Response to Laser Treatment of Café au Lait Macules Based on Morphologic Features

Abstract

Response to laser treatment for café au lait macules (CALMs) is inconsistent and difficult to predict. To test the hypothesis that irregularly bordered CALMs of the "coast of Maine" subtype respond better to treatment than those of the smooth-bordered "coast of California" subtype. This retrospective case series included patients from 2 multiple-clinician US practices treated from 2005 through 2016. All patients had a clinical diagnosis of CALM and were treated with a Q-switched or picosecond laser. A total of 51 consecutive patients were eligible, 6 of whom were excluded owing to ambiguous lesion subtype. Observers were blinded to final patient groupings. Treatment with 755-nm alexandrite picosecond laser, Q-switched ruby laser, Q-switched alexandrite laser, or Q-switched 1064-nm Nd:YAG laser. Main outcome was grade in a visual analog scale (VAS) consisting of 4 levels of treatment response: poor (grade 1, 0%-25% improvement), fair (grade 2, 26%-50% improvement), good (grade 3, 51%-75% improvement), and excellent (grade 4, 76%-100% improvement). Forty-five patients were included in the series, 19 with smooth-bordered lesions and 26 with irregularly bordered lesions. Thirty-four (76%) of the participants were female; 33 (73%) were white; and the mean age at the time of laser treatment was 14.5 years (range, 0-44 years). Smooth-bordered lesions received a mean VAS score of 1.76, corresponding to a fair response on average (26%-50% pigmentary clearance). Irregularly bordered lesions received a mean VAS score of 3.67, corresponding to an excellent response on average (76%-100% clearance) (P < .001). CALMs with jagged or ill-defined borders of the coast of Maine subtype tend to respond well to laser treatment, whereas those with smooth and well-defined borders of the coast of California subtype tend to have poor response. Clinicians using Q-switched or picosecond lasers to treat CALMs can use morphologic characteristics to help predict response and more effectively manage patient expectations.