximately 20%-35% of individuals 12-35 years old who meet criteria for a prodromal risk syndrome convert to psychosis within 2 years. However, this estimate ignores the fact that clinical high-risk cases vary considerably in risk. The authors sought to create a risk calculator, based on profiles of risk indicators, that can ascertain the probability of conversion to psychosis in individual patients.

Method

The study subjects were 596 clinical high-risk participants from the second phase of the North American Prodrome Longitudinal Study who were followed up to the time of conversion to psychosis or last contact (up to 2 years). The predictors examined were limited to those that are supported by previous studies and are readily obtainable in general clinical settings. Time-to-event regression was used to build a multivariate model predicting conversion, with internal validation using 1,000 bootstrap resamples.

Results

The 2-year probability of conversion to psychosis was 16%. Higher levels of unusual thought content and suspiciousness, greater decline in social functioning, lower verbal learning and memory performance, slower speed of processing, and younger age at baseline each contributed to individual risk for psychosis. Stressful life events, trauma, and family history of schizophrenia were not significant predictors. The multivariate model achieved a concordance index of 0.71 and, as reported in an article by Carrión et al., published concurrently with this one, was validated in an independent external data set. The results are instantiated in a web-based risk prediction tool envisioned to be most useful in research protocols involving the psychosis prodrome.

Conclusions

A risk calculator comparable in accuracy to those for cardiovascular disease and cancer is available to predict individualized conversion risks in newly ascertained clinical high-risk cases. Given that the risk calculator can be validly applied only for patients who screen positive on the Structured Clinical Interview for Psychosis Risk Syndromes, which requires training to administer, its most immediate uses will be in research on psychosis risk factors and in research-driven clinical (prevention) trials.