A PROGNOSTIC MODEL FOR PREDICTING 10-YEAR SURVIVAL IN PATIENTS WITH PRIMARY MELANOMA

Objective: To develop a prognostic model, based on clinical and pathol ogic data that are routinely available to the clinician, that would es timate the chance for survival of a patient with primary cutaneous mel anoma after definitive surgical therapy. Design: Cohort analytical stu dy. Setting: University medical center. Patients: 488 patients with pr imary cutaneous melanoma who had no apparent metastatic disease. Patie nts were followed prospectively for at least 10 years. An independent validation sample of 142 patients was used to assess the stability of the model. Measurements: Six clinical and pathologic variables that pr edict survival and are readily available to the clinician were used to develop a prediction model. The variables were tested for their assoc iation with death by using a univariate logistic regression model. Poi nt estimates were generated for the probability of surviving melanoma at 10 years. Variables that were statistically significantly associate d with survival were retained for testing in a logistic regression mod el. Results: 488 patients were followed prospectively for a median of 13.5 years (minimum, 10.0 years; maximum, 20.5 years). The overall 10- year survival of the study group was 78%. Four variables were found to be independent predictors of survival. Presented as adjusted odds rat ios, from strongest to weakest relative predictive strength, these var iables were tumor thickness (odds ratio, 50.8), site of primary melano ma (odds ratio, 4.4), age of the patient (odds ratio, 3.0), and sex of the patient (odds ratio, 2.0). The four-variable model was significan tly more accurate than tumor thickness alone, particularly for predict ing death. Overall, use of the model reduced the error rate of the pre diction of death by 50%. Conclusions: A prognostic model that uses fou r readily accessible variables more accurately predicts outcome in pat ients with primary melanoma than does tumor thickness alone. This four -variable model can identify patients at high risk for the recurrence of disease, an identification that becomes increasingly important as a djuvant therapies are developed for treatment of melanoma.