Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer

Abstract

Purpose: Prognostic models for overall survival (OS) for patients with metastatic castration-resistant prostate cancer (mCRPC) are dated and do not reflect significant advances in treatment options available for these patients. This work developed and validated an updated prognostic model to predict OS in patients receiving first-line chemotherapy.

Methods: Data from a phase III trial of 1,050 patients with mCRPC were used (Cancer and Leukemia Group B CALGB-90401 [Alliance]). The data were randomly split into training and testing sets. A separate phase III trial served as an independent validation set. Adaptive least absolute shrinkage and selection operator selected eight factors prognostic for OS. A predictive score was computed from the regression coefficients and used to classify patients into low- and high-risk groups. The model was assessed for its predictive accuracy using the time-dependent area under the curve (tAUC).

Results: The model included Eastern Cooperative Oncology Group performance status, disease site, lactate dehydrogenase, opioid analgesic use, albumin, hemoglobin, prostate-specific antigen, and alkaline phosphatase. Median OS values in the high- and low-risk groups, respectively, in the testing set were 17 and 30 months (hazard ratio [HR], 2.2; P < .001); in the validation set they were 14 and 26 months (HR, 2.9; P < .001). The tAUCs were 0.73 (95% CI, 0.70 to 0.73) and 0.76 (95% CI, 0.72 to 0.76) in the testing and validation sets, respectively.

Conclusion: An updated prognostic model for OS in patients with mCRPC receiving first-line chemotherapy was developed and validated on an external set. This model can be used to predict OS, as well as to better select patients to participate in trials on the basis of their prognosis.

Fig 1.

Survival distribution by the training, testing, and validation data sets.

Fig 2.

Nomogram predicting overall survival probability. Instructions to physicians: All of the eight prognostic factors should be available before using this model. An online calculator is available at: https://www.cancer.duke.edu/Nomogram/firstlinechemotherapy.html. Please start from the second top axis by identifying the opioid analgesic use. Draw a vertical line to the points axis (top line) to represent the number of prognostic points the patients will receive for opioid analgesic use. Do the same for the other prognostic variables. Once all prognostic points for the predictors have been determined, add up the prognostic points for each prognostic variable. On the basis of the total points, one can determine the 18-month survival probability by drawing a vertical line from the total points x-axis to the survival probability. The same process can be performed to estimate the 24-, 30-, 36-, and 48-month survival probability or the median survival. ECOG PS, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; PSA, prostate-specific antigen; ULN, upper limit of normal.

Fig 3.

(A, B) Kaplan-Meier survival curves for the two risk groups in the testing and validation sets. (C, D) Kaplan-Meier survival curves for the three risk groups in the testing and validation sets.

Fig 4.

Time-dependent area under the curve (AUC) by the three models.

Fig A1.

Relationship between variables in the model and log hazard (HR) of death. ECOG PS, Eastern Cooperative Oncology Group performance status; LDH, lactate dehydrogenase; PSA, prostate-specific antigen; ULN, upper limit of normal.

Fig A2.

Calibration plots for the Cancer and Leukemia Group B 90401 training set at (A) 18, (B) 21, (C) 24, and (D) 30 months.