Zhan Jinshan, Chen Fangqi, Li Yanqiu, Huang Changzheng
Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Department of Dermatology, Hubei NO.3 People's Hospital of Jiang Hang University, Wuhan, Hubei, China.
Clin Rheumatol. 2025 Jan;44(1):277-289. doi: 10.1007/s10067-024-07244-4. Epub 2024 Nov 25.
To develop a simplified predictive model for identifying psoriatic arthritis (PsA) in psoriasis patients.
Data from the National Health and Nutrition Examination Survey (NHANES) database were analyzed, including patients with psoriasis without arthritis (PsC) or PsA. The least absolute shrinkage and selection operator, Boruta algorithm, random forest, and stepwise regression were employed to select key variables from 38 potential predictors. Logistic regression models were constructed for each combination of selected variables and evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration plots, Brier scores, and decision curve analysis (DCA).
The study included 587 patients with psoriasis, 238 of whom had PsA. The variable combinations proposed by the Boruta algorithm exhibited the best overall performance. Key predictors in the Borutamodel included age, fasting glucose, education level, thyroid disease, hypertension, and chronic bronchitis. This model achieved area under the curve (AUC) of 0.781 (95% CI, 0.737-0.826) for the training set and 0.780 (95% CI, 0.712-0.848) for the testing set in the ROC curve analyses. The AUC values in the PR curves were 0.687 (95% CI, 0.611-0.757) and 0.653 (95% CI, 0.535-0.770), respectively. The Brier scores of 0.186 and 0.191 for the testing and training sets indicated a good fit, further supported by the calibration curves. DCA showed a net clinical benefit for decision thresholds ranging from 0.2 to 0.8 in both datasets.
The Borutamodel represents a promising tool for early risk assessment of PsA. Key Points • National Database Utilization: This study leverages the NHANES database to predict psoriatic arthritis risk, addressing previous limitations tied to regional or ethnic constraints. • Comprehensive Variable Analyses: The research examines 38 variables, including demographics, health conditions, laboratory results, and lifestyle factors, using four distinct screening methods and thorough evaluations of model performance. • Innovative Risk Model: The study introduces a novel risk assessment model that integrates age, fasting glucose, education, and comorbidities including hypertension, thyroid disease, and chronic bronchitis, thus moving beyond traditional focus on skin lesions and joint symptoms.
开发一种简化的预测模型，用于识别银屑病患者中的银屑病关节炎（PsA）。
分析了来自美国国家健康与营养检查调查（NHANES）数据库的数据，包括无关节炎的银屑病患者（PsC）或PsA患者。采用最小绝对收缩和选择算子、Boruta算法、随机森林和逐步回归从38个潜在预测因子中选择关键变量。针对所选变量的每种组合构建逻辑回归模型，并使用受试者工作特征（ROC）曲线、精确召回率（PR）曲线、校准图、布里尔评分和决策曲线分析（DCA）进行评估。
该研究纳入了587例银屑病患者，其中238例患有PsA。Boruta算法提出的变量组合表现出最佳的总体性能。Boruta模型中的关键预测因子包括年龄、空腹血糖、教育水平、甲状腺疾病、高血压和慢性支气管炎。在ROC曲线分析中，该模型在训练集的曲线下面积（AUC）为0.781（95%CI，0.737 - 0.826），在测试集的AUC为0.780（95%CI，0.712 - 0.848）。PR曲线中的AUC值分别为0.687（95%CI，0.611 - 0.757）和0.653（95%CI，0.535 - 0.770）。测试集和训练集的布里尔评分分别为0.186和0.191，表明拟合良好，校准曲线进一步支持了这一点。DCA显示，在两个数据集中，决策阈值在0.2至0.8范围内都有净临床益处。
Boruta模型是一种有前景的PsA早期风险评估工具。要点•国家数据库利用：本研究利用NHANES数据库预测银屑病关节炎风险，解决了以往与区域或种族限制相关的局限性。•综合变量分析：该研究使用四种不同的筛选方法并对模型性能进行全面评估，考察了38个变量，包括人口统计学、健康状况、实验室检查结果和生活方式因素。•创新风险模型：该研究引入了一种新型风险评估模型，整合了年龄、空腹血糖、教育程度以及包括高血压、甲状腺疾病和慢性支气管炎在内的合并症，从而超越了传统上对皮肤病变和关节症状的关注。
