Competing Risks of Mortality in Patients with Prostate Cancer

A patient’s life expectancy must be integrated into critical decision-making regarding treatment of patients with localized PCa. 

Accurately quantifying life expectancy in routine practice, however, is challenging. Waltz et al described a predictive model for patients with localized PCa that integrates age and comorbidity status and affords an estimate of 10-year life expectancy for patients who underwent either a radical prostatectomy or external beam radiation therapy treatments.9

The accuracy of the model is nearly 85%. The challenge with using the model is that the Charlson comorbidity index must be calculated for the patient in question before the nomogram can be used.  Nevertheless, using the web-based operationalization tool on life expectancy prediction can be generated rather rapidly using this model (Figure 1).

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A simple assessment of life expectancy, however, is limited.  The ideal predictive model would integrate competing risks of death in a given patient with localized PCa and compare these risks with the odds of dying from prostatic malignancy. Recently Albertsen et al updated their original publication10 to generate competing risks probability tables for patients diagnosed with T1c disease as stratified by Gleason Sum and Charlson Comorbidity Index.11 

Again, such tools are extremely useful for framing a discussion, especially with an elderly and/or comorbid patient who is diagnosed with localized prostatic malignancy.

Figure 1

Pre-Prostatectomy Prediction of Biochemical Failure:

When counseling patients regarding radical prostatectomy for localized PCa, the urologist must set realistic expectations. Depending on the clinicopathologic variables of a given patient’s disease, there is a given risk for PCa recurrence following surgery. 

These risks must be objectively assessed and balanced against surgical tradeoffs in order to avoid disappointment or regret regarding treatment choices. These risks can also serve as the framework regarding discussions of the potential need for adjuvant/salvage radiation treatments. The original nomogram predicting five-year biochemical failure rates, which was published by Kattan et al in 199812 has since been updated. The current model from the same group is arguably the most robust and clinically useful predictive tool for assessing one- to 10-year probability of PSA recurrence following prostatectomy13

The nomogram integrates variables such as PSA, number of biopsy cores involved by cancer, clinical stage, and Gleason score to generate predicted biochemical failure up to 10 years following surgery (Figure 2). This model has been externally validated and has been shown to have an accuracy of 79%.13  The University of California in San Francisco CAPRA score is another very useful tool that can be used to risk stratify patients prior to surgery and help predict recurrence rates following radical prostatectomy.14

Figure 2



In summary, while predictive models are not without shortcomings, these tools afford objective metrics that can help guide clinical decisions regarding risk stratification and tradeoffs. is a novel web portal that allows rapid point-of-care utilization of published statistical models. This tool affords real-time objectification of critical-decision making in a busy clinical setting.

Alexander Kutikov, MD, and Robert G. Uzzo, MD, are affiliated with the Fox Chase Cancer Center in Philadelphia. Dr. Kutikov is Assistant Professor of Urologic Oncology and Dr. Uzzo is Professor of Surgery and Chairman of the Department Surgery and holds the G. Willing “Wing” Pepper Chair in Cancer Research. Dr. Uzzo also is Medical Director, Urology, for Renal & Urology News.


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