Evidence map›Paper›PMID 39948593›Full record

ArticleArthritis research & therapy2025

Clinical features and survival analysis of 40 cases of anti-MDA5 antibody-positive dermatomyositis complicated with interstitial lung disease.

Kaikai Zhao, Juan Zhang, Qunyu Kong, Yong Zhang, Cong Li, Kaikai Huo, Na Fan, Wenjing Deng, Jie Shi, Chunya Wang and 3 more

Abstract read
In one paragraph

Article in Arthritis research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
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  9. Review
  10. A case of rapidly progressive interstitial lung disease.Respiratory medicine case reports · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Kaikai Zhao *Department of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Juan Zhang *Department of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Qunyu KongChongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd., Chongqing, Chongqing, 401123, China.
Yong ZhangChongqing Nanpeng Artificial Intelligence Technology Research Institute Co., Ltd., Chongqing, Chongqing, 401123, China.
Cong LiDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Kaikai HuoDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Na FanDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Wenjing DengDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Jie ShiDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Chunya WangDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Xueyi LiDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Shuanying YangDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China.
Ping FangDepartment of Pulmonary and Critical Care Medicine, the Second Affiliated Hospital fo Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an, Shaanxi, 710004, China. pingf7613@xjtu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral studies have shown that patients with anti-MDA5 antibody-positive dermatomyositis (anti-MDA5 antibody + DM) have an increased risk of developing rapid progression of interstitial lung disease (RPILD), which is associated with poor prognosis and high mortality. However, diagnosis and treatment are often delayed due to atypical early clinical features and heterogeneity. Therefore, clinical features should be identified to establish a prognosis model for early identification and intervention, thereby improving the clinical prognosis of patients.

objectivesThe study aimed to investigate the clinical features, risk factors, treatment strategy, and construct a survival prognosis model for anti-MDA5 antibody + DM patients with ILD.

methodsA total of 40 anti-MDA5 antibody + DM-ILD patients admitted to the Department of Pulmonary and Critical Care Medicine and the Department of Rheumatology and Immunology in the Second Affiliated Hospital of Xi 'an Jiaotong University from September 2018 to May 2022 were retrospectively analyzed. Prognostic factors correlated with overall survival (OS) during hospitalization were identified by multivariate Cox regression analysis, and a nomogram was established. The nomogram was internally validated using C-index and time-dependent (at 1-, 2-, and 3- months) calibration curves with 1000 iterations of bootstrap resampling. Moreover, the optimal truncation values for continuous variables and Kaplan-Meier (K-M) curves were determined, which were used to analyze the difference in survival between groups. Finally, time-dependent decision curve analysis (DCA) was employed to validate the clinical value of the nomogram.

resultsSignificant differences were found between the survival group and the non-survival group in terms of age, oxygenation index, extent of lung lesions, diffuse alveolar damage (DAD) and nonspecific interstitial pneumonia (NSIP), and LDH, GLU, CEA, ferritin, CRP levels in serum (P < 0.05). Multivariate regression analysis revealed that increased NSIP in high-resolution computed tomography (HRCT) and ALT,LDH,CEA,CRP were risk factors for poor prognosis (P < 0.05). A nomogram diagram was constructed according to the final multiple Cox model to predict the 1-, 2-, and 3-month OS. According to ALT, AST, LDH, CEA, and CRP cutoff values, the KM algorithm was used to estimate the survival curve (P < 0.05). DCA curves were drawn for the model-dependent variables included treatment style, NSIP, ALT, AST, LDH, CEA, and CRP. This indicated that the nomogram yielded a higher net benefit compared to other single prognostic factors, and the cutoff value grouping model showed better practical application value. Combined treatment with glucocorticoids and immunosuppressants was a protective factor for long-term survival. Survival analysis indicated that patients with anti-MDA5 + DM-ILD could benefit from combined treatment for longer survival.

conclusionsAnti-MDA5 antibody + DM is prone to interstitial lung disease, poor prognosis, and high mortality. Risk prediction model could help us paying attention to these features which may allow the early identification of high-risk patients and promote timely diagnosis and treatment.

Indexed as

AutoantibodiesDermatomyositisInterferon-Induced Helicase, IFIH1Lung Diseases, InterstitialAdultAgedFemaleHumansMaleMiddle AgedNomogramsPrognosisRetrospective StudiesRisk FactorsSurvival AnalysisAutoantibodiesIFIH1 protein, humanInterferon-Induced Helicase, IFIH1Anti-MDA5 antibodyDermatomyositisInterstitial lung diseasePrognostic factors

Identifiers

PMID39948593
PMCPMC11823200

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.