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Br J Dermatol. 2019 Sep 14. doi: 10.1111/bjd.18524. [Epub ahead of print]

A risk prediction model for the development of subsequent primary melanoma in a population-based cohort.

Author information

1
Cancer Epidemiology and Prevention Research, Sydney School of Public Health, The University of Sydney, Sydney, Australia.
2
Melanoma Institute Australia, The University of Sydney, Sydney, Australia.
3
Lineberger Comprehensive Cancer Center, Chapel Hill, NC, U.S.A.
4
Department of Dermatology, University of North Carolina, Chapel Hill, NC, U.S.A.
5
University of Texas MD Anderson Cancer Center, Houston, TX, U.S.A.
6
Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
7
Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, U.S.A.
8
Department of Internal Medicine, University of New Mexico Cancer Center, University of New Mexico, Albuquerque, NM, U.S.A.
9
Department of Population Health Sciences, Medical College of Georgia, Augusta University, Augusta, GA, U.S.A.
10
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, U.S.A.
11
Department of Surgery, University of North Carolina, Chapel Hill, NC, U.S.A.

Abstract

BACKGROUND:

Guidelines for follow-up of patients with melanoma are based on limited evidence.

OBJECTIVES:

To guide skin surveillance, we developed a risk prediction model for subsequent primary melanomas, using demographic, phenotypical, histopathological, sun exposure and genomic risk factors.

METHODS:

Using Cox regression frailty models, we analysed data for 2613 primary melanomas from 1266 patients recruited to the population-based Genes, Environment and Melanoma study in New South Wales, Australia, with a median of 14 years' follow-up via the cancer registry. Discrimination and calibration were assessed.

RESULTS:

The median time to diagnosis of a subsequent primary melanoma decreased with each new primary melanoma. The final model included 12 risk factors. Harrell's C-statistic was 0·73 [95% confidence interval (CI) 0·68-0·77], 0·65 (95% CI 0·62-0·68) and 0·65 (95% CI 0·61-0·69) for predicting second, third and fourth primary melanomas, respectively. The risk of a subsequent primary melanoma was 4·75 times higher (95% CI 3·87-5·82) for the highest vs. the lowest quintile of the risk score. The mean absolute risk of a subsequent primary melanoma within 5 years was 8·0 ± SD 4.1% after the first primary melanoma, and 46·8 ± 15·0% after the second, but varied substantially by risk score.

CONCLUSIONS:

The risk of developing a subsequent primary melanoma varies considerably between individuals and is particularly high for those with two or more primary melanomas. The risk prediction model and its associated nomograms enable estimation of the absolute risk of subsequent primary melanoma, on the basis of on an individual's risk factors, and can be used to tailor surveillance intensity, communicate risk and provide patient education.

PMID:
31520533
DOI:
10.1111/bjd.18524

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