Wang Kai, Tian Yian, Liu Shanshan, Zhang Zhongyuan, Shen Leilei, Meng Deqian, Li Ju
Department of Rheumatology, the Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huaian, 223001, People's Republic of China.
Pharmgenomics Pers Med. 2022 Sep 1;15:775-783. doi: 10.2147/PGPM.S369556. eCollection 2022.
Rapidly progressive interstitial lung disease (RP-ILD) is a significant complication that determines the prognosis of dermatomyositis (DM). Early RP-ILD diagnosis can improve screening and diagnostic efficiency and provide meaningful guidance to carry out early and aggressive treatment.
A retrospective screening of 284 patients with DM was performed. Clinical and laboratory characteristics of the patients were recorded. The risk factors of RP-ILD in DM patients were screened by logistic regression (LR) and machine learning methods, and the prediction models of RP-ILD were developed by machine learning methods, namely least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost).
According to the result of univariate LR, disease duration is a protective factor for RP-ILD, and ESR, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are risk factors for RP-ILD. The top 10 important variables of the 3 machine learning models were intersected to obtain common important variables, and there were 5 common important variables, namely disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody. The AUC of LASSO, RF and XGBoost test set were 0.661, 0.667 and 0.867, respectively. We further validated the performance of these three models on validation set, and the results showed that, the AUC of LASSO, RF and XGBoost were 0.764, 0.727 and 0.909, respectively. Based on the results of the models, XGBoost is the optimal model in this study.
Disease duration, LDH, CRP, anti-Ro-52 antibody and anti-MDA5 antibody are vital risk factors for RP-ILD in DM. The prediction model constructed using XGBoost can be used for risk identification and early intervention in DM patients with RP-ILD and practical application.
快速进展性间质性肺病（RP-ILD）是决定皮肌炎（DM）预后的重要并发症。早期诊断RP-ILD可提高筛查和诊断效率，并为开展早期积极治疗提供有意义的指导。
对284例DM患者进行回顾性筛查。记录患者的临床和实验室特征。采用逻辑回归（LR）和机器学习方法筛选DM患者发生RP-ILD的危险因素，并通过机器学习方法即最小绝对收缩和选择算子（LASSO）、随机森林（RF）和极端梯度提升（XGBoost）建立RP-ILD的预测模型。
单因素LR结果显示，病程是RP-ILD的保护因素，而血沉、C反应蛋白、抗Ro-52抗体和抗MDA5抗体是RP-ILD的危险因素。对3种机器学习模型的前10个重要变量进行交集运算，得到5个共同的重要变量，即病程、乳酸脱氢酶、C反应蛋白、抗Ro-52抗体和抗MDA5抗体。LASSO、RF和XGBoost测试集的AUC分别为0.661、0.667和0.867。我们在验证集上进一步验证了这3种模型的性能，结果显示，LASSO、RF和XGBoost的AUC分别为0.764、0.727和0.909。基于模型结果，XGBoost是本研究中的最优模型。
病程、乳酸脱氢酶、C反应蛋白、抗Ro-52抗体和抗MDA5抗体是DM患者发生RP-ILD的重要危险因素。利用XGBoost构建的预测模型可用于DM合并RP-ILD患者的风险识别和早期干预及实际应用。
