The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, China.
Front Immunol. 2022 Jan 20;12:740968. doi: 10.3389/fimmu.2021.740968. eCollection 2021.
This study aimed to develop a risk of psoriatic arthritis (PsA) predictive model for plaque psoriasis patients based on the available features.
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 build a PsA risk predictive model. Calibration, discrimination, and clinical utility of the prediction model were evaluated by using the calibration plot, C-index, the area under the curve (AUC), and decision curve analysis. Internal validation was performed using bootstrapping validation. The model was subjected to external validation with two separate cohorts.
Age at onset, duration, nail involvement, erythematous lunula, onychorrhexis, oil drop, and subungual hyperkeratosis were presented as 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 internal validation and external cohort validation (0.741, 0.844, and 0.845). The decision curve indicated good effect of the PsA nomogram in guiding clinical practice.
This novel PsA nomogram could assess the risk of PsA in plaque psoriasis patients with good efficiency.
本研究旨在基于现有特征，为斑块状银屑病患者开发一种银屑病关节炎（PsA）风险预测模型。
招募了斑块状银屑病或 PsA 患者。收集了患者的特征、皮肤损伤和指甲临床表现。采用最小绝对收缩和选择算子（LASSO）进行特征选择，然后进行逻辑回归分析，进一步选择特征并构建 PsA 风险预测模型。通过校准图、C 指数、曲线下面积（AUC）和决策曲线分析评估预测模型的校准、区分和临床实用性。采用 bootstrap 验证进行内部验证。该模型还通过两个独立队列进行了外部验证。
发病年龄、病程、指甲受累、红斑半月、甲纵嵴、油滴和甲下过度角化被认为是预测指标，用于建立预测列线图。该预测模型显示出良好的校准和区分度（C 指数：0.759；95%CI：0.707-0.811）。该预测模型的 AUC 为 0.7578092。内部验证和外部队列验证中 C 指数的表现均非常出色（0.741、0.844 和 0.845）。决策曲线表明，PsA 列线图在指导临床实践方面具有良好的效果。
该新型 PsA 列线图可以有效地评估斑块状银屑病患者发生 PsA 的风险。
