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).

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

 

Conclusions

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.  Cancernomograms.com 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.

References

  1. Esserman L, Shieh Y, Thompson I. Rethinking screening for breast cancer and prostate cancer. JAMA. 2009;302:1685-1692.
  2. Jemal A, Siegel R, Xu J, et al. Cancer statistics, 2010. CA Cancer J Clin. 2010;60: 277-300.
  3. Welch HG, Black WC. Overdiagnosis in cancer. J Natl Cancer Inst. 2010;102: 605-613.
  4. Shariat SF, Kattan MW, Vickers AJ, et al. Critical review of prostate cancer predictive tools. Future Oncol. 2009;5:1555-1584.
  5. Shariat SF, Karakiewicz PI, Roehrborn CG, et al. An updated catalog of prostate cancer predictive tools. Cancer. 2008;113:3075-3099.
  6. Nam RK, Toi A, Klotz LH, et al. Assessing individual risk for prostate cancer. J Clin Oncol. 2007;25:3582-3588.
  7. Thompson IM, Ankerst DP, Chi C, et al. Assessing prostate cancer risk: results from the Prostate Cancer Prevention Trial. J Natl Cancer Inst. 2006;98:529-534.
  8. Karakiewicz PI, Benayoun S, Kattan MW, et al. Development and validation of a nomogram predicting the outcome of prostate biopsy based on patient age, digital rectal examination and serum prostate specific antigen. J Urol. 2005;173:1930-1934.
  9. Walz J, Gallina A, Saad F, et al. A nomogram predicting 10-year life expectancy in candidates for radical prostatectomy or radiotherapy for prostate cancer. J Clin Oncol. 2007;25:3576-3581.
  10. Albertsen PC, Hanley JA, Gleason DF, et al. Competing risk analysis of men aged 55 to 74 years at diagnosis managed conservatively for clinically localized prostate cancer. JAMA. 1998;280:975-980.
  11. Albertsen PC, Moore DF, Shih W, et al. Impact of comorbidity on survival among men with localized prostate cancer. J Clin Oncol. 2011;29:1335-1341.
  12. Kattan MW, Eastham JA, Stapleton AM, et al. A preoperative nomogram for disease recurrence following radical prostatectomy for prostate cancer. J Natl Cancer Inst. 1998;90:766-771.
  13. Stephenson AJ, Scardino PT, Eastham JA, et al. Preoperative nomogram predicting the 10-year probability of prostate cancer recurrence after radical prostatectomy. J Natl Cancer Inst. 2006;98:715-717.
  14. Cooperberg MR, Pasta DJ, Elkin EP, et al. The University of California, San Francisco Cancer of the Prostate Risk Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J Urol. 2005;173:1938-1942.