Wu Wenqi, Hu Xiaohao, Yan Linyang, Li Zhiyin, Li Bo, Chen Xinpeng, Lin Zexun, Zeng Huiqiong, Li Chun, Mo Yingqian, Wu Yalin, Wang Qingwen
Department of Rheumatology and Immunology, Peking University Shenzhen Hospital, Shenzhen, People's Republic of China.
Shenzhen Key Laboratory of Inflammatory and Immunology Diseases, Shenzhen, People's Republic of China.
J Inflamm Res. 2025 Feb 3;18:1511-1522. doi: 10.2147/JIR.S487595. eCollection 2025.
In primary healthcare, diagnosing rheumatoid arthritis (RA) is challenging due to a general lack of in-depth knowledge of RA by general practitioners (GPs) and the lack of effective tools, leading to high rates of missed diagnosis. This study focuses on a screening model for primary healthcare, aiming to improve early RA screening accuracy and efficiency at a relatively lower cost, reducing delays in GPs' recognition of RA.
We randomly selected 2106 participants from the RA group or combined control group (comprising healthy individuals and patients with non-RA rheumatic diseases) at Peking University Shenzhen Hospital as the developing cohort. Guided by experienced rheumatologists, we built a comprehensive database with 26 clinical features. Using 10 classical machine learning algorithms, we developed screening models. Evaluation metrics determined the best model. Employing multivariatelogistic regression results and the best-performing model to identify the least costly features, ensuring applicability in primary healthcare clinics. Subsequently, we retrained and validated our proposed model based on two primary healthcare validation cohorts.
In experiments, the algorithms achieved over 88% accuracy on training and test sets. Random Forest (RF) excelled with 96.20% (95% CI 95.39% to 97.02%) accuracy, 96.22% (95% CI 95.40% to 97.03%) specificity, 96.18% (95% CI 95.37% to 97.00%) sensitivity, and 96.20% (95% CI 95.39% to 97.02%) Areas Under Curves (AUC). A meticulous feature selection identified 11 key features for RA screening. In an external test on two primary healthcare datasets with these features, RF demonstrated an accuracy of 88.435% (95% CI 85.55% to 91.32%), sensitivity of 98.55% (95% CI 97.47% to 99.63%), specificity of 85.56% (95% CI 82.39% to 88.73%), and an AUC of 92.055% (95% CI 89.62% to 94.49%).
The screening model excels in automating prompt identification of RA in primary healthcare, improving the early detection of RA, and reducing delays and associated costs. Our findings contribute positively and are poised to elevate prospective RA management, fostering improvements in healthcare sector responsiveness and resource efficiency.
在基层医疗保健中，由于全科医生（GP）对类风湿性关节炎（RA）普遍缺乏深入了解且缺乏有效工具，导致RA的诊断具有挑战性，漏诊率很高。本研究聚焦于基层医疗保健的筛查模型，旨在以相对较低的成本提高RA早期筛查的准确性和效率，减少全科医生对RA识别的延迟。
我们从北京大学深圳医院的RA组或联合对照组（包括健康个体和非RA风湿性疾病患者）中随机选择2106名参与者作为开发队列。在经验丰富的风湿病学家的指导下，我们建立了一个包含26种临床特征的综合数据库。使用10种经典机器学习算法，我们开发了筛查模型。评估指标确定了最佳模型。利用多变量逻辑回归结果和表现最佳的模型来识别成本最低的特征，以确保在基层医疗诊所的适用性。随后，我们基于两个基层医疗验证队列对我们提出的模型进行了重新训练和验证。
在实验中，这些算法在训练集和测试集上的准确率超过88%。随机森林（RF）表现出色，准确率为96.20%（95%可信区间95.39%至97.02%），特异性为96.22%（95%可信区间95.40%至97.03%），敏感性为96.18%（95%可信区间95.37%至97.00%），曲线下面积（AUC）为96.20%（95%可信区间95.39%至97.02%）。经过细致的特征选择，确定了11个用于RA筛查的关键特征。在对具有这些特征的两个基层医疗数据集进行的外部测试中，RF的准确率为88.435%（95%可信区间85.55%至91.32%），敏感性为98.55%（95%可信区间97.47%至99.63%），特异性为85.56%（95%可信区间82.39%至88.73%），AUC为92.055%（95%可信区间89.62%至94.49%）。
该筛查模型在基层医疗保健中能出色地自动快速识别RA，提高RA的早期检测率，减少延迟和相关成本。我们的研究结果具有积极贡献，并有望提升RA的前瞻性管理，促进医疗保健部门响应能力和资源效率的提高。
