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PURPOSE: The most favorable outcome that can be achieved after radical prostatectomy (RP) is complete tumor resection with full recovery of continence and potency. Risks of erectile dysfunction, urinary incontinence and disease recurrence have been described, but separately these estimates do not adequately inform patients of the likelihood of returning to their preoperative functional status while remaining cancer-free. The purpose of this study was to estimate the probability of achieving optimal clinical outcome – absence of disease with full recovery of potency and continence – after RP.
METHODS: We used a Markov state-transition model to simulate clinical outcomes in the first 4 years following RP. The primary endpoint was time until full recovery of continence and potency with no evidence of disease. The model consisted of three mutually exclusive health states: (1) incomplete recovery (impotent or incontinent with no evidence of disease); (2) complete recovery (potent and continent with no evidence of disease); and (3) disease recurrence, as demonstrated by elevated serum prostate specific antigen (PSA). All patients started in the incomplete recovery state. Disease recurrence was possible from both the incomplete and complete recovery states. Transition probabilities were estimated from a clinical database of 647 men who underwent RP for clinical stage T1 to T3 prostate cancer. The model used a one-month Markov cycle length.
RESULTS: Complete recovery was achieved in 30% of men at 12 months, 42% at 24 months, and 53% at 48 months postoperatively. In the first 4 years following surgery, the cohort spent an average of 17.6 months in the optimal outcome state. Fewer than 10% of patients experienced disease recurrence by the end of 4 years.
CONCLUSIONS: Our results suggest that a majority of prostate cancer patients can achieve full potency, continence, and cancer control by 48 months after RP. Information about the individual risks of impotence, incontinence and disease recurrence may be less useful to patients than estimates of the likelihood of achieving the optimal post-operative outcome and the expected duration of that outcome. Markov modeling may provide valuable information in clinical situations where multiple dynamic outcomes are relevant to the decision-maker, and where estimating the joint probability and duration of these outcomes is not straightforward using standard statistical methods.
See more of Poster Session - Utility Theory; Health Economics; Patient & Physician Preferences; Simulation; Technology Assessment
See more of The 26th Annual Meeting of the Society for Medical Decision Making (October 17-20, 2004)