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Risk Prediction for Rapidly Progressive Interstitial Lung Disease in Anti-MDA5-Positive Dermatomyositis: The CRAFT Model

BACKGROUND: Anti-melanoma differentiation-associated protein 5-positive dermatomyositis (MDA5(+) DM) is characterized by a life-threatening complication of rapidly progressive interstitial lung disease (RP-ILD). Early prediction of RP-ILD can enhance diagnostic accuracy and therapeutic efficacy. Thi...

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Detalles Bibliográficos
Autores principales: Guo, Jinqiang, Mei, Chunli, Yu, Qi, Huang, Anbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10281065/
https://www.ncbi.nlm.nih.gov/pubmed/37317506
http://dx.doi.org/10.12659/MSM.940251
Descripción
Sumario:BACKGROUND: Anti-melanoma differentiation-associated protein 5-positive dermatomyositis (MDA5(+) DM) is characterized by a life-threatening complication of rapidly progressive interstitial lung disease (RP-ILD). Early prediction of RP-ILD can enhance diagnostic accuracy and therapeutic efficacy. This study was conducted to develop a nomogram model for predicting RP-ILD in patients with MDA5(+) DM. MATERIAL/METHODS: We retrospectively analyzed 53 patients with MDA5(+) DM, of whom 21 patients were diagnosed with RP-ILD between January 2018 and January 2021. Univariate analysis (t test, Mann-Whitney U test, chi-squared test, or Fisher’s exact test) and receiver operating characteristic (ROC) analysis were used to select candidate variables. Multivariate logistic regression analysis was conducted to construct a prediction model, which was subsequently transformed into a nomogram. ROC analysis, calibration curve and decision curve analysis were performed to evaluate the model’s performance. The bootstrapping method (resampling=500) was used for internal validation. RESULTS: We successfully established a nomogram, called the CRAFT model, to predict RP-ILD in MDA5(+) DM patients. The model included 4 variables, namely C-reactive protein-to-albumin ratio, red blood cell distribution width-coefficient of variation, fever status, and CD3(+) T cells. The model presented high predictive power and a good performance in calibration curve and decision curve analysis. In addition, the model had a good predictive ability in internal validation. CONCLUSIONS: The CRAFT model could help to predict RP-ILD in patients with MDA5(+) DM.