Department of Rheumatology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Rheumatology and Immunology, the Second Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
Arthritis Res Ther. 2024 May 9;26(1):92. doi: 10.1186/s13075-024-03330-9.
The macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics.
We retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation.
Among all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5-25%), normal (25-75%), high (75-95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE.
Machine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.
系统性红斑狼疮（SLE）继发巨噬细胞活化综合征（MAS）是一种严重且危及生命的并发症。早期诊断 MAS 极具挑战性。本研究旨在利用临床特征，通过机器学习模型和诊断评分卡辅助临床决策。
我们回顾性收集了 188 例 SLE 或 SLE 继发 MAS 患者的临床资料。使用最小绝对收缩和选择算子（LASSO）筛选出 13 个有意义的临床预测变量。随后，将这些变量作为输入应用于 5 种机器学习模型中。使用受试者工作特征曲线下面积（ROC-AUC）、F1 评分和 F2 评分评估模型性能。为了提高临床实用性，我们基于逻辑回归（LR）分析和卡方分箱法制定了诊断评分卡，并确定了评分卡的概率阈值和分层。此外，本研究还从国内其他 4 家医院收集数据进行外部验证。
在所有机器学习模型中，LR 模型在内部验证中表现最佳，ROC-AUC 为 0.998，F1 评分为 0.96，F2 评分为 0.952。我们构建的评分卡确定评分 49 分为概率阈值，ROC-AUC 为 0.994，F2 评分为 0.936。根据诊断概率将评分结果分为五组：极低（<5%）、低（5%-25%）、正常（25%-75%）、高（75%-95%）和极高（>95%）。在外部验证中，支持向量机（SVM）模型的性能评估结果优于其他模型，AUC 值为 0.947，评分卡模型的 AUC 值为 0.915。此外，我们还建立了一个 SLE 继发 MAS 的早期识别在线评估系统。
机器学习模型可显著提高 SLE 继发 MAS 的诊断准确性，诊断评分卡模型有助于在临床环境中对疾病发生进行个性化概率预测。
