A nomogram for the prediction of co-infection in MDA5 dermatomyositis : A rapid clinical assessment model

Bibliographische Detailangaben
Veröffentlicht in:	Clinical immunology (Orlando, Fla.). - 1999. - 272(2025) vom: 15. März, Seite 110431
1. Verfasser:	Wu, Yinlan (VerfasserIn)
Weitere Verfasser:	Li, Yanhong, Zhou, Yu, Luo, Yubin, Cheng, Lu, Zhao, Jing, Huang, Deying, Ma, Ling, Wu, Tong, Liang, Xiuping, Liao, Zehui, Tan, Chunyu, Liu, Yi
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2025
Zugriff auf das übergeordnete Werk:	Clinical immunology (Orlando, Fla.)
Schlagworte:	Journal Article Anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5 DM) Infection Nomogram Prediction model Interferon-Induced Helicase, IFIH1 EC 3.6.4.13 IFIH1 protein, human EC 3.6.1.-

Beschreibung
Zusammenfassung:	Copyright © 2025 Elsevier Inc. All rights reserved. BACKGROUND: Patients with anti-melanoma differentiation-associated gene 5-positive dermatomyositis (MDA5 DM) are prone to infections, but there is a lack of rapid methods to assess infection risk, which greatly affects patient prognosis. This study aims to analyze the clinical features of MDA5 DM patients systematically and develop a predictive model for infections METHODS: Retrospective analysis was performed on clinical data from 118 hospitalized patients with MDA5 DM. According to the results of pathogen detection and clinical manifestations, the patients were divided into infected group and non-infected group. LASSO analysis and multivariate logistic regression were used to establish the prediction model of infection in MAD5 DM patients. The resulting model was visualized using a Nomogram. We used methods such as Receiver Operating Characteristic (ROC) curve analysis, Area Under the Curve (AUC) calculation to evaluate the model RESULT: The Cough, interstitial lung disease, moist rales, positive anti-RO-52, carcinoembryonic antigen, triglyceride, hydroxybutyrate dehydrogenase and erythrocyte sedimentation rate were significantly associated with infection risk in MDA5 DM patients. A prediction model was developed using these eight risk factors, achieving an AUC of 0.851 in determining co-infection status. Further analysis based on infection site and pathogen classification demonstrated strong discrimination performance of the model in identifying pulmonary infection (AUC: 0.844) and fungal infection (AUC: 0.822) CONCLUSION: This study aimed to develop a clinical prediction model and visualize it using Nomogram to assess the risk of infection in MDA5 DM. The model provides an effective tool for determining infection status in patients and serves as a reference for formulating clinical medication regimens
Beschreibung:	Date Completed 03.05.2025 Date Revised 03.05.2025 published: Print-Electronic Citation Status MEDLINE
ISSN:	1521-7035
DOI:	10.1016/j.clim.2025.110431