Evidence map›Paper›PMID 39222463›Full record

ArticleAnnals of clinical and translational neurology2024

Clinical characteristics of double negative atypical inflammatory demyelinating disease: A prospective study.

Fei Jiang, Haobing Cai, Hongliang Li, Weifan Yin, Song Ouyang, Jue Hu, Ewen Tu, Ke Fu, Junjie Yin, Zhen Zhao and 3 more

Abstract read
In one paragraph

Article in Annals of clinical and translational neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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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.

Fei JiangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, 410000, Hunan, P.R. China.
Haobing CaiDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, 410000, Hunan, P.R. China.
Hongliang LiDepartment of Acupuncture and Tuina Rehabilitation, The First Hospital of Hunan University of Chinese Medicine, Changsha, 410000, Hunan, P.R. China.
Weifan YinDepartment of Neurology, The Second Xiangya Hospital, Central South University, Changsha, 410000, Hunan, P.R. China.
Song OuyangThe "Double-First Class" Application Characteristic Discipline of Hunan Province (Clinical Medicine) Changsha Medical University, Changsha, 410000, Hunan, P.R. China.
Jue HuDepartment of Neurology, Changsha Central Hospital, Changsha, 410000, Hunan, P.R. China.
Ewen TuDepartment of Neurology, Hunan Provincial Brain Hospital (Hunan Second People's Hospital), Changsha, 410000, Hunan, P.R. China.
Ke FuDepartment of Neurology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, 410000, Hunan, P.R. China.
Junjie YinDepartment of Neurology, Hunan University of Medicine General Hospital, Huaihua, 418000, Hunan, P.R. China.
Zhen ZhaoDepartment of Neurology, Zhuzhou Hospital Affiliated to Xiangya Medical College, Central South University, Zhuzhou, 412000, Hunan, P.R. China.
Jieyu YangDepartment of Social Work, Changsha Social Work College, Changsha, 410004, Hunan, P.R. China.
Qiuming ZengDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, 410000, Hunan, P.R. China.
Huan YangDepartment of Neurology, Xiangya Hospital, Central South University, Changsha, 410000, Hunan, P.R. China.ORCID 0000-0002-8690-2544

Funding

Changsha Municipal Natural Science Foundation kq2007037Ministry of Education 22YJA840015Municipal Natural Science FoundationNational Natural Science Foundation of China 82171399National Natural Science Foundation of China 82371413Natural Science Foundation of Hunan province, China 2022JJ40724Natural Science Foundation of Hunan province, China 2023JJ30062Natural Science Foundation of Hunan province, China 2023JJ30868Natural Science Foundation of Hunan province, China 2023JJ40502Science and Technology Innovation Guidance Project of Hunan Province 2020SK53009Scientific Research Fund of Hunan Provincial Education Department 22B0358
6 · The paper itself

Abstract

objectiveThis study aimed to investigate the clinical characteristics and predictors of relapse in double negative atypical inflammatory demyelinating disease (IDD) and to explore potential antigenic targets by tissue-based assays (TBA) using rat brain indirect immunofluorescence.

methodsWe compared the clinical, laboratory, and MRI data of double negative atypical IDD with other IDD patients. Serum samples were collected for TBA. The predictors of relapse were examined over a minimum of 24 months follow-up.

resultsIn our cohort of 98 patients with double negative atypical IDD, there was no significant female predominance (58.2%, 57/98). The lesions primarily affected the spinal cord and brain stem, with fewer cases of involvement in the area postrema (5.1%, 5/98) and longitudinally extensive transverse myelitis (43.9%, 43/98). A total of 62.5% (50/80) patients tested positive for anti-astrocyte antibodies based on rat brain TBA. Over a median duration of 39.5 months, 80 patients completed the entire follow-up, and 47.5% (38/80) patients exhibited monophasic course. A total of 36% (18/50) patients positively for anti-astrocyte antibodies had a monophasic course, which is significantly lower than patients negatively for anti-astrocyte antibodies (66.7%, 20/30) (p = 0.008). The presence of anti-astrocyte antibodies (hazard ratio (HR), 2.243; 95% CI, 1.087-4.627; p = 0.029) and ≥4 cerebrum lesions at first attack (HR, 2.494; 95% CI, 1.224-5.078; p = 0.012) were risk factors for disease relapse, while maintenance immunotherapy during remission (HR, 0.361; 95% CI, 0.150-0.869; p = 0.023) was protective factor.

interpretationDouble negative atypical IDD are unique demyelinating diseases with a high relapse rate. Maintenance immunotherapy is helpful to the prevention of relapse, particularly in patients with anti-astrocyte antibodies or ≥4 cerebrum lesions at first attack.

Indexed as

AutoantibodiesAdolescentAdultAnimalsBrainChildDemyelinating DiseasesFemaleFollow-Up StudiesHumansMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesRatsRecurrenceAutoantibodies

Identifiers

PMID39222463
PMCPMC11514904

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Registered trials

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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.