Lin Zheng, Pan Si-Yi, Shi Yue-Yi, Wu Xuan, Dou Yuan, Lin Ping, Cao Yi
Department of Dermatology, First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Department of Nephrology, Hangzhou Traditional Chinese Medical (TCM) Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Front Immunol. 2024 Dec 10;15:1497713. doi: 10.3389/fimmu.2024.1497713. eCollection 2024.
Psoriatic arthritis (PSA) is an inflammatory joint disease associated with psoriasis (PSO) that can be easily missed. Existing PSA screening tools ignore objective serologic indicators. The aim of this study was to develop a disease screening model and the Psoriatic Arthritis Inflammation Index (PSAII) based on serologic data to enhance the efficiency of PSA screening.
A total of 719 PSO and PSA patients from the National Health and Nutrition Examination Survey (NHANES) (as training set and test set) and 135 PSO and PSA patients who were seen at The First Affiliated Hospital of Zhejiang Chinese Medical University (as external validation set) were selected, 31 indicators for these patients were collected as potential input features for the model. Least Absolute Shrinkage and Selection Operator (LASSO) was used to identify PSA-related features. Five models of logistic regression (LR), random forest, k-nearest neighbor, gradient augmentation and neural network were developed in the training set using quintuple cross validation. And we developed PSAII based on the results of LASSO regression and weights of logistic model parameters. All performance metrics are derived on the test set and the external validation set.
Five variables were selected to build models, including age, lymphocyte percentage, neutrophil count, eosinophilic count, and C-reactive protein. In all established models, the LR model performed the best, with an Area Under Curve (AUC) of 0.87 (95% confidence interval (CI): 0.83-0.90) on the test set; on the external validation set the AUC was 0.82 (95%CI: 0.74-0.90). The PSAII formula was PSAII = percentage of lymphocytes × C-reactive protein/(neutrophil count × eosinophilic count × 10). The AUC of PSAII in the test is 0.93 (95%CI: 0.88-0.97), and the cutoff value is 18. The AUC of the external validation set is 0.81 (95%CI: 0.72-0.89).
This study developed and validated five models to assist screening for PSA by analyzing serum data from NHANES and Chinese populations. The LR model demonstrated the best performance. We created PSAII for PSA screening. However, the high false positive rate of PSAII makes it necessary to combine it with other PSA screening tools when applied.
银屑病关节炎（PSA）是一种与银屑病（PSO）相关的炎性关节疾病，容易被漏诊。现有的PSA筛查工具忽略了客观的血清学指标。本研究的目的是基于血清学数据开发一种疾病筛查模型和银屑病关节炎炎症指数（PSAII），以提高PSA筛查的效率。
从美国国家健康与营养检查调查（NHANES）中选取719例PSO和PSA患者（作为训练集和测试集），以及在浙江中医药大学附属第一医院就诊的135例PSO和PSA患者（作为外部验证集），收集这些患者的31项指标作为模型的潜在输入特征。使用最小绝对收缩和选择算子（LASSO）来识别与PSA相关的特征。在训练集中使用五重交叉验证开发了逻辑回归（LR）、随机森林、k近邻、梯度增强和神经网络五种模型。并根据LASSO回归结果和逻辑模型参数权重开发了PSAII。所有性能指标均在测试集和外部验证集上得出。
选择了五个变量来构建模型，包括年龄、淋巴细胞百分比、中性粒细胞计数、嗜酸性粒细胞计数和C反应蛋白。在所有建立的模型中，LR模型表现最佳，在测试集上的曲线下面积（AUC）为0.87（95%置信区间（CI）：0.83 - 0.90）；在外部验证集上AUC为0.82（95%CI：0.74 - 0.90）。PSAII公式为PSAII = 淋巴细胞百分比×C反应蛋白/（中性粒细胞计数×嗜酸性粒细胞计数×10）。PSAII在测试集中的AUC为0.93（95%CI：0.88 - 0.97），截断值为18。外部验证集的AUC为0.81（95%CI：0.72 - 0.89）。
本研究通过分析NHANES和中国人群的血清数据，开发并验证了五种辅助PSA筛查的模型。LR模型表现最佳。我们创建了用于PSA筛查的PSAII。然而，PSAII的高假阳性率使得在应用时需要将其与其他PSA筛查工具相结合。
