Development and External Validation of a Melanoma Risk Prediction Model Based on Self-assessed Risk Factors
Autor: | Bruce K. Armstrong, Elisabete Weiderpass, Graham J. Mann, Kylie Vuong, Kevin McGeechan, Anne E. Cust, Jennifer H. Barrett, Hans-Olov Adami, Catherine M. Olsen, David C. Whiteman, D. Timothy Bishop, Marit B. Veierød, John R. Davies, John L. Hopper, Eiliv Lund |
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Rok vydání: | 2016 |
Předmět: |
Male
Oncology Skin Neoplasms Multivariate analysis Logistic regression 0302 clinical medicine Risk Factors 030212 general & internal medicine Child Melanoma Aged 80 and over education.field_of_study Absolute risk reduction Middle Aged Area Under Curve 030220 oncology & carcinogenesis Carcinoma Squamous Cell Female Risk assessment Cohort study Adult medicine.medical_specialty Adolescent Ultraviolet Rays Population Dermatology Risk Assessment Diagnostic Self Evaluation Young Adult 03 medical and health sciences Internal medicine medicine Humans Hair Color education Nevus Aged Sweden business.industry Australia United Kingdom Surgery Logistic Models ROC Curve Carcinoma Basal Cell Case-Control Studies Relative risk Multivariate Analysis Cutaneous melanoma business |
Zdroj: | JAMA Dermatology. 152:889 |
ISSN: | 2168-6068 |
Popis: | Importance Identifying individuals at high risk of melanoma can optimize primary and secondary prevention strategies. Objective To develop and externally validate a risk prediction model for incident first-primary cutaneous melanoma using self-assessed risk factors. Design, Setting, and Participants We used unconditional logistic regression to develop a multivariable risk prediction model. Relative risk estimates from the model were combined with Australian melanoma incidence and competing mortality rates to obtain absolute risk estimates. A risk prediction model was developed using the Australian Melanoma Family Study (629 cases and 535 controls) and externally validated using 4 independent population-based studies: the Western Australia Melanoma Study (511 case-control pairs), Leeds Melanoma Case-Control Study (960 cases and 513 controls), Epigene-QSkin Study (44 544, of which 766 with melanoma), and Swedish Women’s Lifestyle and Health Cohort Study (49 259 women, of which 273 had melanoma). Main Outcomes and Measures We validated model performance internally and externally by assessing discrimination using the area under the receiver operating curve (AUC). Additionally, using the Swedish Women’s Lifestyle and Health Cohort Study, we assessed model calibration and clinical usefulness. Results The risk prediction model included hair color, nevus density, first-degree family history of melanoma, previous nonmelanoma skin cancer, and lifetime sunbed use. On internal validation, the AUC was 0.70 (95% CI, 0.67-0.73). On external validation, the AUC was 0.66 (95% CI, 0.63-0.69) in the Western Australia Melanoma Study, 0.67 (95% CI, 0.65-0.70) in the Leeds Melanoma Case-Control Study, 0.64 (95% CI, 0.62-0.66) in the Epigene-QSkin Study, and 0.63 (95% CI, 0.60-0.67) in the Swedish Women’s Lifestyle and Health Cohort Study. Model calibration showed close agreement between predicted and observed numbers of incident melanomas across all deciles of predicted risk. In the external validation setting, there was higher net benefit when using the risk prediction model to classify individuals as high risk compared with classifying all individuals as high risk. Conclusions and Relevance The melanoma risk prediction model performs well and may be useful in prevention interventions reliant on a risk assessment using self-assessed risk factors. |
Databáze: | OpenAIRE |
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