Evaluating the Risk Factors of Post Inflammatory Hyperpigmentation Complications with Nd-YAG Laser Toning Using LASSO-Based Algorithm

Abstract

The neodymium-doped yttrium aluminum garnet (Nd-YAG) laser is used for removal of pigmented skin patches and rejuvenation of skin. However, complications such as hyperpigmentation, hypopigmentation, and petechiae can occur after frequent treatments. Therefore, identifying the risk factors for such complications is important. The development of a multivariable logistic regression model with least absolute shrinkage and selection operator (LASSO) is needed to provide valid predictions about the incidence of post inflammatory hyperpigmentation complication probability (PIHCP) among patients treated with Nd-YAG laser toning. A total of 125 female patients undergoing laser toning therapy between January 2014 and January 2016 were examined for post-inflammatory hyperpigmentation (PIH) complications. Factor analysis was performed using 15 potential predictive risk factors of PIH determined by a physician. The LASSO algorithm with cross-validation was used to select the optimal number of predictive risk factors from the potential factors for a multivariate logistic regression PIH complication model. The optimal number of predictive risk factors for the model was five: immediate endpoints of laser (IEL), α-hydroxy acid (AHA) peels, Fitzpatrick skin phototype (FSPT), acne, and melasma. The area under the receiver operating characteristic curve (AUC) was 0.79 (95% CI, 0.70–0.88) in the optimal model. The overall performance of the LASSO-based PIHCP model was satisfactory based on the AUC, Omnibus, Nagelkerke R2, and Hosmer–Lemeshow tests. This predictive risk factor model is useful to further optimize laser toning treatment related to PIH. The LASSO-based PIHCP model could be useful for decision-making.