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Prognostic Model for Predicting Survival in Men With Hormone-Refractory Metastatic Prostate Cancer

Susan Halabi, Eric J. Small, Philip W. Kantoff, Michael W. Kattan, Ellen B. Kaplan, Nancy A. Dawson, Ellis G. Levine, Brent A. Blumenstein, Nicholas J. Vogelzang

From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of Hematology and Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL.

Address reprint requests to Susan Halabi, PhD, Duke University Medical Center, Box 3958, Durham, NC 27710; email: susan.halabi{at}duke.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Purpose: To develop and validate a model that can be used to predict the overall survival probability among metastatic hormone-refractory prostate cancer patients (HRPC).

Patients and Methods: Data from six Cancer and Leukemia Group B protocols that enrolled 1,101 patients with metastatic hormone-refractory adenocarcinoma of the prostate during the study period from 1991 to 2001 were pooled. The proportional hazards model was used to develop a multivariable model on the basis of pretreatment factors and to construct a prognostic model. The area under the receiver operating characteristic curve (ROC) was calculated as a measure of predictive discrimination. Calibration of the model predictions was assessed by comparing the predicted probability with the actual survival probability. An independent data set was used to validate the fitted model.

Results: The final model included the following factors: lactate dehydrogenase, prostate-specific antigen, alkaline phosphatase, Gleason sum, Eastern Cooperative Oncology Group performance status, hemoglobin, and the presence of visceral disease. The area under the ROC curve was 0.68. Patients were classified into one of four risk groups. We observed a good agreement between the observed and predicted survival probabilities for the four risk groups. The observed median survival durations were 7.5 (95% confidence interval [CI], 6.2 to 10.9), 13.4 (95% CI, 9.7 to 26.3), 18.9 (95% CI, 16.2 to 26.3), and 27.2 (95% CI, 21.9 to 42.8) months for the first, second, third, and fourth risk groups, respectively. The corresponding median predicted survival times were 8.8, 13.4, 17.4, and 22.80 for the four risk groups.

Conclusion: This model could be used to predict individual survival probabilities and to stratify metastatic HRPC patients in randomized phase III trials.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
PROSTATE CANCER is the leading cancer among men in the United States, accounting for 31% of all male malignancies.¹ The American Cancer Society estimated that 189,000 men in the United States would be diagnosed with prostate cancer and that 30,200 men would die from this cancer during 2002.¹ It is estimated that one in six men will be diagnosed with prostate cancer sometime during his lifetime and that one in 30 men will die of this disease.²

Several prognostic models have been developed to predict different outcomes in various patient populations, from untreated clinically localized cancer to patients in other clinical states. The vast majority of models, however, are based on predicting outcomes: those that were pathologic,^3,4 prostate-specific antigen (PSA) recurrence,^5–10 or disease recurrence in patients with clinically localized prostate cancer.

Few studies have identified prognostic models that are predictive of survival in men with hormone-refractory prostate cancer (HRPC). In an analysis of 85 patients with metastatic hormone-resistant prostate cancer, Berry et al¹¹ identified factors predictive of short survival duration. These included age (> 65 years); severe bone pain; poor performance status; presence of soft tissue metastases; anemia; and elevated levels of lactate dehydrogenase (LDH), acid phosphatase, alkaline phosphatase, and prolactin. In an analysis of 1,020 patients, Emrich et al¹² identified factors that were predictive of survival in order of importance: previous hormone response, anorexia, elevated acid phosphatase, pain, elevated alkaline phosphatase, obstructive symptoms, tumor grade, performance status, anemia, and age at diagnosis. Kantoff et al¹³ identified prognostic factors on the basis of 242 metastatic HRPC patients. These factors were alkaline phosphatase, LDH, baseline PSA, and hemoglobin. Other factors identified in other studies were greater than 50% decline in PSA,^14–16 changes in PSA after therapy,^17,18 weight loss,¹⁹ extent of bone metastasis,^19,20 pretreatment serum testosterone level,²⁰ and any decline in PSA.²¹ Biologic markers such as plasma and urine vascular endothelial growth factor and reverse transcriptase polymerase chain reaction for PSA have been identified as statistically significant predictors of overall survival in androgen-independent cancer patients.^22–25 Recently, colleagues at Memorial Sloan-Kettering Cancer Center (New York, NY) developed and validated a pretreatment nomogram.²⁶

The objective of this study was to develop a pretreatment prognostic model that could be used to predict survival probability among men with HRCP. Data from six Cancer and Leukemia Group B (CALGB) studies were used to examine the relationship between baseline factors and overall survival. Furthermore, independent data sets were used to validate the prognostic model.

	PATIENTS AND METHODS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Study Population
Data on 1,101 men from six clinical trials conducted by the CALGB between 1992 to 1998 were pooled. All patients signed informed consent forms before study registration. These studies were CALGB 9181, 9182, 9480, 9680, 9780, and 9583.^{13,16,27–30} The patient population consisted of men with prostate cancer who had progressive metastatic disease and for whom both androgen ablation and antiandrogen withdrawal had failed. CALGB 9181 was a randomized study of low-dose versus high-dose megestrol. One hundred forty-nine patients were randomly assigned to treatment on this study. CALGB 9182 was a phase III study in which 242 patients were randomly assigned to receive either hydrocortisone with mitoxantrone or hydrocortisone alone. CALGB 9480 was a phase III study of 390 patients randomly assigned to receive three different fixed doses of suramin. CALGB 9583 was a phase III study in which 260 patients were randomly assigned with equal probability to receive antiandrogen withdrawal alone followed by ketoconazole at progression or antiandrogen withdrawal plus simultaneous ketoconazole and hydrocortisone. CALGB 9680 was a randomized phase II trial of high-dose mitoxantrone/granulocyte-macrophage colony-stimulating factor and low-dose steroids. Twenty-one patients without pelvic irradiation received 21 mg/m² mitoxantrone every 3 weeks (arm 1), whereas 24 patients who had pelvic irradiation received 17 mg/m² (arm 2). CALGB 9780 was a phase II study in which 46 patients were treated with docetaxel, estramustine, and low-dose hydrocortisone. For all studies, eligible patients had progressive adenocarcinoma of the prostate after androgen ablation, an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, and adequate hematologic, renal, and hepatic function. Additional details have been published elsewhere.^{13,16,21,27–30}

Statistical Analysis
The main end point was survival duration. Survival duration was defined as the time between randomization (or registration for the nonrandomized studies) and death. Patients were censored if they were known to be alive or they were lost to follow-up. The Kaplan-Meier product-limit estimator was used to estimate the survival distribution.³¹ The proportional hazards model was used to assess the prognostic significance of baseline factors in univariable and multivariable analyses.³² The proportional hazards model was used to develop a multivariable model and to validate the prognostic model. The goal was to use two thirds (n = 760) of the 1,101 patients for the learning set and one third (n = 341) of the patients for the validation set. Data for the learning set were used from three protocols (CALGB 9181, 9182, and 9480) to develop the model (nomogram). PSA, LDH, and alkaline phosphatase levels had heavily right-skewed distributions and were modeled using the log transformation. Martingale and Schoenfeld residuals were used to check the adequacy of the linearity and the proportional hazards assumptions.³³

We assessed the predictive performance of the final model (nomogram) by internal validation using the bootstrap resampling technique.^33,34 For each of the 200 bootstrap samples, the model was refitted and then tested on the original sample to obtain a bias-corrected estimate of predictive accuracy (ie, when the model is applied to an independent sample of patients).³³ The area under the receiver operating characteristic curve (ROC) was calculated as a measure of predictive discrimination in the original sample and the bootstrapped validation samples. An index of 0.5 indicates no discrimination ability, whereas a value of 1 indicates perfect discrimination.³³ Calibration of the model predictions was evaluated by comparing the predicted probability at 12 and 24 months with the Kaplan-Meier survival probability.

In addition, data from three protocols (CALGB 9583, 9680, and 9780) were used to validate the final model. For each patient, we calculated a risk score of death using the parameter estimates from the final model that was developed from the learning sample. The final model was used to obtain individual predicted probability of survival for each patient on the basis of his covariates, and data were categorized on the basis of the quartile of the predicted probability.³⁵ The statistical analyses for model (nomogram) development and validation were done using the S-plus software (Statistical Sciences, Seattle, WA) and, as well, software that is available electronically in the public domain.³⁶

	RESULTS

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Learning Data
Table 1 displays the baseline characteristics of the 760 patients in the learning sample and 341 patients in the validation samples. The median age of the learning sample was 71 years, and 84% of these patients were white. Eighty-seven percent of the patients had an ECOG performance status of 0 or 1. Eighty-two percent of patients had bone metastases and 31% had lymph node involvement. Median baseline PSA was 126 ng/mL, median hemoglobin was 12 g/dL, and median alkaline phosphatase was 171 U/L. Median survival duration among these patients was 13 months (95% confidence interval [CI], 12 to 14), and median length of follow-up among surviving patients was 37 months (Fig 1).

View larger version (14K): [in this window] [in a new window]   Fig 1. Overall survival in the learning and validation samples.

View larger version (19K): [in this window] [in a new window]   Fig 2. Plots showing the relationship of predictors with hazard of death.

View larger version (52K): [in this window] [in a new window]   Fig 3. Pretreatment nomogram predicting probability of survival.

Validation Sample
Patients in the validation data set had an improved survival compared with patients in the learning samples (Table 1). The median survival duration for 341 patients was 17 months (95% CI, 14 to 19 months), and the median follow-up for surviving patients was 24 months.

We evaluated the nomogram for its discriminative ability and calibration (external validation). The area under the ROC curve using the 341 patients was 0.68 in the validation samples. Figure 4 presents how the predictions from the model at 12 and 24 months compared with the actual survival probability for the 341 patients in our analysis (calibration).

View larger version (13K): [in this window] [in a new window]   Fig 4. Calibration of the nomogram.

View larger version (17K): [in this window] [in a new window]   Fig 5. Observed survival by risk groups.

	DISCUSSION

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

Our model is reasonably accurate in terms of correctly predicting survival probability. The accuracy of the prediction is, however, higher at 1 year as opposed to 2 years. This may simply reflect difficulty in predicting further into the future or may reflect real differences in overall survival rates between those patients in the learning data set and those in the validation data sets. Additional research should validate this model prospectively.

Available therapies for HRPC patients are palliative and have not been shown to prolong survival duration.^39,40 However, survival is clearly dependent on pretreatment clinical variables. Identification of prognostic factors is important to classify patients into different strata.⁴¹ An understanding of the distribution of patients into these studies could account for results reported in phase II clinical trials. For phase III trials, the stratification of patients ensures that the treatment groups are balanced with respect to the known or possible factors to avoid the possibility of confounding. Furthermore, the utility of risk stratification may help identify subsets of patients that may have prolonged survival duration. Indeed, it is possible that some treatment will be beneficial for only a subgroup of patients but not for others.

The proposed model has several strengths. First, the model incorporated a large number of patients with metastatic HRPC. Second, the data were prospectively collected from multicenter protocols that enrolled and treated patients on carefully controlled and monitored clinical trials that are of high quality. Third, detailed treatment information and outcome data were available from these trials.

Limitations of this study include the fact that patients enrolled in these studies were required to have an ECOG performance status of 0 to 2 and were also deemed appropriate for participation in a clinical trial. Thus, the results of the study cannot be generalized to the entire HRPC patient population. Second, the patient population is heterogeneous because we pooled data from six different studies that enrolled patients in the study period between 1992 and 1998. However, this heterogeneity may also be the strength of the data, which increases the general applicability of the derived model. Finally, the model that was developed and validated did not include biologic markers as predictors of overall survival. Future research is needed that incorporates such markers.

In summary, the model developed here has been thoroughly validated within the CALGB. The variables included in the prognostic model are routinely collected in clinical practice and can be easily incorporated to derive a prognostic score. This nomogram will be helpful to obtain individual survival probability and to stratify patients in future randomized phase III studies.

	APPENDIX

TOP ABSTRACT INTRODUCTION PATIENTS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
The following institutions participated in these studies:Lee S. Schwartzberg, CA71323, Baptist Cancer Institute CCOP, Harvey B. Niell, CA47555, University of Tennessee Memphis, Memphis, TN; Stephen George, CA33601, CALGB Statistical Office, Jeffrey Crawford, CA47577, Duke University Medical Center, Durham, James N. Atkins, CA45808, Southeast Cancer Control Consortium Inc CCOP, Goldsboro, Thomas C. Shea, CA47559, University of North Carolina at Chapel Hill, Chapel Hill, David D Hurd, CA03927, Wake Forest University School of Medicine, Winston-Salem, NC; Irving M. Berkowitz, CA45418, Christiana Care Health Services, Inc CCOP, Wilmington, DE; Jeffrey Kirshner, CA45389, Community Hospital-Syracuse CCOP,Syracuse, Marc Citron, CA11028, Long Island Jewish Medical Center, Lake Success, Lewis R. Silverman, CA04457, Mount Sinai School of Medicine, New York, Daniel R Budman, CA35279, North Shore University Hospital, Manhasset, Ellis Levine, CA02599, Roswell Park Cancer Institute, Buffalo, Stephen L. Graziano, CA21060, SUNY Upstate Medical University, Syracuse, Michael Schuster, CA07968, Weill Medical College of Cornell University, New York, NY; George P Canellos, CA32291, Dana Farber Cancer Institute, Michael L. Grossbard, CA12449, Massachusetts General Hospital, Boston, F. Marc Stewart, CA37135, University of Massachusetts Medical Center, Worcester, MA; Marc Ernstoff, CA04326, Dartmouth Medical School-Norris Cotton Cancer Center, Lebanon, NH; Edward P. Gelmann, CA77597, Georgetown University Medical Center, Joseph J. Drabeck, CA26806, Walter Reed Army Medical Center, Washington, DC; H. James Wallace Jr, CA35091, Green Mountain Oncology Group CCOP, Bennington, Hyman B. Muss, CA77406, Vermont Cancer Center, Burlington, VT; Jonathan A. Polikoff, CA45374, Kaiser Permanente CCOP, San Diego, Stephen Seagren, CA11789, University of California at San Diego, San Diego, Alan Venook, CA60138, University of California at San Francisco, San Francisco, CA; Mark R. Green, CA03927, Medical University of South Carolina, Charleston, SC; William Sikov, CA08025, Rhode Island Hospital, Providence, RI; John Ellerton, CA35421, Southern Nevada Cancer Research Foundation CCOP, Las Vegas, NV; Clara D Bloomfield, CA77658, The Ohio State University Medical Center, Columbus, OH; Robert Diasio, CA47545, University of Alabama Birmingham, Birmingham, AL; Gini Fleming, CA41287, University of Chicago Medical Center, Chicago, David Gustin, CA74811, University of Illinois at Chicago, Chicago, IL; David Van Echo, CA31983, University of Maryland Cancer Center, Baltimore, MD; Bruce A Peterson, CA16450, University of Minnesota, Minneapolis, MN; Michael C Perry, CA12046, University of Missouri/Ellis Fischel Cancer Center, Columbia, Nancy Bartlett, CA77440, Washington University School of Medicine, St. Louis, MO; Anne Kessinger, CA77298, University of Nebraska Medical Center, Omaha, NE; and John D. Roberts, CA52784, Virginia Commonwealth University MB CCOP, Richmond, VA.

	NOTES

 
Supported in part by grants from the National Cancer Institute (CA31946 and CA 36601), National Institutes of Health, Department of Health and Human Services, Bethesda, MD, to the Cancer and Leukemia Group B (R. Schilsky).

The contents of this article are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute.

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Submitted June 17, 2002; accepted December 23, 2002.

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