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.

中文翻译：

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通过机器学习早期识别继发于系统性红斑狼疮的巨噬细胞激活综合征

继发于系统性红斑狼疮 (SLE) 的巨噬细胞激活综合征 (MAS) 是一种严重且危及生命的并发症。 MAS 的早期诊断尤其具有挑战性。在这项研究中，开发了机器学习模型和诊断评分卡，以帮助利用临床特征进行临床决策。我们回顾性收集了 188 名 SLE 或继发于 SLE 的 MAS 患者的临床数据。使用最小绝对收缩和选择算子 (LASSO) 筛选出 13 个显着的临床预测变量。这些变量随后被用作五个机器学习模型的输入。使用受试者工作特征曲线下面积 (ROC-AUC)、F1 评分和 F2 评分来评估模型的性能。为了提高临床可用性，我们开发了一种基于逻辑回归 (LR) 分析和卡方分箱的诊断评分卡，为该卡建立了概率阈值和分层。此外，本研究还收集了国内其他四家医院的数据进行外部验证。在所有机器学习模型中，LR模型在内部验证中表现出最高水平，ROC-AUC为0.998，F1分数为0.96，F2分数为0.952。我们构建的记分卡将概率阈值确定为 49，ROC-AUC 为 0.994，F2 分数为 0.936。根据诊断概率将评分结果分为五组：极低（低于 5%）、低（5-25%）、正常（25-75%）、高（75-95%）和极高（高于95%）。在外部验证过程中，性能评估显示，支持向量机（SVM）模型的AUC值为0.947，优于其他模型，记分卡模型的AUC为0.915。此外，我们还建立了一个在线评估系统，用于早期识别继发于 SLE 的 MAS。机器学习模型可以显着提高继发于 SLE 的 MAS 的诊断准确性，诊断记分卡模型可以促进临床环境中疾病发生的个性化概率预测。