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February 12, 2014

Risk Prediction Models for Incident Primary Cutaneous Melanoma

KVKylie VuongThe University of Melbourne KMKevin McGeechanThe University of Sydney BABruce K. ArmstrongBoston University

Puntos clave

To review existing multivariable risk prediction models for incident primary cutaneous melanoma in adults.
Systematic review of studies published until April 30, 2013, sourced from EMBASE, MEDLINE, PREMEDLINE, and Cochrane databases.
Nineteen studies with 28 prediction models were included, focusing on various predictors related to melanoma risk.
Independent reviewers extracted and analyzed model performance and study characteristics.
Discrimination for the models ranged from fair to very good (AUC 0.62-0.86) across nine studies.
The Fears model tailored for the US population emerged as the most clinically relevant tool among the reviewed models.
Limited validation and variability in model performance were noted, suggesting the need for further assessment.

Resumen

IMPORTANCE: Currently, there is no comprehensive assessment of melanoma risk prediction models. OBJECTIVE: To systematically review published studies reporting multivariable risk prediction models for incident primary cutaneous melanoma for adults. EVIDENCE REVIEW: EMBASE, MEDLINE, PREMEDLINE, and Cochrane databases were searched to April 30, 2013. Eligible studies were hand searched and citation tracked. Two independent reviewers extracted information. FINDINGS: Nineteen studies reporting 28 melanoma prediction models were included. The number of predictors in the final models ranged from 2 to 13; the most common were nevi, skin type, freckle density, age, hair color, and sunburn history. There was limited reporting and substantial variation among the studies in model development and performance. Discrimination (the ability of the model to differentiate between patients with and without melanoma) was reported in 9 studies and ranged from fair to very good (area under the receiver operating characteristic curve, 0.62-0.86). Few studies assessed internal or external validity of the models or their use in clinical and public health practice. Of the published melanoma risk prediction models, the risk prediction tool developed by Fears and colleagues, which was designed for the US population, appears to be the most clinically useful and may also assist in identifying high-risk groups for melanoma prevention strategies. CONCLUSIONS AND RELEVANCE: Few melanoma risk prediction models have been comprehensively developed and assessed. More external validation and prospective evaluation will help translate melanoma risk prediction models into useful tools for clinical and public health practice.

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