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The Impact of Co-morbidity on Life Expectancy Among Men with Localized Prostate Cancer
0022-5347/96/1561-0127$03.00/0
WEJOURNAL OF UROLOGY Copyright 0 1996 by AMERICAN UROLOGICAL ASSOCIATION, INC.
Vol. 156, 127-132, July 1996 Printed in U.S.A.
THE IMPACT OF CO-MORBIDITY ON LIFE EXPECTANCY AMONG MEN WITH LOCALIZED PROSTATE CANCER PETER C. ALBERTSEN, DENNIS G. FRYBACK, BARRY E. STORER, THOMAS F. KOLON JUDITH FINE
AND
From the Division of Urology, Department of Surgery, University of Connecticut Health Center, Farmington and Yale Cancer Center, Yale University, New Haven, Connecticut, and Departments of Preventive Medicine and Biostatistics, Uniiwsity o f Wisconsin-Madison, Madison, Wisconsin
ABSTRACT
Purpose: We evaluated 3 indexes used to assess patient co-morbidities to determine whether t h e y could predict mortality among men with clinically localized prostate cancer. Materials and Methods: We measured the impact of co-morbidity classifications on all cause mortality using a parametric proportional hazards model based on a retrospective cohort analysis. Results: Each index tested is a highly significant predictor of mortality for patients dying of nonprostate cancer related causes after adjusting for age a n d Gleason score. Conclusions: Each co-morbidity index provides significant, independent predictive information concerning patient mortality beyond that provided by age, Gleason score and clinical stage alone. KEYWORDS:prostatic neoplasms, comorbidity, life expectancy
Considerable controversy surrounds the treatment of clinically localized prostate cancer,' stemming partly from the absence of randomized clinical trials that clearly document the benefit of 1 treatment modality over another. Estimates of treatment efficacy are frequently based on cause specific and all cause survival analyses of large case series.2~3Unfortunately, patient co-morbid conditions can affect these results, making comparisons among different treatment alternatives difficult. In a recent retrospective analysis of men 65 to 75 years old diagnosed with clinically localized prostate cancer in Connecticut during the early 1970s we demonstrated that tumor grade and co-morbid conditions impact on patient survival.4 We presently explore the contribution of co-morbidity to survival among men with localized prostate cancer. We evaluated 3 indexes, which had been previously validated elsewhere, to determine whether they could predict patient mortality more accurately than those based on patient age alone. The instruments tested were not designed for use in prostate cancer patients. The index of coexistent disease developed by Greenfield et a1 was designed to predict the functional status of patients following hospitalization and includes measures of the activities of daily living.5 It has been tested on patients with breast cancer and hip fractures. Charlson et a1 developed a co-morbidity scoring system for use in patients entering clinical trials.6 The index developed by Kaplan and Feinstein was designed to classify initial co-morbidity among patients with diabetes mellitus participating in a long-term outcome analysis.7 Rather than developing another instrument to assess patient co-morbidities, we elected to test whether these 3 systems could be used to stratify men with newly diagnosed, clinically localized prostate cancer into groups with distinctly different prognoses according to competing medical hazards. Any scheme used to quantify medical co-morbidities that impact on patient mortality from causes other than prostate cancer would certainly incorporate the same major conditions included in these indexes. On inspection, all 3 instruments had a high face validity for this purpose.
When deciding between different treatment recommendations, physicians currently estimate patient potential life expectancy by incorporating information based on patient age modified by a subjective estimate of the impact of comorbidity. We hope to improve the ability of researchers to control for co-morbid conditions when evaluating survival data from large series. METHODS
Cohort identification and data collection. To model the long-term impact of co-morbidities and prostate cancer on patient survival, we assembled a retrospective cohort of men 65 to 75 years old identified by the Connecticut Tumor Registry who were diagnosed with cancer putatively localized to the prostate between 1971 and 1976. Identifying information, hospital of diagnosis and treatment, and case disposition as of March 1993 were obtained directly from the Connecticut Tumor Registry. Information concerning patient co-morbidities was obtained by abstracting patient records at each of the 37 hospitals and 2 Veterans Affairs medical centers where the disease had been diagnosed. Only information available up to diagnosis was used to classify patients. All charts were abstracted with a standard form by study personnel blinded to the long-term outcome as recorded by the Connecticut Tumor Registry. Available original pathology slides were forwarded to a referee pathologist who was blinded to the case status to achieve standardized histological grading. Co-morbidity indexes tested. The Kaplan-Feinstein index identifies 12 categories of co-morbid illnesses: hypertension, cardiac, cerebral or psychic, respiratory, renal, hepatic, gastrointestinal, peripheral vascular, malignancy, locomotor, alcoholism, and a miscellaneous category encompassing uncontrolled systemic collagen disease, recurrent bleeding requiring transfusion and chronic active infections not included in this list.7 Each category has 4 grades of severity from 0 (none) to 3 (severe). A patient is evaluated by a chart reviewer blinded to case outcome and assigned grades for each of the 12 categories according to definitions specified by Accepted for publication January 26, 1996. .~ 2 or more co-morbidities are Supported by Grant HS06770 from the Agency for Health Care Kaplan and F e i n ~ t e i n When present, the patient is assigned an aggregate score of the Policy and Research, Rockville, Maryland. 127
128
CO-MORBIDITY OF PROSTATE CANCER
highest grade present except when there are 2 or more cat- of the Connecticut Tumor Registry and the National Cancer egories with grade 2, in which case the aggregate score is Institute Surveillance Epidemiology and End Results (SEER) methodology. Among these 991 individuals were 58 grade 3. Charlson et al introduced an index as a prospective method men whose prostate cancer was diagnosed at autopsy or for classifying co-morbid conditions that might alter the risk incidentally during cystectomy. These cases were excluded of mortality in longitudinal studies.6 They also defined cate- since they are not informative for clinicians wishing to know gories of diseases, including group 1-myocardial infarct, the natural history of the disease. Furthermore, we excluded congestive heart failure, peripheral vascular disease, cere- 111 patients who were treated with radical surgery or radibrovascular disease, dementia, chronic pulmonary disease, ation therapy so as not to add additional confounding variconnective tissue disease, ulcer disease, mild liver disease ables associated with treatment. Review of primary Source and diabetes, and group 2-hemiplegia, moderate to severe data failed to confirm the diagnosis of prostate cancer in 32 renal disease, diabetes with end organ damage, any tumor, cases, while 146 had clinically advanced disease by modern leukemia and lymphoma. Each disease in group 1 has a staging criteria (acid phosphatase level, bone scan or metaweight of 1 and each disease in group 2 has a weight of 2. static survey suggested metastatic disease, or obvious extraModerate or severe liver disease is weighted 3 and any met- capsular disease extension identified on review of original astatic solid tumor or the acquired immunodeficiency syn- pathology slides). These cases were excluded to ensure a drome is weighted 6. The patient record is evaluated for any population cohort relevant to modern decision making. Five of these conditions and the weights for all diseases are cases were excluded due to a diagnosis incidental to treatsummed to obtain the final index score. In the original de- ment of bladder cancer. Of the remaining 639 eligible cases velopment of this index Charlson et al classified patients into 188 (29%) were excluded because of incomplete or missing 4 groups with a summed score of 0, 1 , 2 and 3 or more, and charts: no patient record could be located in 159, treatment compared the result to the Kaplan-Feinstein index.6 They could not be determined in 3, neither a pathology report nor found a rough correspondence in mortality experience of pa- original pathology slide was available to confirm the diagnosis of prostate cancer in 19 and insufficient clinical informatients between the 2 indexes. The index of coexistent disease evaluated 14 medical con- tion was available to compute a co-morbidity index score in 7. Mean age a t diagnosis of the 451 men analyzed was 70.9 ditions, including organic heart disease, ischemic heart disease, primary arrhythmias, congestive heart failure, hyper- years. On a retrospective review of chart and pathology data tension, cerebrovascular accident, peripheral vascular the disease was stage A1 in 64 cases (14%), stage A2 in 108 disease, diabetes mellitus, respiratory problems, malignan- (24%) and stage B in 222 (49%) according to the Jewettcies, liver disease, renal disease, arthritis and gastrointesti- Whitmore classification scheme.8 The remaining 57 cases nal disease.5 Chart review is used to identify an occurrence of (13%) were classified simply as stage A, since pathological any medical condition in these categories, and each is effec- material was unavailable to permit further staging. Hortively graded into 4 severity levels using rules provided by monal treatment was initiated immediately for 202 men, the developers of this index: 0-no history or evidence of the while the remaining 249 received no therapy during the first condition, 1-asymptomatic or symptomatic controlled dis- 3 months after diagnosis. Cases excluded for missing information did not differ staease, 2-significant symptomatic controlled disease and 3-uncontrolled disease. Multiple diseases result in a final tistically in year of entry in the Connecticut Tumor Registry score equal to the severity of the worst single condition. If a or number of years between entry in the registry and death or patient has a score of 1 based on co-morbid conditions, a date of last contact. Men excluded for missing data were 0.54 physical impairment (defined by a list of symptoms and prob- years younger at entry into the registry than analyzed cases lems that must be documented in the chart) increases the (95%confidence interval -1.07 to -0.01, p = 0.045). Of the overall score to 2. excluded cases 8.5% were still alive at last contact compared Data elements. Data collected included the initial indica- to 8.9% of analyzed cases, and the difference was not signiftion for surgery that resulted in case identification (benign icant. Excluded cases were much more likely to have an prostatic hypertrophy or palpable lesion), the method by undetermined cause of death than did analyzed cases (84.6 which the cancer was diagnosed (transurethral resection, versus 8.0%). open prostatectomy, needle biopsy or other method), results Statistical analyses. To test the influence of co-morbidity of metastatic evaluation (acid phosphatase, bone scan, met- classifications on mortality we modeled survival using a paraastatic survey, lymph node dissection), if completed, and metric proportional hazards model based on the Gompertz initial treatment within 3 months of diagnosis. Co- distribution using all cause mortality data (table 1).From the morbidities were abstracted using the 3 indexes described. 1986 Connecticut abridged life tables reported by 5-year ages Only medical conditions other than the prostate cancer were for Connecticut men (provided by the Connecticut Departcoded. Each patient received a score of 0 to 3 according to the ment of Public Health and Addiction Services), we estabdefinitions developed by the index of coexistent disease and lished a reference background hazard rate for all cause male the Kaplan-Feinstein index using the procedures of the index mortality. Univariate analyses examined the contributions of developer. Patients were also assembled into 4 groups additional variables considered 1at a time above and beyond using the Charlson et a1 index by summing the index the baseline hazard function to predict survival. For multiweighted values for diseases: score 0-weighted sum 0, score variate analyses the baseline hazard function was used to 1-weighted sum 1 or 2, score 2-weighted sum 3 or 4 and account for age in a stepwise forward selection procedure score 3-weighted sum 5 or more. The Connecticut Tumor Registry provided the patient birth date and date of diagnosis (both confirmed during chart TABLE1. Values for A in the Gompertz model abstraction), date of last contact for followup, and date and cause of death (from death certificates) for deceased patients. Gleason Grade We recorded cause of death as secondary to prostate cancer if 2-4 5-1 8-10 1of 3 antecedent causes of death listed on the death certifiUnivariate model: -9.159 -8.510 -7.926 cate mentioned prostate cancer. Multivariate model (ICED): Exclusions. The Connecticut Tumor Registry identified 991 -~ -8.681 -8.082 0 -9.446 men meeting our entry criteria who also represent the entire -8.420 -7.820 1 -9.1845 population of incident cases of putatively localized prostate -1.784 -7.185 2 -8.411 3 -7.063 -6.298 -5.699 cancer in Connecticut fitting the age and date requirements
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CO-MORBIDITY OF PROSTATE CANCER
using a proportional hazards model to examine the contribu- is no standard method of adjusting plotted curves of cause tions of other independent variables, including the co- specific cumulative mortality for age at diagnosis and Gleason morbidity scores. Details of this analysis and other results score of the tumor, we provided cumulative plots of raw mortality data for qualitative purposes only. All cause cumuwere reported previously.4 Cause specific mortality rates were modeled by censoring lative mortality can be partitioned for display into nonprosfollowup beyond the time of death for men dying of causes tate cancer mortality-prostate cancer not listed on the other than prostate cancer. Cause specific cumulative mor- death certificate as a contributing cause, prostate cancer tality, stratified by co-morbidity index, was estimated and mortality-prostate cancer listed as 1 of 3 contributing plotted as described by Kalbfleisch and Prentice.9 Estimates causes and death of unknown cause. The latter category is of life expectancy presented in table 2 were derived using the not displayed because of small sample size (36). bivariate equation documented in the Appendix. The equaFigure 1 shows the cumulative mortality from all causes tion was used to compute survival probabilities in half year (including unknown) of death for the 411 men who died by intervals up to current age plus 30 years. The computed the end of followup as a function of the severity of cosurvival probability for each of these 60 half year intervals morbidities at diagnosis according to the 3 indexes tested. was multiplied by 0.5 years and these products were summed Men with no co-morbidities a t diagnosis demonstrated an to obtain an approximate age-specific life expectancy. This apparent survival advantage over those with minimal, modprocess was repeated for ages of 65, 70 and 75 years a t erate and severe co-morbidities. Only 62 to 68% of men with diagnosis. The model presumes a baseline survival repre- no co-morbidities at diagnosis died within 10 years of diagsented by the 1986 Connecticut life tables and the demo- nosis (that is before ages 75 to 85 years given the study entry graphics of our sample of men. criteria) depending upon which index was used. Men with RESULTS
After a mean followup of 15.5 years 40 (9%)men were alive a t last contact, 154 (34%)died of prostate cancer, 221 (49%) died of other causes and 36 (8%)died of unknown causes. Of the 40 living men the median time from diagnosis to last contact was 18 years. Only 7 patients were lost to followup within 10 years. In univariate analyses of all cause mortality data Gleason score of the prostate cancer was the best single independent predictor of age-adjusted survival when grouped into 4 categories, including scores of 2 to 4, 5 to 7 and 8 to 10 and pathology insufficient to score (chi-square 50.8 with 3 degrees of freedom, p <0.0001). We described this result in detail previously.4 In analyses using co-morbidity score to predict age-adjusted survival the index of coexistent disease was the most predictive of the 3 indexes (chi-square 35.0, 3 degrees of freedom, p
20
0
Years since diagnosis"
TABLE2 . Estimated life expectancy (years) by age and index of coexistent disease co-morbidity score Index of Coexistent Disease &-Morbidity Score
65
70
75
0 1 2 3 Overall
17.9 15.9 10.8 4.0 15.7
14.8 12.9 8.4 2.8 12.7
11.9 10.1 6.3 1.9 10.0
Age at Diagnosis (yrs.) 0
Years since diagnosis"
20
FIG. 1. Cumulative mortality from all causes of death stratified by seventy of co-morbidities at diagnosis as measured by each of 3 instruments tested. Data are not adjusted for patient age or Gleason sum. ICED, index of coexistent disease.
130
CO-MORBIDITY OF PROSTATE CANCER
minimal or moderate co-morbid conditions had similar cumulative mortality curves for a 70 to 80% death rate 10 years after diagnosis. As expected, men with severe co-morbidities had a high cumulative mortality, although sample size was small in this category with all 3 indexes. All men with severe co-morbidities according to the index of coexistent disease or Charlson et d system and more than 90% with severe comorbidities according to the Kaplan-Feinstein system were dead within 10 years. Figure 2 shows cumulative mortality for the 221 men who died of causes other than prostate cancer stratified by comorbidity score. Again, men with the lowest co-morbidity scores had a lower cumulative mortality rate compared to men with minimal, moderate or severe co-morbidities. Each of the 3 co-morbidity indexes is a highly significant predictor of mortality from causes other than prostate cancer after adjustment for age (p <0.0001). Figure 3 shows cumulative mortality stratified by comorbidity score for the 154 men who died of prostate cancer. All men were treated conservatively for the disease and none
“1
‘r O
Years s&e diagnosis”
M
a, ? U i
0
0
i
Years sinte diagnosi;’
Years sin$e diagnosis”
20
20
Years s i d e diagnosis”
20
FIG. 3. Cumulative mortality from prostate cancer stratified by severity of co-morbidity at diagnosis as measured by 3 instruments tested. Data are not adjusted for patient age or Gleason sum. ICED, index of coexistent disease.
Years sin& diagnosi;’
20
received either radiation therapy or underwent radical surgery. Although not apparent in these unadjusted plots, comorbidity scores were still significant predictors of prostate cancer related deaths after adjustment for age and Gleason score but with considerably weaker contributions than for the nonprostate cancer deaths (p = 0.01, 0.15, and 0.05 for index of coexistent disease, Charlson et al, and Kaplan2 Feinstein indexes, respectively). Table 2 presents estimates of life expectancy by index of coexistent disease scores for men 65 to 75 years old. The table was derived from survival data for men with Gleason score 2 to 4 tumors using the bivariate equation outlined in the Appendix. These men were shown previously to have a survival curve identical to that of the general population of men alive during the same era.4 Estimates for a n index of coexis20 0 Years si&e diagnosis” tent disease of 3 should be used with caution because the FIG.2. Cumulative mortality from causes other than prostate model was derived from a small number of cases with this cancer stratifiedby seventy of co-morbidityat diagnosis as measured degree of co-morbidity. Since life expectancy greater than 10 by 3 instruments tested. Data are not adjusted for patient age or years is frequently cited as a guideline for recommending Gleason sum. ICED, index of coexistent disease. aggressive intervention, the table should assist clinicians 0
7 - - - - - -
I .
131
CO-MORBIDITY OF PROSTATE CANCER
who advise patients age 65 to 75 years old at diagnosis concerning appropriate treatment options for newly diagnosed, localized prostate cancer.2 DISCUSSION
Competing disease hazards can have a significant role when analyzing results of prior series in which patient selection for treatment differs according to how ill the patients appear. However, physicians reporting case series data rarely account for the impact of co-morbid disease.lo The resulting selection bias can have a significant impact on case series survival data. Several instruments have been developed to assist physicians in classifying the extent of co-morbid disease. We tested 3 indexes in a retrospective cohort of men with clinically localized prostate cancer to determine whether any could be used to adjust for the impact of co-morbid disease. Our findings demonstrate that all 3 instruments provide additional information concerning cumulative mortality among men with localized prostate cancer after controlling for age and Gleason score. Men 65 to 75 years old with untreated localized prostate cancer had significantly different 10-year mortality rates (all cause, or nonprostate cancer or prostate cancer associated mortalities) depending on the number and severity of associated co-morbid conditions. This finding has important implications for case series data. Patients undergoing aggressive treatment interventions, such as radical prostatectomy and possibly brachytherapy or cryosurgery, are likely to have fewer co-morbid conditions compared to men undergoing radiation therapy or conservative management with androgen deprivation. As a consequence, data reporting patient outcomes following aggressive management using all cause survival curves are likely to be biased in favor of greater survival rates compared to similar reports of outcomes following radiation therapy or conservative management. Physicians must review these results with caution, especially when making comparisons among alternative treatment strategies. Our results show that even restricting analysis to mortality associated with prostate cancer does not free researchers from controlling case series analyses for co-morbid conditions. Whether differences in mortality associated with COmorbid disease result from differences in tumor biology or simply from errors in determining the true cause of death on death certificates cannot be determined from our study. Unfortunately, this problem confronts all researchers reporting disease specific survival data. Although prostate cancer specific mortality may interest the researcher, patients and clinicians often rely on all cause mortality estimates when making decisions about treatment. Reporting results stratified by co-morbidity will assist clinicians and patients to determine which aggregate results are most applicable to prognosis. All 3 co-morbidity classification systems we tested are acceptable for stratifying results of case series. The index of coexistent disease is marginally more contributory in the all cause proportional hazards model for survival, analysis of mortality rate due to nonprostate causes of death, and predicting prostate cancer related mortality after controlling for age and Gleason score. Physicians who use any of these instruments will find them easy to administer especially in a Prospective setting. All 3 indexes inventory specific medical conditions according to definitions supplied by the develope r ~ . The ~ - ~Charlson et a1 and Kaplan-Feinstein indexes are scored as previously outlined, while the index of coexistent disease uses a key that incorporates activities of daily living. Regardless of the system used, we urge clinicians and researchers to report outcomes of series for treatment of prostate cancer stratified by Gleason grade and co-morbidities.
CONCLUSIONS
Controversy surrounding the treatment of localized prostate cancer frequently stems from differing estimates of treatment efficacy, which are often based on cause specific and all cause survival analyses of large case series. Unfortunately, other factors, such as co-morbid conditions, can affect these results and make comparisons among different treatment alternatives difficult. To determine whether any of 3 previously validated instruments used to evaluate comorbidity could provide more accurate predictions of patient mortality than those based on age alone, we constructed survival curves using a parametric proportional hazards model. With data from a retrospective population based cohort of men 65 to 75 years old diagnosed with clinically localized prostate cancer in Connecticut during the 1970s, we found that Gleason score and co-morbidity indexes were highly significant predictors of mortality (p
The expression used to compute the probability that a man y years old a t diagnosis will survive to age t years (t > y) is:
where e is the natural logarithm base. In many popular computer spreadsheet programs the computation involved in raising e to a power is done with a mathematical function denoted @exp( 1, so this computation can be accomplished easily with access to a microcomputer and spreadsheet software. This computation also can be done on a hand held scientific calculator.
132
CO-MORBIDITY OF F'ROSTATE CANCER
new method of classifying prognostic co-morbidity in longituThe coefficient B is fixed as a constant with the value dinal studies: development and validation. J . Chron. Dis., 40: 0.08169. To compute survival probabilities implied by the 373, 1987. 1986 Connecticut life tables, the remaining constant is given 7. Kaplan, M. H. and Feinstein, A. R.: The importance of classifying by A = -9.121. To compute survival probabilities underlying initial co-morbidity in evaluating the outcomes of diabetes results in our article values for the constant A are given in mellitus. J . Chron. Dis., 27: 387, 1974. table 2. 8. Catalona, W. J. and Whitmore, W. F., Jr.: New staging systems Use of this computational formula should be restricted to for prostate cancer. J . Urol., 142: 1302, 1989. initial ages, y, of 65 to 75 years and t not to exceed age 95 9. Kalbfleisch, J . D. and Prentice, R. L.: The Statistical Analysis of years. Extrapolation beyond these ranges goes beyond the Failure Time Data. New York John Wiley & Sons, 1980. data presented in our study. Additionally, it should be re- 10. Wasson, J. H., Cushman, C. C., Bruskewitz, R. C., Littenberg, B., membered that this formula computes a lower bound on the Mulley, A. G., J r . and Wennberg, J. E.: A structured literature survival probability for men diagnosed today because of the review of treatment for localized prostate cancer. Prostate Disease Patient Outcome Research Team. Arch. Fam. Med., 2 biases and empirical choices discussed previously.4 487, 1993. An example calculation of the estimated lower bound 10year survival probability for a man 70 years old at diagnosis EDITORIAL COMMENT with a Gleason grade 5 to 7 clinically localized tumor, who would be classified with an index of coexistent disease of 1 for This article has important implications for investigators and clico-morbidities is given by: nicians. Many researchers are studying the outcomes of different e{-l/0,08169.
e-8420. (e008169(801 - e0081169(70)))
(2)
which reduces further to e1-12,241.e-B"20.(e65362-e5.11"
)I
(3)
and then to e- .002698(364.58454)
(4)
and finally to 0.354. Age-specific survival curves are generated by varying t in small increments and repeating this calculation, then plotting the computed survival probabilities against the correspondingvalues oft. Age-specificlife expectancy is computed by numerical integration of the survival curve. REFERENCES
1. Albertsen, P.: A 72-year-old man with localized prostate cancer.
J.A.M.A., 2 7 4 69, 1995. 2. Brendler, C. B. and Walsh, P. C.: The role of radical prostatectomy in the treatment of prostate cancer. CA, 42:212, 1992. 3. Bagshaw, M. A., Cox, R. S. and Hancock, S. L.: Control of prostate cancer with radiation therapy: long-term results. J. Urol., part 2 , 1 5 2 1781, 1994. 4. Albertsen, P. C., Fryback, D. G., Storer, B. E., Kolon, T. F. and Fine, J.: Long-term survival among men with conservatively treated localized prostate cancer. J.A.M.A., 274: 626, 1995. 5. Greenfield, S., Apolone, G., McNeil, B. J. and Cleary, P. D.: The importance of co-existent disease in the occurrence of postoperative complications and one-year recovery in patients undergoing total hip replacement. Comorbidity and outcomes after hip replacement. Med. Care., 31: 141, 1993. 6. Charlson, M. E., Pompie, P., Ales, K. L. and MacKenzie, C. R.: A
treatments for men with clinically localized prostate cancer, which is a subject of considerable controversy. Because of the long doubling times of most such cancers, many retrospective, nonrandomized studies are being conducted to define optimal treatment strategies for M e r e n t patient subgroups pending definitive results of prospective randomized trials, which may be 10 years or longer away. Patient age and tumor factors, such as Gleason grade and pathological stage, are accepted as important potential confounding factors in these analyses. The authors clearly establish that patient comorbidity can bias such studies as well, and provide a n approach to measure and control for this factor. Such analytical methods are particularly important when the focus is on overall mortality, which is an important outcome given the known inaccuracies in determinations of cause of death in retrospective studies. This inaccuracy may have been reflected in the finding that co-morbidity was a sigdicant, although weaker, predictor of cancer specific mortality in this study. Clinicians often hear that aggressive early detection and treatment efforts for prostate cancer are best reserved for men with a 10-year life expectancy. This point seems to be 1of few on which most authorities agree, regardless of which side they take on the general issue of the net benefits of prostate cancer screening and treatment. However, how does a clinician decide which patient is likely to live 10 years considering not only age but also other medical problems? Table 1indicates that while men with average co-morbidity would be estimated to have a 10-year life expectancy at age 75 years, those with an index of coexisting disease score of 2 would have a similar life expectancy at age 65 years. This information, although based on 1986 life tables for 1state, may help practitioners in the trenches be more accurate in deciding who is likely to benefit from aggressive screening and treatment efforts. Michael J . Barry Medical Practices Evaluation Center Massachusetts General Hospital Boston, Massachusetts
WEJOURNAL OF UROLOGY Copyright 0 1996 by AMERICAN UROLOGICAL ASSOCIATION, INC.
Vol. 156, 127-132, July 1996 Printed in U.S.A.
THE IMPACT OF CO-MORBIDITY ON LIFE EXPECTANCY AMONG MEN WITH LOCALIZED PROSTATE CANCER PETER C. ALBERTSEN, DENNIS G. FRYBACK, BARRY E. STORER, THOMAS F. KOLON JUDITH FINE
AND
From the Division of Urology, Department of Surgery, University of Connecticut Health Center, Farmington and Yale Cancer Center, Yale University, New Haven, Connecticut, and Departments of Preventive Medicine and Biostatistics, Uniiwsity o f Wisconsin-Madison, Madison, Wisconsin
ABSTRACT
Purpose: We evaluated 3 indexes used to assess patient co-morbidities to determine whether t h e y could predict mortality among men with clinically localized prostate cancer. Materials and Methods: We measured the impact of co-morbidity classifications on all cause mortality using a parametric proportional hazards model based on a retrospective cohort analysis. Results: Each index tested is a highly significant predictor of mortality for patients dying of nonprostate cancer related causes after adjusting for age a n d Gleason score. Conclusions: Each co-morbidity index provides significant, independent predictive information concerning patient mortality beyond that provided by age, Gleason score and clinical stage alone. KEYWORDS:prostatic neoplasms, comorbidity, life expectancy
Considerable controversy surrounds the treatment of clinically localized prostate cancer,' stemming partly from the absence of randomized clinical trials that clearly document the benefit of 1 treatment modality over another. Estimates of treatment efficacy are frequently based on cause specific and all cause survival analyses of large case series.2~3Unfortunately, patient co-morbid conditions can affect these results, making comparisons among different treatment alternatives difficult. In a recent retrospective analysis of men 65 to 75 years old diagnosed with clinically localized prostate cancer in Connecticut during the early 1970s we demonstrated that tumor grade and co-morbid conditions impact on patient survival.4 We presently explore the contribution of co-morbidity to survival among men with localized prostate cancer. We evaluated 3 indexes, which had been previously validated elsewhere, to determine whether they could predict patient mortality more accurately than those based on patient age alone. The instruments tested were not designed for use in prostate cancer patients. The index of coexistent disease developed by Greenfield et a1 was designed to predict the functional status of patients following hospitalization and includes measures of the activities of daily living.5 It has been tested on patients with breast cancer and hip fractures. Charlson et a1 developed a co-morbidity scoring system for use in patients entering clinical trials.6 The index developed by Kaplan and Feinstein was designed to classify initial co-morbidity among patients with diabetes mellitus participating in a long-term outcome analysis.7 Rather than developing another instrument to assess patient co-morbidities, we elected to test whether these 3 systems could be used to stratify men with newly diagnosed, clinically localized prostate cancer into groups with distinctly different prognoses according to competing medical hazards. Any scheme used to quantify medical co-morbidities that impact on patient mortality from causes other than prostate cancer would certainly incorporate the same major conditions included in these indexes. On inspection, all 3 instruments had a high face validity for this purpose.
When deciding between different treatment recommendations, physicians currently estimate patient potential life expectancy by incorporating information based on patient age modified by a subjective estimate of the impact of comorbidity. We hope to improve the ability of researchers to control for co-morbid conditions when evaluating survival data from large series. METHODS
Cohort identification and data collection. To model the long-term impact of co-morbidities and prostate cancer on patient survival, we assembled a retrospective cohort of men 65 to 75 years old identified by the Connecticut Tumor Registry who were diagnosed with cancer putatively localized to the prostate between 1971 and 1976. Identifying information, hospital of diagnosis and treatment, and case disposition as of March 1993 were obtained directly from the Connecticut Tumor Registry. Information concerning patient co-morbidities was obtained by abstracting patient records at each of the 37 hospitals and 2 Veterans Affairs medical centers where the disease had been diagnosed. Only information available up to diagnosis was used to classify patients. All charts were abstracted with a standard form by study personnel blinded to the long-term outcome as recorded by the Connecticut Tumor Registry. Available original pathology slides were forwarded to a referee pathologist who was blinded to the case status to achieve standardized histological grading. Co-morbidity indexes tested. The Kaplan-Feinstein index identifies 12 categories of co-morbid illnesses: hypertension, cardiac, cerebral or psychic, respiratory, renal, hepatic, gastrointestinal, peripheral vascular, malignancy, locomotor, alcoholism, and a miscellaneous category encompassing uncontrolled systemic collagen disease, recurrent bleeding requiring transfusion and chronic active infections not included in this list.7 Each category has 4 grades of severity from 0 (none) to 3 (severe). A patient is evaluated by a chart reviewer blinded to case outcome and assigned grades for each of the 12 categories according to definitions specified by Accepted for publication January 26, 1996. .~ 2 or more co-morbidities are Supported by Grant HS06770 from the Agency for Health Care Kaplan and F e i n ~ t e i n When present, the patient is assigned an aggregate score of the Policy and Research, Rockville, Maryland. 127
128
CO-MORBIDITY OF PROSTATE CANCER
highest grade present except when there are 2 or more cat- of the Connecticut Tumor Registry and the National Cancer egories with grade 2, in which case the aggregate score is Institute Surveillance Epidemiology and End Results (SEER) methodology. Among these 991 individuals were 58 grade 3. Charlson et al introduced an index as a prospective method men whose prostate cancer was diagnosed at autopsy or for classifying co-morbid conditions that might alter the risk incidentally during cystectomy. These cases were excluded of mortality in longitudinal studies.6 They also defined cate- since they are not informative for clinicians wishing to know gories of diseases, including group 1-myocardial infarct, the natural history of the disease. Furthermore, we excluded congestive heart failure, peripheral vascular disease, cere- 111 patients who were treated with radical surgery or radibrovascular disease, dementia, chronic pulmonary disease, ation therapy so as not to add additional confounding variconnective tissue disease, ulcer disease, mild liver disease ables associated with treatment. Review of primary Source and diabetes, and group 2-hemiplegia, moderate to severe data failed to confirm the diagnosis of prostate cancer in 32 renal disease, diabetes with end organ damage, any tumor, cases, while 146 had clinically advanced disease by modern leukemia and lymphoma. Each disease in group 1 has a staging criteria (acid phosphatase level, bone scan or metaweight of 1 and each disease in group 2 has a weight of 2. static survey suggested metastatic disease, or obvious extraModerate or severe liver disease is weighted 3 and any met- capsular disease extension identified on review of original astatic solid tumor or the acquired immunodeficiency syn- pathology slides). These cases were excluded to ensure a drome is weighted 6. The patient record is evaluated for any population cohort relevant to modern decision making. Five of these conditions and the weights for all diseases are cases were excluded due to a diagnosis incidental to treatsummed to obtain the final index score. In the original de- ment of bladder cancer. Of the remaining 639 eligible cases velopment of this index Charlson et al classified patients into 188 (29%) were excluded because of incomplete or missing 4 groups with a summed score of 0, 1 , 2 and 3 or more, and charts: no patient record could be located in 159, treatment compared the result to the Kaplan-Feinstein index.6 They could not be determined in 3, neither a pathology report nor found a rough correspondence in mortality experience of pa- original pathology slide was available to confirm the diagnosis of prostate cancer in 19 and insufficient clinical informatients between the 2 indexes. The index of coexistent disease evaluated 14 medical con- tion was available to compute a co-morbidity index score in 7. Mean age a t diagnosis of the 451 men analyzed was 70.9 ditions, including organic heart disease, ischemic heart disease, primary arrhythmias, congestive heart failure, hyper- years. On a retrospective review of chart and pathology data tension, cerebrovascular accident, peripheral vascular the disease was stage A1 in 64 cases (14%), stage A2 in 108 disease, diabetes mellitus, respiratory problems, malignan- (24%) and stage B in 222 (49%) according to the Jewettcies, liver disease, renal disease, arthritis and gastrointesti- Whitmore classification scheme.8 The remaining 57 cases nal disease.5 Chart review is used to identify an occurrence of (13%) were classified simply as stage A, since pathological any medical condition in these categories, and each is effec- material was unavailable to permit further staging. Hortively graded into 4 severity levels using rules provided by monal treatment was initiated immediately for 202 men, the developers of this index: 0-no history or evidence of the while the remaining 249 received no therapy during the first condition, 1-asymptomatic or symptomatic controlled dis- 3 months after diagnosis. Cases excluded for missing information did not differ staease, 2-significant symptomatic controlled disease and 3-uncontrolled disease. Multiple diseases result in a final tistically in year of entry in the Connecticut Tumor Registry score equal to the severity of the worst single condition. If a or number of years between entry in the registry and death or patient has a score of 1 based on co-morbid conditions, a date of last contact. Men excluded for missing data were 0.54 physical impairment (defined by a list of symptoms and prob- years younger at entry into the registry than analyzed cases lems that must be documented in the chart) increases the (95%confidence interval -1.07 to -0.01, p = 0.045). Of the overall score to 2. excluded cases 8.5% were still alive at last contact compared Data elements. Data collected included the initial indica- to 8.9% of analyzed cases, and the difference was not signiftion for surgery that resulted in case identification (benign icant. Excluded cases were much more likely to have an prostatic hypertrophy or palpable lesion), the method by undetermined cause of death than did analyzed cases (84.6 which the cancer was diagnosed (transurethral resection, versus 8.0%). open prostatectomy, needle biopsy or other method), results Statistical analyses. To test the influence of co-morbidity of metastatic evaluation (acid phosphatase, bone scan, met- classifications on mortality we modeled survival using a paraastatic survey, lymph node dissection), if completed, and metric proportional hazards model based on the Gompertz initial treatment within 3 months of diagnosis. Co- distribution using all cause mortality data (table 1).From the morbidities were abstracted using the 3 indexes described. 1986 Connecticut abridged life tables reported by 5-year ages Only medical conditions other than the prostate cancer were for Connecticut men (provided by the Connecticut Departcoded. Each patient received a score of 0 to 3 according to the ment of Public Health and Addiction Services), we estabdefinitions developed by the index of coexistent disease and lished a reference background hazard rate for all cause male the Kaplan-Feinstein index using the procedures of the index mortality. Univariate analyses examined the contributions of developer. Patients were also assembled into 4 groups additional variables considered 1at a time above and beyond using the Charlson et a1 index by summing the index the baseline hazard function to predict survival. For multiweighted values for diseases: score 0-weighted sum 0, score variate analyses the baseline hazard function was used to 1-weighted sum 1 or 2, score 2-weighted sum 3 or 4 and account for age in a stepwise forward selection procedure score 3-weighted sum 5 or more. The Connecticut Tumor Registry provided the patient birth date and date of diagnosis (both confirmed during chart TABLE1. Values for A in the Gompertz model abstraction), date of last contact for followup, and date and cause of death (from death certificates) for deceased patients. Gleason Grade We recorded cause of death as secondary to prostate cancer if 2-4 5-1 8-10 1of 3 antecedent causes of death listed on the death certifiUnivariate model: -9.159 -8.510 -7.926 cate mentioned prostate cancer. Multivariate model (ICED): Exclusions. The Connecticut Tumor Registry identified 991 -~ -8.681 -8.082 0 -9.446 men meeting our entry criteria who also represent the entire -8.420 -7.820 1 -9.1845 population of incident cases of putatively localized prostate -1.784 -7.185 2 -8.411 3 -7.063 -6.298 -5.699 cancer in Connecticut fitting the age and date requirements
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CO-MORBIDITY OF PROSTATE CANCER
using a proportional hazards model to examine the contribu- is no standard method of adjusting plotted curves of cause tions of other independent variables, including the co- specific cumulative mortality for age at diagnosis and Gleason morbidity scores. Details of this analysis and other results score of the tumor, we provided cumulative plots of raw mortality data for qualitative purposes only. All cause cumuwere reported previously.4 Cause specific mortality rates were modeled by censoring lative mortality can be partitioned for display into nonprosfollowup beyond the time of death for men dying of causes tate cancer mortality-prostate cancer not listed on the other than prostate cancer. Cause specific cumulative mor- death certificate as a contributing cause, prostate cancer tality, stratified by co-morbidity index, was estimated and mortality-prostate cancer listed as 1 of 3 contributing plotted as described by Kalbfleisch and Prentice.9 Estimates causes and death of unknown cause. The latter category is of life expectancy presented in table 2 were derived using the not displayed because of small sample size (36). bivariate equation documented in the Appendix. The equaFigure 1 shows the cumulative mortality from all causes tion was used to compute survival probabilities in half year (including unknown) of death for the 411 men who died by intervals up to current age plus 30 years. The computed the end of followup as a function of the severity of cosurvival probability for each of these 60 half year intervals morbidities at diagnosis according to the 3 indexes tested. was multiplied by 0.5 years and these products were summed Men with no co-morbidities a t diagnosis demonstrated an to obtain an approximate age-specific life expectancy. This apparent survival advantage over those with minimal, modprocess was repeated for ages of 65, 70 and 75 years a t erate and severe co-morbidities. Only 62 to 68% of men with diagnosis. The model presumes a baseline survival repre- no co-morbidities at diagnosis died within 10 years of diagsented by the 1986 Connecticut life tables and the demo- nosis (that is before ages 75 to 85 years given the study entry graphics of our sample of men. criteria) depending upon which index was used. Men with RESULTS
After a mean followup of 15.5 years 40 (9%)men were alive a t last contact, 154 (34%)died of prostate cancer, 221 (49%) died of other causes and 36 (8%)died of unknown causes. Of the 40 living men the median time from diagnosis to last contact was 18 years. Only 7 patients were lost to followup within 10 years. In univariate analyses of all cause mortality data Gleason score of the prostate cancer was the best single independent predictor of age-adjusted survival when grouped into 4 categories, including scores of 2 to 4, 5 to 7 and 8 to 10 and pathology insufficient to score (chi-square 50.8 with 3 degrees of freedom, p <0.0001). We described this result in detail previously.4 In analyses using co-morbidity score to predict age-adjusted survival the index of coexistent disease was the most predictive of the 3 indexes (chi-square 35.0, 3 degrees of freedom, p
20
0
Years since diagnosis"
TABLE2 . Estimated life expectancy (years) by age and index of coexistent disease co-morbidity score Index of Coexistent Disease &-Morbidity Score
65
70
75
0 1 2 3 Overall
17.9 15.9 10.8 4.0 15.7
14.8 12.9 8.4 2.8 12.7
11.9 10.1 6.3 1.9 10.0
Age at Diagnosis (yrs.) 0
Years since diagnosis"
20
FIG. 1. Cumulative mortality from all causes of death stratified by seventy of co-morbidities at diagnosis as measured by each of 3 instruments tested. Data are not adjusted for patient age or Gleason sum. ICED, index of coexistent disease.
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CO-MORBIDITY OF PROSTATE CANCER
minimal or moderate co-morbid conditions had similar cumulative mortality curves for a 70 to 80% death rate 10 years after diagnosis. As expected, men with severe co-morbidities had a high cumulative mortality, although sample size was small in this category with all 3 indexes. All men with severe co-morbidities according to the index of coexistent disease or Charlson et d system and more than 90% with severe comorbidities according to the Kaplan-Feinstein system were dead within 10 years. Figure 2 shows cumulative mortality for the 221 men who died of causes other than prostate cancer stratified by comorbidity score. Again, men with the lowest co-morbidity scores had a lower cumulative mortality rate compared to men with minimal, moderate or severe co-morbidities. Each of the 3 co-morbidity indexes is a highly significant predictor of mortality from causes other than prostate cancer after adjustment for age (p <0.0001). Figure 3 shows cumulative mortality stratified by comorbidity score for the 154 men who died of prostate cancer. All men were treated conservatively for the disease and none
“1
‘r O
Years s&e diagnosis”
M
a, ? U i
0
0
i
Years sinte diagnosi;’
Years sin$e diagnosis”
20
20
Years s i d e diagnosis”
20
FIG. 3. Cumulative mortality from prostate cancer stratified by severity of co-morbidity at diagnosis as measured by 3 instruments tested. Data are not adjusted for patient age or Gleason sum. ICED, index of coexistent disease.
Years sin& diagnosi;’
20
received either radiation therapy or underwent radical surgery. Although not apparent in these unadjusted plots, comorbidity scores were still significant predictors of prostate cancer related deaths after adjustment for age and Gleason score but with considerably weaker contributions than for the nonprostate cancer deaths (p = 0.01, 0.15, and 0.05 for index of coexistent disease, Charlson et al, and Kaplan2 Feinstein indexes, respectively). Table 2 presents estimates of life expectancy by index of coexistent disease scores for men 65 to 75 years old. The table was derived from survival data for men with Gleason score 2 to 4 tumors using the bivariate equation outlined in the Appendix. These men were shown previously to have a survival curve identical to that of the general population of men alive during the same era.4 Estimates for a n index of coexis20 0 Years si&e diagnosis” tent disease of 3 should be used with caution because the FIG.2. Cumulative mortality from causes other than prostate model was derived from a small number of cases with this cancer stratifiedby seventy of co-morbidityat diagnosis as measured degree of co-morbidity. Since life expectancy greater than 10 by 3 instruments tested. Data are not adjusted for patient age or years is frequently cited as a guideline for recommending Gleason sum. ICED, index of coexistent disease. aggressive intervention, the table should assist clinicians 0
7 - - - - - -
I .
131
CO-MORBIDITY OF PROSTATE CANCER
who advise patients age 65 to 75 years old at diagnosis concerning appropriate treatment options for newly diagnosed, localized prostate cancer.2 DISCUSSION
Competing disease hazards can have a significant role when analyzing results of prior series in which patient selection for treatment differs according to how ill the patients appear. However, physicians reporting case series data rarely account for the impact of co-morbid disease.lo The resulting selection bias can have a significant impact on case series survival data. Several instruments have been developed to assist physicians in classifying the extent of co-morbid disease. We tested 3 indexes in a retrospective cohort of men with clinically localized prostate cancer to determine whether any could be used to adjust for the impact of co-morbid disease. Our findings demonstrate that all 3 instruments provide additional information concerning cumulative mortality among men with localized prostate cancer after controlling for age and Gleason score. Men 65 to 75 years old with untreated localized prostate cancer had significantly different 10-year mortality rates (all cause, or nonprostate cancer or prostate cancer associated mortalities) depending on the number and severity of associated co-morbid conditions. This finding has important implications for case series data. Patients undergoing aggressive treatment interventions, such as radical prostatectomy and possibly brachytherapy or cryosurgery, are likely to have fewer co-morbid conditions compared to men undergoing radiation therapy or conservative management with androgen deprivation. As a consequence, data reporting patient outcomes following aggressive management using all cause survival curves are likely to be biased in favor of greater survival rates compared to similar reports of outcomes following radiation therapy or conservative management. Physicians must review these results with caution, especially when making comparisons among alternative treatment strategies. Our results show that even restricting analysis to mortality associated with prostate cancer does not free researchers from controlling case series analyses for co-morbid conditions. Whether differences in mortality associated with COmorbid disease result from differences in tumor biology or simply from errors in determining the true cause of death on death certificates cannot be determined from our study. Unfortunately, this problem confronts all researchers reporting disease specific survival data. Although prostate cancer specific mortality may interest the researcher, patients and clinicians often rely on all cause mortality estimates when making decisions about treatment. Reporting results stratified by co-morbidity will assist clinicians and patients to determine which aggregate results are most applicable to prognosis. All 3 co-morbidity classification systems we tested are acceptable for stratifying results of case series. The index of coexistent disease is marginally more contributory in the all cause proportional hazards model for survival, analysis of mortality rate due to nonprostate causes of death, and predicting prostate cancer related mortality after controlling for age and Gleason score. Physicians who use any of these instruments will find them easy to administer especially in a Prospective setting. All 3 indexes inventory specific medical conditions according to definitions supplied by the develope r ~ . The ~ - ~Charlson et a1 and Kaplan-Feinstein indexes are scored as previously outlined, while the index of coexistent disease uses a key that incorporates activities of daily living. Regardless of the system used, we urge clinicians and researchers to report outcomes of series for treatment of prostate cancer stratified by Gleason grade and co-morbidities.
CONCLUSIONS
Controversy surrounding the treatment of localized prostate cancer frequently stems from differing estimates of treatment efficacy, which are often based on cause specific and all cause survival analyses of large case series. Unfortunately, other factors, such as co-morbid conditions, can affect these results and make comparisons among different treatment alternatives difficult. To determine whether any of 3 previously validated instruments used to evaluate comorbidity could provide more accurate predictions of patient mortality than those based on age alone, we constructed survival curves using a parametric proportional hazards model. With data from a retrospective population based cohort of men 65 to 75 years old diagnosed with clinically localized prostate cancer in Connecticut during the 1970s, we found that Gleason score and co-morbidity indexes were highly significant predictors of mortality (p
The expression used to compute the probability that a man y years old a t diagnosis will survive to age t years (t > y) is:
where e is the natural logarithm base. In many popular computer spreadsheet programs the computation involved in raising e to a power is done with a mathematical function denoted @exp( 1, so this computation can be accomplished easily with access to a microcomputer and spreadsheet software. This computation also can be done on a hand held scientific calculator.
132
CO-MORBIDITY OF F'ROSTATE CANCER
new method of classifying prognostic co-morbidity in longituThe coefficient B is fixed as a constant with the value dinal studies: development and validation. J . Chron. Dis., 40: 0.08169. To compute survival probabilities implied by the 373, 1987. 1986 Connecticut life tables, the remaining constant is given 7. Kaplan, M. H. and Feinstein, A. R.: The importance of classifying by A = -9.121. To compute survival probabilities underlying initial co-morbidity in evaluating the outcomes of diabetes results in our article values for the constant A are given in mellitus. J . Chron. Dis., 27: 387, 1974. table 2. 8. Catalona, W. J. and Whitmore, W. F., Jr.: New staging systems Use of this computational formula should be restricted to for prostate cancer. J . Urol., 142: 1302, 1989. initial ages, y, of 65 to 75 years and t not to exceed age 95 9. Kalbfleisch, J . D. and Prentice, R. L.: The Statistical Analysis of years. Extrapolation beyond these ranges goes beyond the Failure Time Data. New York John Wiley & Sons, 1980. data presented in our study. Additionally, it should be re- 10. Wasson, J. H., Cushman, C. C., Bruskewitz, R. C., Littenberg, B., membered that this formula computes a lower bound on the Mulley, A. G., J r . and Wennberg, J. E.: A structured literature survival probability for men diagnosed today because of the review of treatment for localized prostate cancer. Prostate Disease Patient Outcome Research Team. Arch. Fam. Med., 2 biases and empirical choices discussed previously.4 487, 1993. An example calculation of the estimated lower bound 10year survival probability for a man 70 years old at diagnosis EDITORIAL COMMENT with a Gleason grade 5 to 7 clinically localized tumor, who would be classified with an index of coexistent disease of 1 for This article has important implications for investigators and clico-morbidities is given by: nicians. Many researchers are studying the outcomes of different e{-l/0,08169.
e-8420. (e008169(801 - e0081169(70)))
(2)
which reduces further to e1-12,241.e-B"20.(e65362-e5.11"
)I
(3)
and then to e- .002698(364.58454)
(4)
and finally to 0.354. Age-specific survival curves are generated by varying t in small increments and repeating this calculation, then plotting the computed survival probabilities against the correspondingvalues oft. Age-specificlife expectancy is computed by numerical integration of the survival curve. REFERENCES
1. Albertsen, P.: A 72-year-old man with localized prostate cancer.
J.A.M.A., 2 7 4 69, 1995. 2. Brendler, C. B. and Walsh, P. C.: The role of radical prostatectomy in the treatment of prostate cancer. CA, 42:212, 1992. 3. Bagshaw, M. A., Cox, R. S. and Hancock, S. L.: Control of prostate cancer with radiation therapy: long-term results. J. Urol., part 2 , 1 5 2 1781, 1994. 4. Albertsen, P. C., Fryback, D. G., Storer, B. E., Kolon, T. F. and Fine, J.: Long-term survival among men with conservatively treated localized prostate cancer. J.A.M.A., 274: 626, 1995. 5. Greenfield, S., Apolone, G., McNeil, B. J. and Cleary, P. D.: The importance of co-existent disease in the occurrence of postoperative complications and one-year recovery in patients undergoing total hip replacement. Comorbidity and outcomes after hip replacement. Med. Care., 31: 141, 1993. 6. Charlson, M. E., Pompie, P., Ales, K. L. and MacKenzie, C. R.: A
treatments for men with clinically localized prostate cancer, which is a subject of considerable controversy. Because of the long doubling times of most such cancers, many retrospective, nonrandomized studies are being conducted to define optimal treatment strategies for M e r e n t patient subgroups pending definitive results of prospective randomized trials, which may be 10 years or longer away. Patient age and tumor factors, such as Gleason grade and pathological stage, are accepted as important potential confounding factors in these analyses. The authors clearly establish that patient comorbidity can bias such studies as well, and provide a n approach to measure and control for this factor. Such analytical methods are particularly important when the focus is on overall mortality, which is an important outcome given the known inaccuracies in determinations of cause of death in retrospective studies. This inaccuracy may have been reflected in the finding that co-morbidity was a sigdicant, although weaker, predictor of cancer specific mortality in this study. Clinicians often hear that aggressive early detection and treatment efforts for prostate cancer are best reserved for men with a 10-year life expectancy. This point seems to be 1of few on which most authorities agree, regardless of which side they take on the general issue of the net benefits of prostate cancer screening and treatment. However, how does a clinician decide which patient is likely to live 10 years considering not only age but also other medical problems? Table 1indicates that while men with average co-morbidity would be estimated to have a 10-year life expectancy at age 75 years, those with an index of coexisting disease score of 2 would have a similar life expectancy at age 65 years. This information, although based on 1986 life tables for 1state, may help practitioners in the trenches be more accurate in deciding who is likely to benefit from aggressive screening and treatment efforts. Michael J . Barry Medical Practices Evaluation Center Massachusetts General Hospital Boston, Massachusetts