Table 1_Development of a machine learning-based predictive nomogram for screening children with juvenile idiopathic arthritis: a pseudo-longitudinal study of 223,195 children in the United States.docx

BackgroundJuvenile idiopathic arthritis (JIA) is a prevalent chronic rheumatological condition in children, with reported prevalence ranging from 12. 8 to 45 per 100,000 and incidence rates from 7.8 to 8.3 per 100,000 person-years. The diagnosis of JIA can be challenging due to its symptoms, such as joint pain and swelling, which can be similar to other conditions (e.g., joint pain can be associated with growth in children and adolescents).
MethodsThe National Survey of Children's Health (NSCH) database (2016–2021) of the United States was used in the current study. The NSCH database is funded by the Health Resources and Services Administration and Child Health Bureau and surveyed in all 50 states plus the District of Columbia. A total of 223,195 children aged 0 to 17 were analyzed in this study. A least absolute shrinkage and selection operator (LASSO) logistic regression and stepwise logistic regression were used to select the predictors, which were used to create the nomograms to predict JIA.
ResultsA total of 555 (248.7 per 100,000) JIA cases were reported in the NSCH. In the LASSO model, the receiver operating characteristic curve demonstrated excellent discrimination, with an area under the curve (AUC) of 0.9002 in the training set and 0.8639 in the validation set. Of the 16 variables selected by LASSO, 13 overlapped with those from the stepwise model. The regression achieved an AUC of 0.9130 in the training set and 0.8798 in the validation set. Sensitivity, specificity, and accuracy were 79.1%, 90.2%, and 90.2% in the training set, and 69.0%, 90.9%, and 90.8% in the validation set.
DiscussionUsing two well-validated predictor models, we developed nomograms for the early prediction of JIA in children based on the NSCH database. The tools are also available for parents and health professionals to utilize these nomograms. Our easy-to-use nomograms are not intended to replace the standard diagnostic methods. Still, they are designed to assist parents, clinicians, and researchers in better-estimating children's potential risk of JIA. We advise individuals utilizing our nomogram model to be mindful of potential pre-existing selection biases that may affect referrals and diagnoses.

【背景】幼年特发性关节炎（Juvenile idiopathic arthritis, JIA）是儿童群体中高发的慢性风湿性疾病，据报道其患病率介于每10万人12.8至45例，年发病率为每10万人年7.8至8.3例。由于该病的关节疼痛、肿胀等症状可与其他疾病表现相似（例如儿童及青少年的关节疼痛可能与生长发育相关），因此JIA的诊断颇具挑战性。
【方法】本研究采用美国2016-2021年全国儿童健康调查（National Survey of Children's Health, NSCH）数据库。该数据库由健康资源与服务管理局（Health Resources and Services Administration）与儿童健康局（Child Health Bureau）资助，调查覆盖美国全部50个州及哥伦比亚特区。本研究共纳入223195名0至17岁儿童进行分析。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）逻辑回归与逐步逻辑回归筛选预测因子，并据此构建列线图以预测JIA发病风险。
【结果】NSCH数据库中共报告555例JIA病例（患病率为每10万人248.7例）。在LASSO模型中，受试者工作特征曲线（receiver operating characteristic curve, ROC）展现出优异的区分效能，训练集的曲线下面积（area under the curve, AUC）为0.9002，验证集为0.8639。在LASSO筛选出的16个变量中，有13个与逐步回归模型筛选得到的变量重合。该回归模型在训练集的AUC为0.9130，验证集为0.8798。训练集的灵敏度、特异度与准确率分别为79.1%、90.2%及90.2%，验证集则分别为69.0%、90.9%及90.8%。
【讨论】本研究基于NSCH数据库，通过两种经过充分验证的预测模型，构建了可用于儿童JIA早期预测的列线图。此类工具可供家长与医疗专业人员使用。本研究开发的易用型列线图并非旨在替代标准诊断流程，而是用于协助家长、临床医师及研究人员更好地评估儿童罹患JIA的潜在风险。我们提醒使用本列线图模型的使用者，需留意可能影响转诊与诊断的潜在预先选择偏倚。