Peter C. Albertsen

The effect of finasteride on the risk of acute urinary retention and the need for surgical treatment among men with benign prostatic hyperplasia

Background Finasteride is known to improve urinary symptoms in men with benign prostatic hyperplasia, but the extent to which the benefit is sustained and whether finasteride reduces the incidence of related events, including the need for surgery and the development of acute urinary retention, are not known. Methods In this double-blind, randomized, placebo-controlled trial, we studied 3040 men with moderate-to-severe urinary symptoms and enlarged prostate glands who were treated daily with 5 mg of finasteride or placebo for four years. Symptom scores (on a scale of 1 to 34), urinary flow rates, and the occurrence of outcome events were assessed every four months in 3016 men. Prostate volume was measured in a subgroup of the men. Complete data on outcomes were available for 2760 men. Results During the four-year study period, 152 of the 1503 men in the placebo group (10 percent) and 69 of the 1513 men in the finasteride group (5 percent) underwent surgery for benign prostatic hyperplasia (reduction in risk with finasteride, 55 percent; 95 percent confidence interval, 41 to 65 percent). Acute urinary retention developed in 99 men (7 percent) in the placebo group and 42 men (3 percent) in the finasteride group (reduction in risk with finasteride, 57 percent; 95 percent confidence interval, 40 to 69 percent). Among the men who completed the study, the mean decreases in the symptom score were 3.3 in the finasteride group and 1.3 in the placebo group (P<0.001). Treatment with finasteride also significantly improved urinary flow rates and reduced prostate volume (P<0.001). Conclusions Among men with symptoms of urinary obstruction and prostatic enlargement, treatment with finasteride for four years reduces symptoms and prostate volume, increases the urinary flow rate, and reduces the probability of surgery and acute urinary retention.

Early Detection of Prostate Cancer: AUA Guideline

PURPOSE The guideline purpose is to provide the urologist with a framework for the early detection of prostate cancer in asymptomatic average risk men. MATERIALS AND METHODS A systematic review was conducted and summarized evidence derived from over 300 studies that addressed the predefined outcomes of interest (prostate cancer incidence/mortality, quality of life, diagnostic accuracy and harms of testing). In addition to the quality of evidence, the panel considered values and preferences expressed in a clinical setting (patient-physician dyad) rather than having a public health perspective. Guideline statements were organized by age group in years (age <40; 40 to 54; 55 to 69; ≥ 70). RESULTS Except prostate specific antigen-based prostate cancer screening, there was minimal evidence to assess the outcomes of interest for other tests. The quality of evidence for the benefits of screening was moderate, and evidence for harm was high for men age 55 to 69 years. For men outside this age range, evidence was lacking for benefit, but the harms of screening, including over diagnosis and overtreatment, remained. Modeled data suggested that a screening interval of two years or more may be preferred to reduce the harms of screening. CONCLUSIONS The Panel recommended shared decision-making for men age 55 to 69 years considering PSA-based screening, a target age group for whom benefits may outweigh harms. Outside this age range, PSA-based screening as a routine could not be recommended based on the available evidence.

Long-Term Functional Outcomes after Treatment for Localized Prostate Cancer

BACKGROUND The purpose of this analysis was to compare long-term urinary, bowel, and sexual function after radical prostatectomy or external-beam radiation therapy. METHODS The Prostate Cancer Outcomes Study (PCOS) enrolled 3533 men in whom prostate cancer had been diagnosed in 1994 or 1995. The current cohort comprised 1655 men in whom localized prostate cancer had been diagnosed between the ages of 55 and 74 years and who had undergone either surgery (1164 men) or radiotherapy (491 men). Functional status was assessed at baseline and at 2, 5, and 15 years after diagnosis. We used multivariable propensity scoring to compare functional outcomes according to treatment. RESULTS Patients undergoing prostatectomy were more likely to have urinary incontinence than were those undergoing radiotherapy at 2 years (odds ratio, 6.22; 95% confidence interval [CI], 1.92 to 20.29) and 5 years (odds ratio, 5.10; 95% CI, 2.29 to 11.36). However, no significant between-group difference in the odds of urinary incontinence was noted at 15 years. Similarly, although patients undergoing prostatectomy were more likely to have erectile dysfunction at 2 years (odds ratio, 3.46; 95% CI, 1.93 to 6.17) and 5 years (odds ratio, 1.96; 95% CI, 1.05 to 3.63), no significant between-group difference was noted at 15 years. Patients undergoing prostatectomy were less likely to have bowel urgency at 2 years (odds ratio, 0.39; 95% CI, 0.22 to 0.68) and 5 years (odds ratio, 0.47; 95% CI, 0.26 to 0.84), again with no significant between-group difference in the odds of bowel urgency at 15 years. CONCLUSIONS At 15 years, no significant relative differences in disease-specific functional outcomes were observed among men undergoing prostatectomy or radiotherapy. Nonetheless, men treated for localized prostate cancer commonly had declines in all functional domains during 15 years of follow-up. (Funded by the National Cancer Institute.).

Prostate Specific Antigen Best Practice Statement: 2009 Update

PURPOSE We provide current information on the use of PSA testing for the evaluation of men at risk for prostate cancer, and the risks and benefits of early detection. MATERIALS AND METHODS The report is a summary of the American Urological Association PSA Best Practice Policy 2009. The summary statement is based on a review of the current professional literature, clinical experience and the expert opinions of a multispecialty panel. It is intended to serve as a resource for physicians, other health care professionals, and patients. It does not establish a fixed set of guidelines, define the legal standard of care or pre-empt physician judgment in individual cases. RESULTS There are two notable differences in the current policy. First, the age for obtaining a baseline PSA has been lowered to 40 years. Secondly, the current policy no longer recommends a single, threshold value of PSA, which should prompt prostate biopsy. Rather, the decision to proceed to prostate biopsy should be based primarily on PSA and DRE results, but should take into account multiple factors including free and total PSA, patient age, PSA velocity, PSA density, family history, ethnicity, prior biopsy history and comorbidities. CONCLUSIONS Although recently published trials show different results regarding the impact of prostate cancer screening on mortality, both suggest that prostate cancer screening leads to overdetection and overtreatment of some patients. Therefore, men should be informed of the risks and benefits of prostate cancer screening before biopsy and the option of active surveillance in lieu of immediate treatment for certain men diagnosed with prostate cancer.

Prostate Cancer Diagnosis and Treatment After the Introduction of Prostate-Specific Antigen Screening: 1986–2005

BACKGROUND Although there is uncertainty about the effect of prostate-specific antigen (PSA) screening on the rate of prostate cancer death, there is little uncertainty about its effect on the rate of prostate cancer diagnosis. Systematic estimates of the number of men affected, however, to our knowledge, do not exist. METHODS We obtained data on age-specific incidence and initial course of therapy from the National Cancer Institutes Surveillance, Epidemiology, and End Results program. We then used age-specific male population estimates from the US Census to determine the excess (or deficit) in the number of men diagnosed and treated in each year after 1986-the year before PSA screening was introduced. RESULTS Overall incidence of prostate cancer rose rapidly after 1986, peaked in 1992, and then declined, albeit to levels considerably higher than those in 1986. Overall incidence, however, obscured distinct age-specific patterns: The relative incidence rate (2005 relative to 1986) was 0.56 in men aged 80 years and older, 1.09 in men aged 70-79 years, 1.91 in men aged 60-69 years, 3.64 in men aged 50-59 years, and 7.23 in men younger than 50 years. Since 1986, an estimated additional 1 305 600 men were diagnosed with prostate cancer, 1 004 800 of whom were definitively treated for the disease. Using the most optimistic assumption about the benefit of screening-that the entire decline in prostate cancer mortality observed during this period is attributable to this additional diagnosis-we estimated that, for each man who experienced the presumed benefit, more than 20 had to be diagnosed with prostate cancer. CONCLUSIONS The introduction of PSA screening has resulted in more than 1 million additional men being diagnosed and treated for prostate cancer in the United States. The growth is particularly dramatic for younger men. Given the considerable time that has passed since PSA screening began, most of this excess incidence must represent overdiagnosis.

Active Surveillance for Prostate Cancer: A Systematic Review of the Literature

CONTEXT Prostate cancer (PCa) remains an increasingly common malignancy worldwide. The optimal management of clinically localized, early-stage disease remains unknown, and profound quality of life issues surround PCa interventions. OBJECTIVE To systematically summarize the current literature on the management of low-risk PCa with active surveillance (AS), with a focus on patient selection, outcomes, and future research needs. EVIDENCE ACQUISITION A comprehensive search of the PubMed and Embase databases from 1980 to 2011 was performed to identify studies pertaining to AS for PCa. The search terms used included prostate cancer and active surveillance or conservative management or watchful waiting or expectant management. Selected studies for outcomes analysis had to provide a comprehensive description of entry characteristics, criteria for surveillance, and indicators for further intervention. EVIDENCE SYNTHESIS Data from seven large AS series were reviewed. Inclusion criteria for surveillance vary among studies, and eligibility therefore varies considerably (4-82%). PCa-specific mortality remains low (0-1%), with the longest published median follow-up being 6.8 yr. Up to one-third of patients receive secondary therapy after a median of about 2.5 yr of surveillance. Surveillance protocols and triggers for intervention vary among institutions. Most patients are treated for histologic reclassification (27-100%) or prostate-specific antigen doubling time <3 yr (13-48%), while 7-13% are treated with no evidence of progression. Repeat prostate biopsy with a minimum of 12 cores appears to be important for monitoring patients for changes in tumor histology over time. CONCLUSIONS AS for PCa offers an opportunity to limit intervention to patients who will likely benefit the most from radical treatment. This approach confers a low risk of disease-specific mortality in the short to intermediate term. An early, confirmatory biopsy is essential for limiting the risk of underestimating tumor grade and amount.

Outcomes of Localized Prostate Cancer Following Conservative Management

CONTEXT Most newly diagnosed prostate cancers are clinically localized, and major treatment options include surgery, radiation, or conservative management. Although conservative management can be a reasonable choice, there is little contemporary prostate-specific antigen (PSA)-era data on outcomes with this approach. OBJECTIVE To evaluate the outcomes of clinically localized prostate cancer managed without initial attempted curative therapy in the PSA era. DESIGN, SETTING, AND PARTICIPANTS A population-based cohort study of men aged 65 years or older when they were diagnosed (1992-2002) with stage T1 or T2 prostate cancer and whose cases were managed without surgery or radiation for 6 months after diagnosis. Living in areas covered by the Surveillance, Epidemiology, and End Results (SEER) program, the men were followed up for a median of 8.3 years (through December 31, 2007). Competing risk analyses were performed to assess outcomes. MAIN OUTCOME MEASURES Ten-year overall survival, cancer-specific survival, and major cancer related interventions. RESULTS Among men who were a median age of 78 years at cancer diagnosis, 10-year prostate cancer-specific mortality was 8.3% (95% confidence interval [CI], 4.2%-12.8%) for men with well-differentiated tumors; 9.1% (95% CI, 8.3%-10.1%) for those with moderately differentiated tumors, and 25.6% (95% CI, 23.7%-28.3%) for those with poorly differentiated tumors. The corresponding 10-year risks of dying of competing causes were 59.8% (95% CI, 53.2%-67.8%), 57.2% (95% CI, 52.6%-63.9%), and 56.5% (95% CI, 53.6%-58.8%), respectively. Ten-year disease-specific mortality for men aged 66 to 74 years diagnosed with moderately differentiated disease was 60% to 74% lower than earlier studies: 6% (95% CI, 4%-8%) in the contemporary PSA era (1992-2002) compared with results of previous studies (15%-23%) in earlier eras (1949-1992). Improved survival was also observed in poorly differentiated disease. The use of chemotherapy (1.6%) or major interventions for spinal cord compression (0.9%) was uncommon. CONCLUSIONS Results following conservative management of clinically localized prostate cancer diagnosed from 1992 through 2002 are better than outcomes among patients diagnosed in the 1970s and 1980s. This may be due, in part, to additional lead time, overdiagnosis related to PSA testing, grade migration, or advances in medical care.

Single-Therapy Androgen Suppression in Men with Advanced Prostate Cancer: A Systematic Review and Meta-Analysis

Androgen ablation delays clinical progression and palliates symptoms of metastatic disease in men with advanced prostate cancer (1-4). The earliest method was orchiectomy, and diethylstilbestrol (DES) subsequently became the first reversible method (5-7). Newer alternatives include luteinizing hormone-releasing hormone (LHRH) agonists, such as leuprolide, goserelin, and buserelin (8-10), and nonsteroidal antiandrogens, such as flutamide, nilutamide, and bicalutamide (11-13). Cyproterone acetate is the only steroidal antiandrogen still used for primary hormonal therapy (14-16). Many randomized, controlled trials have compared two or more of these options for monotherapy in men with advanced prostate cancer. Additional trials have tested the efficacy of antiandrogens combined with orchiectomy or LHRH agonists, an approach that is often called combined or maximal androgen blockade. Previous meta-analyses have compared monotherapy with combined androgen blockade (17-19). To date, no systematic review or meta-analysis has evaluated the evidence on effectiveness of monotherapies. Systematic reviews offer structured analysis of results of primary investigations by using strategies to limit bias and random error. They efficiently integrate otherwise unmanageable amounts of information to support clinical decision making. When it is feasible, quantitative meta-analysis can increase power and precision and enhance estimates of treatment effects and exposure risks. Meta-analysis also allows evaluation of consistency of findings or exploration of differences in outcomes, according to predefined subpopulations or factors regarding study quality. As part of a comprehensive review of the evidence on the relative effectiveness and cost-effectiveness of methods of androgen suppression as primary treatment for advanced prostate cancer (20), we conducted a systematic review and meta-analysis of randomized, controlled trials that compared different monotherapies. We establish that DES is equivalent to orchiectomy as a comparator for treatments of advanced prostate cancer and summarize our findings on four questions: 1) How effective is an LHRH agonist compared with orchiectomy or DES? 2) How effective is an antiandrogen compared with orchiectomy, DES, or an LHRH agonist? 3) Do the LHRH agonists differ in effectiveness? and 4) Do the antiandrogens differ in effectiveness? Although we sought to compare the adverse effects and quality-of-life effects of these treatments, scant evidence was available. Methods Our review was prospectively designed to define study objectives, search strategy, study selection criteria and methods for determining study eligibility, data elements to be abstracted and methods for abstraction, and methods for assessment of study quality. Two independent reviewers completed each step in this protocol and resolved disagreements by consensus. Disagreements were infrequent and were usually resolved by reconciliation of an oversight. When survival rates were estimated from figures in publications, disagreements were always less than 5% of the measured value, and the consensus estimate was the midpoint. All efficacy studies were randomized, controlled trials. Reviewers assessed the study quality dimensions that have been shown to be sources of bias (21): adequacy of randomization method, use of blinding and adequacy of concealment of allocation, and documentation of withdrawals and whether results were analyzed in an intention-to-treat fashion. Except for blinding and intention-to-treat analysis, published reports usually provided insufficient information to permit valid assessments of these quality dimensions. Therefore, studies that blinded patients and investigators to group assignment and used an intention-to-treat analysis of overall survival or progression-related outcomes were classified as higher-quality studies for sensitivity analysis. Blinding was considered not applicable when orchiectomy was one of the study arms. Literature Search and Study Selection We searched the MEDLINE, Cancerlit, EMBASE, and Cochrane Library databases from 1966 to March 1998 and Current Contents through 24 August 1998 for all articles that included at least one of the following terms in their titles, abstracts, or keyword lists: leuprolide (Lupron, TAP Pharmaceuticals Inc., Deerfield, Illinois), goserelin (Zoladex, Zeneca Pharmaceuticals, Wilmington, Delaware), buserelin (Suprefact, Hoechst Marion Roussel, Kansas City, Missouri), flutamide (Eulexin, Schering Corp., Kenilworth, New Jersey), nilutamide (Anandron, Roussel-Uclaf Laboratory, Romainville, France, and Nilandron, Hoechst Marion Roussel), bicalutamide (Casodex, Zeneca Pharmaceuticals, Wilmington, Delaware), cyproterone acetate (Androcur, Schering Corp.), diethylstilbestrol (DES), and orchiectomy (castration or orchidectomy). Search results were limited to studies on humans indexed under the Medical Subject Heading prostatic neoplasms. Randomized, controlled trials were identified by using the search strategy of the United Kingdom Cochrane Center (22). A total of 1477 references were retrieved and checked against the Cochrane Controlled Trials Register, the Cochrane Collaboration CENTRAL register, and trials cited in two recent meta-analyses. No additional trials were identified. Our study selection criteria limited reports of efficacy outcomes to randomized, controlled trials that compared 1) monotherapy with an LHRH agonist and monotherapy with orchiectomy or DES or 2) monotherapy with an antiandrogen and monotherapy with orchiectomy, DES, or an LHRH agonist. To facilitate comparison of results across trials that used different controls, studies that directly compared orchiectomy with DES were also included. Randomized, controlled trials that compared only different doses of the same agent were excluded. For adverse events, phase II studies that reported withdrawals from therapy were included. All studies reporting on quality of life were included. The patient population of interest was men with advanced prostate cancer, including regional or disseminated metastases (stage D1 or D2 disease [any T, N1 to N3, M0 or any T, any N, M1]) and minimally advanced disease (stage C disease [T3 or T4, N0 or NX, M0]). We also looked for outcomes that were analyzed by such patient prognostic factors as tumor grade, extent of disease, and performance status. Outcomes of interest were overall cancer-specific and progression-free survival, time to treatment failure, adverse effects, and quality of life. Where available, data on patient preferences were included. Adverse Events We encountered well-described difficulties (23, 24) in capturing infrequent events from small trials and inconsistencies among trials in measuring and reporting adverse events. Summarized here is the most reliable index of serious adverse events: the rate of withdrawal from therapy. A summary of adverse events by category (for example, cardiovascular, endocrine) is included in the full evidence report (20). Meta-Analysis We used the general approach to meta-analysis of trials in prostate cancer described by Caubet and colleagues (17), with additional guidance from Whitehead and Whitehead (25). To combine evidence from studies with several different treatment arms, it was necessary to go beyond standard meta-analysis techniques (26). The solution to the problem entails defining variables that describe the possible interventions. The poor survival rates for metastatic prostate cancer have implied a large value for the hazard rate (rate of death across time). We made the same assumption that is used in standard meta-analysisthat is, we assumed that the effect measure (hazard ratio in this case) remains constant across studies. Because several different treatments are now available, we assumed that all of the hazard ratios among the various treatments remain constant. The model is a generalization of the random-effects model described by DerSimonian and Laird (27). It is essentially the same model used by EGRET (28), except that it is applied to continuous outcomes instead of dichotomous outcomes. The model is a generalization that includes both fixed-effects and random-effects terms. The fixed-effects terms are the individual study intercepts. The random-effects terms are the slopes for the treatment effects. Estimates of all variables, including the extra variation, are obtained by maximum likelihood. On the basis of the preceding assumptions, our objective was to estimate the hazard rate for each arm of each study or to estimate the proportional hazards term and its standard error. We obtained estimates from other statistics for studies that did not provide this information directly. Caubet and colleagues (17) suggested a technique for estimating the log-hazard ratio from the chi-square value of the log-rank test. Where Kaplan-Meier curves were given, it was usually possible to estimate individual hazards, as described in the comprehensive evidence review (20). To use this meta-analysis method, we constructed a table of hazard rates for each arm of each study. The meta-analysis was done with software developed at the Duke Clinical Research Institute, Durham, North Carolina. Sensitivity analyses were used to test for heterogeneity of methods (including the effect of including studies of lower methodologic quality), participants, and interventions. An initial analysis determined whether the results of orchiectomy and DES were comparable and whether it was valid to pool studies in which the control groups used either of these monotherapies. Separate analyses also compared the available monotherapies and categories of monotherapies. All meta-analysis results were reported as hazard ratios relative to orchiectomy. Data Synthesis Overview of the Evidence Base The literature search identified 24 controlled trials that, collectively, randomly assigned more than 6600 patients to treatment with different monotherapies for

Survival following primary androgen deprivation therapy among men with localized prostate cancer.

CONTEXT Despite a lack of data, increasing numbers of patients are receiving primary androgen deprivation therapy (PADT) as an alternative to surgery, radiation, or conservative management for the treatment of localized prostate cancer. OBJECTIVE To evaluate the association between PADT and survival in elderly men with localized prostate cancer. DESIGN, SETTING, AND PATIENTS A population-based cohort study of 19,271 men aged 66 years or older receiving Medicare who did not receive definitive local therapy for clinical stage T1-T2 prostate cancer. These patients were diagnosed in 1992-2002 within predefined US geographical areas, with follow-up through December 31, 2006, for all-cause mortality and through December 31, 2004, for prostate cancer-specific mortality. Instrumental variable analysis was used to address potential biases associated with unmeasured confounding variables. MAIN OUTCOME MEASURES Prostate cancer-specific survival and overall survival. RESULTS Among patients with localized prostate cancer (median age, 77 years), 7867 (41%) received PADT, and 11,404 were treated with conservative management, not including PADT. During the follow-up period, there were 1560 prostate cancer deaths and 11,045 deaths from all causes. Primary androgen deprivation therapy was associated with lower 10-year prostate cancer-specific survival (80.1% vs 82.6%; hazard ratio [HR], 1.17; 95% confidence interval [CI], 1.03-1.33) and no increase in 10-year overall survival (30.2% vs 30.3%; HR, 1.00; 95% CI, 0.96-1.05) compared with conservative management. However, in a prespecified subset analysis, PADT use in men with poorly differentiated cancer was associated with improved prostate cancer-specific survival (59.8% vs 54.3%; HR, 0.84; 95% CI, 0.70-1.00; P = .049) but not overall survival (17.3% vs 15.3%; HR, 0.92; 95% CI, 0.84-1.01). CONCLUSION Primary androgen deprivation therapy is not associated with improved survival among the majority of elderly men with localized prostate cancer when compared with conservative management.

Estimating treatment benefits for the elderly: the effect of competing risks.

The current practice of encouraging patients to participate in treatment decisions requires that clinicians be facile in communicating the risks and benefits of therapy. Sharing numeric data can foster the process. However, because the format in which data are presented influences their interpretation [1-3], clinicians need to consider which format best describes the outcomes their patients face. Consider the tension between relative and absolute risk reduction. The interpretation of even a large relative risk reduction is highly dependent on the baseline risk for the specific disease. A 50% reduction in mortality with early intervention, for example, appears different when the risk for death from disease is changed from 2 per 1000 to 200 per 1000. When the mortality risk is low, the absolute survival benefit is small0.1% (2/1000 to 1/1000); when the risk is high, the absolute benefit is great10% (200/1000 to 100/1000). In the former scenario, patients might reasonably choose to forego a noxious intervention. In the latter, however, patients might be more likely to accept the morbidity of treatment. Because this distinction between relative and absolute risk reduction is concealed when benefit is expressed in only relative terms, many have argued that relative risk reductions should be anchored by baseline risk so that the absolute benefit of treatment is clear [2, 4, 5] However, an absolute measure of disease risk (or risk reduction from therapy) is not the ultimate outcome of interest to patients. Overall risk is more important. The difference is the risks patients face from other conditionsthat is, competing risks. When competing risks are great, they matter. The importance of even a 10% absolute survival benefit from treatment is markedly diminished for a patient who is at greater risk for death from other causes, regardless of the proposed therapy. Such great competing risks are most prevalent among the elderly. Although physicians intuitively understand the relevance of competing risks, they may be less sure about how to quantify the effect. We provide a framework to help physicians gauge the effect of competing risks in their elderly patients. Methods Overview To quantify the effect of competing risks, we used age-specific mortality data from U.S. vital statistics and the declining exponential approximation for life expectancy (DEALE) to model age-specific expectations for persons faced with a particular disease-related mortality. We sought to determine, for example, how a new disease with a 5-year mortality rate of 25% would affect the life expectancy of an average 70-year-old man. We then considered two refinements: the first, to better adjust for the individual patient (using self-reported health status), and the second, to describe more thoroughly the outcome (by including disabling events). Modeling the Effect of a New Disease on Life Expectancy Life expectancy and mortality are fundamentally related to probability estimates. In the general population, life expectancy decreases with increasing age, and annual mortality increases. Gompertz was the first to describe this complex mathematic relation using an exponential function that now bears his name. As life expectancy decreases, mortality rates become almost constant over time. When this occurs, the relation between survival and mortality rates can be approximated with a much simpler mathematic relation: a declining exponential function (the DEALE). This approximation was first validated and popularized by Beck and colleagues [6, 7] and is particularly suited to calculating the effect that a new risk has in older patients. The fundamental assumption behind this technique is that life expectancy equals the inverse of the annual mortality rate: Equation 1 Because mortality rates are essentially constant probability estimates when assessed over relatively short time horizons, patient-specific mortality rates can be expressed as the sum of the disease-independent mortality rate (also known as age-specific mortality rate) and a disease-related mortality rate (also known as case-fatality or excess mortality rate): Equation 2 Note that when disease-related mortality is zero (that is, when the patient does not have the disease or when the disease has no effect on survival), the patient-specific mortality rate (and thus life expectancy) is determined solely by the patients age. Calculation of the life expectancy estimates used in Figure 1 and Figure 2 is relatively simple. Because Figure 1 is the central portion of our paper, we now describe it in detail. Normal life expectancy (the top curves) was determined from the most recent data (1991) from the National Center for Health Statistics, U.S. Department of Health and Human Services [8]. On the basis of remaining life expectancy and the DEALE [6, 7], we calculated the age-specific mortality rate for each age cohort from 65 to 85 years of age. Combining the age-specific mortality with the hypothetical disease-related mortality allowed us to calculate the other four curves. The disease-related annual mortality rate can be calculated from 5-year disease-specific survival using the following equation: Equation 3 Figure 1. The effect of selected disease-related mortality rates on the remaining life expectancy of women (left) and men (right) at the time of diagnosis. Figure 2. The effect of age on the distribution of health states in the future. Thus, if the disease-related 5-year mortality rate is 25% (and the 5-year survival rate is 75%), then the disease-related annual mortality rate is 0.06. Equation 4 A 70-year-old man, for example, has a life expectancy of 12.2 years or an annual age-specific mortality rate of 0.08. Equation 5 Given the foregoing disease, the mans all-cause annual mortality rate is 0.14 (= 0.06 + 0.08), and his life expectancy is 7.2 years. Equation 6 Thus, the sum of the age-specific and disease-related mortality rates gives the patient-specific mortality rate, the inverse of which is life expectancy. Normal life expectancy serves as our proxy for disease-independent data. The mortality reflected in this measure is, of course, itself the result of several diseases in the elderlyprimarily cardiovascular disease and cancer. The method we describe produces a valid approximation whenever the disease in question is not a major contributor to the age-specific mortality rate. For example, if the disease in question was all cardiovascular disease or all cancer, then much of the age-specific mortality rate would already account for the mortality from the disease. Completely successful therapy for such a broad category of disease would move a patient well above his or her normal life expectancy by removing the common causes of death. Thus, the method we describe should be applied only when the physician is considering more discrete diagnoses (for example, aortic aneurysm or breast cancer), which make a relatively small contribution to overall mortality. To provide some quantitative data on how great a contributor to all-cause mortality a given disease can be without affecting our method, we did a sensitivity analysis that removed the contribution of a particular disease from normal life expectancy and accordingly revised the estimate of perfect treatment on life expectancy. For example, for a disease that accounts for 40% of all-cause mortality (such as all cardiovascular diagnoses), revised treatment benefit (in years) was three times the benefit estimated by our method. For a disease that accounts for 30% of all-cause mortality (for example, all cancers considered together), the revised benefit was twice as high as the benefit estimated by our method. However, for a disease that constitutes less than 10% of all-cause mortality (this is the case for any individual cancer), the revised benefit is small (for example, less than 20% higher than that estimated by our method). Adjustments for Health Status The adjustments for health status shown in Table 1 are based on data from the East Boston Senior Health Project. All participants were asked the following question: Compared with others your age, would you rate your overall health as excellent, good, fair, or poor? Analyzing the 1437 men and 2332 women separately, we used 5-year follow-up data to calculate, for each health status self-rating, the proportion of patients who died. The ratio of this health status-specific survival to overall survival served as our health status weight. A more precise analysis for men and women, using five age cohorts (ages 65 to 69 years, 70 to 74 years, 75 to 79 years, 80 to 84 years, and 85 years and older) produced essentially the same weights. Table 1. Estimated Physiologic Age of Elderly Patients Adjusted for Their Self-Reported Health Status* Overall, men who described themselves as in excellent health had a lower mortality rate than average (health status weight, 0.52). Men who reported themselves as in good, fair, and poor health had health status weights of 0.89, 1.26, and 1.88, respectively. The analysis for women showed health status weights of 0.64, 0.88, 1.08, and 1.82 for self-reported health status of excellent, good, fair, and poor, respectively. To approximate a physiologic age to reflect health status, we applied the health status weights to four chronologic ages: 65, 70, 75, and 80 years. Using the age-specific annual mortality from U.S. Vital Statistics data [8] and the health status weight, we calculated a health status-adjusted mortality rate as the following: Equation 7 We then returned to the Vital Statistics data to determine the age at which an average person would have this annual mortality rate. These data do not provide annual mortality rates for persons older than 85 years, forcing us to report 85 years and older for the highest mortality rates. The process was done separately for men and women. Future Disabling Events The expectation of future disabling events (Figure 3) is based on cros