A novel CT scoring method predicts the prognosis of interstitial lung disease associated with anti-MDA5 positive dermatomyositis

Anti-MDA5 autoantibodies predict clinical dynamics of dermatomyositis following SARS-CoV-2 mRNA vaccination: a retrospective statistical analysis of case reports

Since the introduction of mRNA vaccines against SARS-CoV-2, the induction of autoimmunity by mRNA vaccination has been discussed. Several cases of dermatomyositis (DM) associated with mRNA vaccination against SARS-CoV-2 infection have been reported. The question is whether there is a common pathomechanism for the induction of DM by this mRNA vaccination. The aim of this review is to analyse the sample of previously published case reports of DM following COVID-19 mRNA vaccination for common indicators of a possible immune pathomechanism.In this review, we summarised case reports of DM following mRNA vaccination against COVID-19. We considered this case report landscape as a cumulative sample (n = 32) and identified common clinical and molecular parameters in the intersection of case reports and statistically analysed the effect of these parameters on the development of DM.MDA-5 antibodies seem to play a role in the autoantibody signature after mRNA vaccination. MDA-5-positive DM is statistically more strongly associated with mRNA vaccination and interstitial lung disease/rapidly progressive interstitial lung disease (ILD/RP-ILD) than MDA-5-negative DM. MDA-5-positive DM seems not to be associated with an increased risk of malignancy, whereas MDA-5-negative DM is more strongly associated with malignancy.Our findings emphasize the potential role of innate antiviral signalling pathways in connecting DM to mRNA vaccination. MDA-5 autoantibodies appear to be predictive of a severe DM progression following mRNA vaccination. There seems to be an association between MDA-5 autoantibodies and paraneoplastic DM post-vaccination. Further studies are required to uncover the underlying mechanisms of autoimmunity triggered by mRNA vaccination.

Lung disease prediction based on CT images using REInf-net and world cup optimization based BI-LSTM classification

A major global source of disability as well as mortality is respiratory illness. Though visual evaluation of computed tomography (CT) images and chest radiographs are a primary diagnostic for respiratory illnesses, it is limited in its ability to assess severity and predict patient outcomes due to low specificity and fundamental infectious organisms. In order to address these problems, world cup optimization-based Bi-LSTM classification and lung disease prediction on CT images using REINF-net were employed. To enhance the image quality, the gathered lung CT images are pre-processed using Lucy Richardson and CLAHE algorithms. For the purpose of lung infection segmentation, the pre-processed images are segmented using the REInf-net. The GLRLM method is used to extract features from the segmented images. In order to predict lung disease in CT images, the extracted features are trained using the Bi-LSTM based on world cup optimization. Accuracy, Precision, recall, Error and Specificity for the proposed model are 97.8%, 96.7%, 96.7%, 2.2% and 98.3%. These evaluated values are contrasted with the results of existing methods like WCO-BiLSTM, MLP, CNN and LSTM. Finally, the Lung disease prediction based on CT images using REINF-Net and world cup optimization based BI-LSTM classification performs better than the existing model.

The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more

Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and the practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.

Radiomics based on HRCT can predict RP-ILD and mortality in anti-MDA5 + dermatomyositis patients: a multi-center retrospective study

OBJECTIVES

To assess the effectiveness of HRCT-based radiomics in predicting rapidly progressive interstitial lung disease (RP-ILD) and mortality in anti-MDA5 positive dermatomyositis-related interstitial lung disease (anti-MDA5 + DM-ILD).

METHODS

From August 2014 to March 2022, 160 patients from Institution 1 were retrospectively and consecutively enrolled and were randomly divided into the training dataset (n = 119) and internal validation dataset (n = 41), while 29 patients from Institution 2 were retrospectively and consecutively enrolled as external validation dataset. We generated four Risk-scores based on radiomics features extracted from four areas of HRCT. A nomogram was established by integrating the selected clinico-radiologic variables and the Risk-score of the most discriminative radiomics model. The RP-ILD prediction performance of the models was evaluated by using the area under the receiver operating characteristic curves, calibration curves, and decision curves. Survival analysis was conducted with Kaplan-Meier curves, Mantel-Haenszel test, and Cox regression.

RESULTS

Over a median follow-up time of 31.6 months (interquartile range: 12.9-49.1 months), 24 patients lost to follow-up and 46 patients lost their lives (27.9%, 46/165). The Risk-score based on bilateral lungs performed best, attaining AUCs of 0.869 and 0.905 in the internal and external validation datasets. The nomogram outperformed clinico-radiologic model and Risk-score with AUCs of 0.882 and 0.916 in the internal and external validation datasets. Patients were classified into low- and high-risk groups with 50:50 based on nomogram. High-risk group patients demonstrated a significantly higher risk of mortality than low-risk group patients in institution 1 (HR = 4.117) and institution 2 cohorts (HR = 7.515).

CONCLUSION

For anti-MDA5 + DM-ILD, the nomogram, mainly based on radiomics, can predict RP-ILD and is an independent predictor of mortality.

Multivariate logistic regression analysis of poor prognosis of dermatomyositis and clinical value of ferritin/Kl-6 in predicting prognosis

BACKGROUND

Dermatomyositis (DM) is a rare inflammatory disease. Our research focuses on predicting poor prognosis in DM patients and evaluating the prognostic significance of ferritin and Salivary Sugar Chain Antigen-6 (KL-6) through multivariate logistic regression analysis.

METHODS

Between February 2018 and April 2020, 80 DM patients at our hospital were categorized into MDA5 positive (n = 20) and negative (n = 60) groups. We conducted multivariate logistic regression to determine DM's poor prognosis risk factors and evaluate ferritin/KL-6's predictive value for prognosis.

RESULTS

Analysis showed no gender, age, body mass index (BMI), or lifestyle (smoking, drinking) differences, nor in dyspnea, muscle weakness, skin ulcers, and acetylcysteine treatment effects (p > 0.05). Significant differences emerged in arrhythmias, interstitial pneumonia, C-reactive protein, albumin, and lactate dehydrogenase levels (p < 0.05). Before treatment, differences were negligible (p > 0.05), but post-treatment, serum KL-6 and ferritin levels dropped. MDA5 positive patients had elevated serum KL-6 and ferritin levels than survivors (p < 0.05), with a strong correlation to DM. Combined diagnosis using serum KL-6 and ferritin for DM prognosis showed area under curves of 0.716 and 0.634, significantly outperforming single-index diagnoses with an area under curve (AUC) of 0.926 (p < 0.05).

CONCLUSION

Serum KL-6 and ferritin show marked abnormalities in DM, useful as indicators for evaluating polymyositis and DM conditions. However, the study's small sample size is a drawback. Expanding the sample size is essential to monitor serum KL-6 and ferritin changes in DM patients under treatment more closely, aiming to improve clinical assessment and facilitate detailed research.

In vitro PK/PD modeling of tyrosine kinase inhibitors in non-small cell lung cancer cell lines

Tyrosine kinase inhibitors (TKIs) are routinely prescribed for the treatment of non-small cell lung cancer (NSCLC). As with all medications, patients can experience adverse events due to TKIs. Unfortunately, the relationship between many TKIs and the occurrence of certain adverse events remains unclear. There are limited in vivo studies which focus on TKIs and their effects on different regulation pathways. Many in vitro studies, however, that investigate the effects of TKIs observe additional changes, such as changes in gene activations or protein expressions. These studies could potentially help to gain greater understanding of the mechanisms for TKI induced adverse events. However, in order to utilize these pathways in a pharmacokinetic/pharmacodynamic (PK/PD) framework, an in vitro PK/PD model needs to be developed, in order to characterize the effects of TKIs in NSCLC cell lines. Through the use of ordinary differential equations, cell viability data and nonlinear mixed effects modeling, an in vitro TKI PK/PD model was developed with estimated PK and PD parameter values for the TKIs alectinib, crizotinib, erlotinib, and gefitinib. The relative standard errors for the population parameters are all less than 25%. The inclusion of random effects enabled the model to predict individual parameter values which provided a closer fit to the observed response. It is hoped that this model can be extended to include in vitro data of certain pathways that may potentially be linked with adverse events and provide a better understanding of TKI-induced adverse events.

A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD

OBJECTIVES

Anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) DM complicated by rapidly progressive interstitial lung disease (RP-ILD) has a high incidence and poor prognosis. The objective of this study was to establish a model for the prediction and early diagnosis of anti-MDA5+ DM-associated RP-ILD based on clinical manifestations and imaging features.

METHODS

A total of 103 patients with anti-MDA5+ DM were included. The patients were randomly split into training and testing sets of 72 and 31 patients, respectively. After image analysis, we collected clinical, imaging and radiomics features from each patient. Feature selection was performed first with the minimum redundancy and maximum relevance algorithm and then with the best subset selection method. The final remaining features comprised the radscore. A clinical model and imaging model were then constructed with the selected independent risk factors for the prediction of non-RP-ILD and RP-ILD. We also combined these models in different ways and compared their predictive abilities. A nomogram was also established. The predictive performances of the models were assessed based on receiver operating characteristics curves, calibration curves, discriminability and clinical utility.

RESULTS

The analyses showed that two clinical factors, dyspnoea (P = 0.000) and duration of illness in months (P = 0.001), and three radiomics features (P = 0.001, 0.044 and 0.008, separately) were independent predictors of non-RP-ILD and RP-ILD. However, no imaging features were significantly different between the two groups. The radiomics model built with the three radiomics features performed worse than the clinical model and showed areas under the curve (AUCs) of 0.805 and 0.754 in the training and test sets, respectively. The clinical model demonstrated a good predictive ability for RP-ILD in MDA5+ DM patients, with an AUC, sensitivity, specificity and accuracy of 0.954, 0.931, 0.837 and 0.847 in the training set and 0.890, 0.875, 0.800 and 0.774 in the testing set, respectively. The combination model built with clinical and radiomics features performed slightly better than the clinical model, with an AUC, sensitivity, specificity and accuracy of 0.994, 0.966, 0.977 and 0.931 in the training set and 0.890, 0.812, 1.000 and 0.839 in the testing set, respectively. The calibration curve and decision curve analyses showed satisfactory consistency and clinical utility of the nomogram.

CONCLUSION

Our results suggest that the combination model built with clinical and radiomics features could reliably predict the occurrence of RP-ILD in MDA5+ DM patients.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

Anti-MDA5 dermatomyositis: an update from bench to bedside

PURPOSE OF REVIEW

This review summarizes the recent developments about anti-MDA5 antibody positive dermatomyositis with a focus on its pathogenesis, clinical features and treatment options of rapidly progressive interstitial lung disease, its most ominous complication.

RECENT FINDINGS

Anti-MDA5+ dermatomyositis has a heterogeneous clinical spectrum with different patient subsets exhibiting widely different outcomes; severe acute interstitial lung disease is the main factor impacting prognosis. The pathogenetic role of anti-MDA5 antibodies is an active area of investigation.

SUMMARY

Anti-MDA5+ dermatomyositis has a wider spectrum of manifestations than previously thought. A high index of suspicion is needed not to miss atypical presentations. In the setting of acute interstitial lung involvement, once a confident diagnosis is made, an aggressive approach with early combined immunosuppression affords the best chances of survival.

18F-FDG PET/CT and HRCT: a combined tool for risk stratification in idiopathic inflammatory myopathy-associated interstitial lung disease

OBJECTIVES

Rapidly progressive interstitial lung disease (RP-ILD) is a life-threatening form of idiopathic inflammatory myopathy (IIM)-associated interstitial lung disease (ILD). We aimed to assess the combination of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) and high-resolution computed tomography (HRCT) for the quantification of IIM-ILD activity and risk stratification for RP-ILD.

METHOD

Patients with IIM and undergoing ¹⁸F-FDG PET/CT were included in this retrospective study. Pulmonary FDG uptake was assessed using the maximum standardized uptake value (SUV_lung) and visual score (PET score). HRCT was evaluated using visual analysis (HRCT score). Multivariable logistic regression was used to identify risk factors for RP-ILD.

RESULTS

Seventy-three patients with IIM (17 with RP-ILD, 38 with non-RP-ILD, and 18 without ILD) were included. SUV_lung, PET score, and HRCT score were significantly higher in RP-ILD than in non-RP-ILD. Strong positive correlations were observed between SUV_lung, PET score, and the HRCT parameters. The area under the curve (AUC) of the PET score to differentiate between RP-ILD and non-RP-ILD (AUC = 0.860) was higher than that of the SUV_lung (AUC = 0.802) and HRCT scores (AUC = 0.806). We developed a risk score based on the number of positive risk factors (PET score > 18, HRCT score > 140, and positive anti-melanoma differentiation-associated gene 5 (MDA5) antibody) to differentiate between RP-ILD and non-RP-ILD (AUC = 0.955). Patients with higher risk scores had significantly worse prognoses.

CONCLUSIONS

¹⁸F-FDG PET/CT is useful for assessing disease activity in patients with IIM-ILD. The combination of PET score, HRCT score, and anti-MDA5 antibody can be used to identify patients at increased risk of RP-ILD and with poor prognoses.

A New Predictive Model for the Prognosis of MDA5+ DM-ILD

Purpose

The purpose of this study is to analyze clinical information and combine significant parameters to generate a predictive model and achieve a better prognosis prediction of dermatomyositis-associated interstitial lung disease with positive melanoma differentiation-associated gene 5 antibody (MDA5+ DM-ILD) and stratify patients according to prognostic risk factors appropriately.

Methods

We retrospectively reviewed 63 patients MDA5+ DM-ILD who were treated in our hospital from January 2018 to January 2021. Our study incorporated most clinical characteristics in clinical practice to explore the associations and predictive functions of clinical characteristics and prognosis. Student's t-test, Mann-Whitney U-test, chi-squared test, Pearson correlation analysis, Cox regression analysis, R, receiver operating characteristic curves (ROC curves), and Kaplan-Meier survival curves were performed to identify independent predictors for the prognosis of MDA5+DM-ILD.

Results

In all the 63 patients with MDA5+DM-ILD, 44 improved but 19 did not. Poor prognosis was found more frequently in patients who were older, clinically amyopathic variant of dermatomyositis (CADM), and/or with short duration, short interval of DM and ILD, long length of stay, fever, dyspnea, non-arthralgia, pulmonary infection, pleural effusion (PE), high total computed tomography scores (TCTs), ground-glass opacity (GGO), consolidation score, reticular score and fibrosis score, decreased forced vital capacity (FVC), forced expiratory volume in 1s (FEV1), albumin, A/G, glomerular filtration rate (GFR) and tumor necrosis factor α (TNFα), high titer of anti-MDA5, proteinuria, high levels of monocyte, lactate dehydrogenase (LDH), ferritin (FER), neuron specific enolase (NSE) and glucocorticoid, antibiotic, antiviral, and non-invasive positive pressure ventilation (NPPV). The multivariate Cox regression analysis demonstrated that duration, fever, PE, TCTs and aspartate transaminase (AST) were independent predictors of poor prognosis in patients with MDA5+DM-ILD. The nomogram model quantified the risk of 400-day death as: duration ≤ 4 months (5 points), fever (88 points), PE (21 points), TCTs ≥10 points (22 points), and AST ≥200 U/L (100 points) with high predictive accuracy and convenience. The ROC curves possessed good discriminative ability for combination of fever, PE, TCTs, and AST, as reflected by the area under curve (AUC) being.954, 95% CI 0.902-1.000, and sensitivity and specificity being 84.2 and 94.6%, respectively.

Conclusion

We demonstrated that duration, fever, PE, TCTs, and AST could be integrated together to be independent predictors of poor prognosis in MDA5+ DM-ILD with highly predictive accuracy.

A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases

OBJECTIVE

To evaluate the response to treatment with intravenous (IVIg) and subcutaneous (20%SCIg) immunoglobulin in our series of patients with Inflammatory idiopathic myopathies (IIM) by the means of artificial intelligence.

BACKGROUND

IIM are rare diseases mainly involving the skeletal muscle with particular clinical, laboratory and radiological characteristics. Artificial intelligence (AI) represents computer processes which allows to perform complex calculations and data analyses, with the least human intervention. Recently, the use an AI in medicine significantly expanded, especially through machine learning (ML) which analyses huge amounts of information and accordingly makes decisions, and deep learning (DL) which uses artificial neural networks to analyse data and automatically learn.

METHODS

In this study, we employed AI in the evaluation of the response to treatment with IVIg and 20%SCIg in our series of patients with IIM. The diagnoses were determined on the established EULAR/ACR criteria. The treatment response was evaluated employing the following: serum creatine kinase levels, muscle strength (MMT8 score), disease activity (MITAX score) and disability (HAQ-DI score). We evaluated all the above parameters, applying, with R, different supervised ML algorithms, including Least Absolute Shrinkage and Selection Operator, Ridge, Elastic Net, Classification and Regression Trees and Random Forest to estimate the most important predictors for a good response to IVIg and 20%SCIg treatment.

RESULTS AND CONCLUSION

By the means of AI we have been able to identify the scores that best predict a good response to IVIg and 20%SCIg treatment. The muscle strength as evaluated by MMT8 score at the follow-up is predicted by the presence of dysphagia and of skin disorders, and the myositis activity index (MITAX) at the beginning of the treatment. The relationship between muscle strength and MITAX indicates a better action of IVIg therapy in patients with more active systemic disease. Considering our results, Elastic Net and similar approaches were seen to be the most viable, efficient, and effective ML methods for predicting the clinical outcome (MMT8 and MITAX at most) in myositis.

Low Circulating Monocytes Is in Parallel With Lymphopenia Which Predicts Poor Outcome in Anti-melanoma Differentiation-Associated Gene 5 Antibody-Positive Dermatomyositis-Associated Interstitial Lung Disease

Anti-melanoma differentiation-associated gene 5 (MDA5) antibody-positive dermatomyositis (DM)-associated interstitial lung disease (ILD) may progress rapidly and lead to high mortality within 6 or 12 months. Except for reported prognostic factors, simple but powerful prognostic biomarkers are still in need in practice. In this study, we focused on circulating monocyte and lymphocyte counts and their variation tendency in the early stage of ILD. A total of 351 patients from two inception anti-MDA5 antibody-positive cohorts were included in this study, with various treatment choices. Lymphocyte count remained lower in the first month after admission in the non-survivor patients. Although baseline monocyte count showed no significant differences, average monocyte count in the following 4 weeks was also lower in the non-survivor group. Based on the C-index and analysis by the “survminer” R package in the discovery cohort, we chose 0.24 × 109/L as the cutoff value for Mono W0-2, 0.61 × 109/L as the cutoff value for lymph W0-2, and 0.78 × 109/L as the cutoff value for peripheral blood mononuclear cell (PBMC) W0-2, to predict the 6-month all-cause mortality. The Kaplan–Meier survival curves and adjusted hazard ratio with age, gender, and the number of immunosuppressants used all validated that patients with lower average monocyte count, lower average lymphocyte count, or lower average PBMC count in the first 2 weeks after admission had higher 6-month death risk, no matter in the validation cohort or in the pooled data. Furthermore, flow cytometry figured out that non-classical monocytes in patients with anti-MDA5 antibody-positive DM were significantly lower than healthy controls and patients with DM without anti-MDA5 antibodies. In conclusion, this study elucidated the predictive value of monocyte and lymphocyte counts in the early stage and may help rheumatologists to understand the possible pathogenesis of this challenging disease.

A Computed Tomography Radiomics-Based Prediction Model on Interstitial Lung Disease in Anti-MDA5-Positive Dermatomyositis

Objectives: Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5⁺ DM-ILD) is a life-threatening disease. The current study aimed to quantitatively assess the pulmonary high-resolution computed tomography (HRCT) images of MDA5⁺ DM-ILD by applying the radiomics approach and establish a multidimensional risk prediction model for the 6-month mortality.

Methods: This retrospective study was conducted in 228 patients from two centers, namely, a derivation cohort and a longitudinal internal validation cohort in Renji Hospital, as well as an external validation cohort in Guangzhou. The derivation cohort was randomly divided into training and testing sets. The primary outcome was 6-month all-cause mortality since the time of admission. Baseline pulmonary HRCT images were quantitatively analyzed by radiomics approach, and a radiomic score (Rad-score) was generated. Clinical predictors selected by univariable Cox regression were further incorporated with the Rad-score, to enhance the prediction performance of the final model (Rad-score plus model). In parallel, an idiopathic pulmonary fibrosis (IPF)-based visual CT score and ILD-GAP score were calculated as comparators.

Results: The Rad-score was significantly associated with the 6-month mortality, outperformed the traditional visual score and ILD-GAP score. The Rad-score plus model was successfully developed to predict the 6-month mortality, with C-index values of 0.88 [95% confidence interval (CI), 0.79–0.96] in the training set (n = 121), 0.88 (95%CI, 0.71–1.0) in the testing set (n = 31), 0.83 (95%CI, 0.68–0.98) in the internal validation cohort (n = 44), and 0.84 (95%CI, 0.64–1.0) in the external validation cohort (n = 32).

Conclusions: The radiomic feature was an independent and reliable prognostic predictor for MDA5⁺ DM-ILD.

The Value of Effective Lung Ventilation Area Ratio Based on CT Image Analysis Is a New Index to Predict the Shorter Outcome of Anti-melanoma Differentiation-Associated Protein 5 Positive Dermatomyositis Associated Interstitial Lung Disease: A Single-Center Retrospective Study

Background: Anti-melanoma differentiation-associated protein 5 (MDA5) positive dermatomyositis (MDA5⁺DM) patients have poor outcomes due to rapidly progressive interstitial lung disease (ILD). The accurate assessment of lung involvement is an urgent focus of research. Methods: A computer-aided lung interstitial image analysis technology has been developed, and a quantitative indicator named effective lung ventilation area ratio (ELVAR) that calculates the proportion of the area outside the lung interstitium in lung tissue has been established. 55 newly diagnosed MDA5⁺DM patients and 46 healthy individuals, matched for age and gender, were enrolled in this study. MDA5⁺DM patients were classified into early death group or early survival group according to their survival state within 3 months after diagnosis. Clinical characteristics, laboratory and immunological test results, lung involvement (including ELVAR value) and treatment were compared between early death group and early survival group to determine an index that can predict prognoses of patients with MDA5⁺DM. Results: There were significant differences between early death MDA5⁺DM patients and early survival MDA5⁺DM patients about 12 indices including age of onset, CRP, ferritin, albumin, and pulmonary involvement including severity of type I respiratory failure at diagnosis, P/F ratio, oxygen supplementation, values of ELVAR, FVC, and DLCO. The results of ROC analysis and correlation analysis showed the value of ELVAR had good diagnostic value and widely correlation with many clinical characteristics. Univariate analysis and Multivariate analysis showed four factors including age of onset, ferritin, value of ELVAR, and oxygen supplementation >4 L/min significantly value for poor prognosis in MDA5⁺DM patients. A cutoff value of 0.835 about ELVAR had good predictive power for mortality within 3 months in 54.2% of MDA5⁺DM patients. Conclusion: The value of ELVAR derived from computed tomography image analysis is a new index that can predict poor outcomes in MDA5⁺DM patients with rapidly progressive interstitial lung disease.