医学
皮肌炎
比例危险模型
内科学
Lasso(编程语言)
弗雷明翰风险评分
接收机工作特性
肿瘤科
疾病
万维网
计算机科学
作者
Chen Zong,Shiyu Wu,Longyang Zhu,Yiran Chen,Xinxin Zhang,Chao Sun,Xin Lü,Guochun Wang,Qinglin Peng
摘要
ABSTRACT Background and Objective Anti‐melanoma differentiation‐associated gene 5‐positive dermatomyositis (MDA5 + DM) exhibits the worst prognosis among all subtypes of idiopathic inflammatory myopathies, with substantial heterogeneity in patient outcomes. This study aimed to investigate prognostic factors for MDA5 + DM and develop a scoring system to determine mortality risk. Methods This retrospective study included 621 patients with MDA5 + DM. Variables were selected using univariable Cox regression and LASSO regression. Predictive models for mortality risks were constructed using machine learning‐based algorithms. A simplified scoring system was established based on the optimal model with thorough validation to ensure predictive accuracy. Results Seven variables emerged as key factors associated with mortality in MDA5 + DM and incorporated into the mortality risk prediction model: ferritin, lactate dehydrogenase, age at onset, CD8+ T‐cell count, C‐reactive protein, albumin, and lung computed tomography pattern of NSIP + OP. Among six models, the Cox proportional hazards model demonstrated superior discriminative ability and clinical utility and was translated into a simplified scoring system ‘FLATCAN’. This model achieved a concordance index of 0.815 and time‐dependent area under the receiver operating characteristic curves for predicting 3‐, 6‐, and 12‐month mortality of 0.895, 0.855, and 0.850, respectively. Patients were effectively stratified into low‐, intermediate‐, and high‐risk groups using the FLATCAN score. Further internal cross‐validation, time‐point splitting, and rapidly progressive interstitial lung disease‐based splitting confirmed the FLATCAN score's robust predictive ability. Conclusion The FLATCAN score provides an easy‐to‐use tool for predicting mortality risk in patients with MDA5 + DM and may facilitate improved risk stratification‐based patient management.
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