Abstract
Targeted therapy and immunotherapy drugs for oncology have greater efficacy and tolerability than cytotoxic chemotherapeutic drugs. However, the cutaneous adverse drug reactions associated with these newer therapies are more common and remain poorly predicted. An effective prediction model is urgently needed and essential. This retrospective study included 1052 patients, divided into train set, test set, and external validation set. As a data-driven study, a total of 76 variables were collected. Univariate logistic analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression were utilized for feature screening. Finally, nine machine-learning models were constructed and compared, and grid search was performed to adjust the parameters. Model performance was evaluated using calibration curve and the area under the receiver operating characteristic curve (AUROC). Nine risk factors were eventually identified: age, treatment modality, cancer types, history of allergies, age-corrected Charlson comorbidity index, percentage of eosinophils, absolute number of monocytes, Eastern Cooperative Oncology Group Performance Status, and C-reactive protein. Among the models, the logistic model performed best, demonstrating strong performance in test set (AUROC = 0.734) and external validation set (AUROC = 0.817). This study identified nine significant risk factors and developed a nomogram prediction model. These findings have important implications for optimizing therapeutic efficacy and maintaining the quality of life of patients from the perspective of managing cutaneous adverse drug reactions. Trial Registration: ChiCTR2400088422.