Predicting the Risk of Psoriatic Arthritis in Plaque Psoriasis Patients: Development and Assessment of a New Predictive Nomogram - Liu, Panpan; Kuang, Yehong; Ye, Li; Peng, Cong; Chen, Wangqing; Shen, Minxue; Zhang, Mi; Zhu, Wu; Lv, Chengzhi; Chen, Xiang (Frontiers in Immunology, 2022)
Objective: This study aimed to develop a risk of psoriatic arthritis (PsA) prediction model for plaque psoriasis patients based on the available features. Methods: Patients with plaque psoriasis or PsA were recruited. The characteristics, skin lesions, and nail clinical manifestations of the patients have been collected. The least absolute shrinkage was used to optimize feature selection, and logistic regression analysis was applied to further select features and built a PsA risk predicting model. Calibration, discrimination, and clinical utility of the predicting model were evaluated by using the calibration plot, C-index, the area under curve (AUC), and decision curve analysis. Internal validation was performed using the bootstrapping validation. The model was subjected to external validation with two separate cohorts. Results: Age at onset, duration, nail involvement, erythematous lunula, onychorrhexis, oil-drop, and subungual hyperkeratosis were presented predictors to perform the prediction nomogram. The predictive model showed good calibration and discrimination (C-index: 0.759; 95% CI: 0.707-0.811). The AUC of this prediction model was 0.7578092. Excellent performances of the C-index were reached in the interval validation and external cohorts validation (0.741, 0.844, and 0.845, respectively). The decision curve indicated the good effect of the PsA nomogram guiding clinical practice. Conclusion: This novel PsA nomogram could assess the risk of PsA in plaque psoriasis patients with good efficiency.