Evidence map›Paper›PMID 39617942›Full record

ArticleOrphanet journal of rare diseases2024

Nomogram for predicting pregnancy-related relapse of myasthenia gravis.

Manqiqige Su, Xiaoqing Liu, Zongtai Wu, Jie Song, Xiao Huan, Huahua Zhong, Rui Zhao, Chongbo Zhao, Yali Zhang, Sushan Luo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Orphanet journal of rare diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. 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

10 authors.

Manqiqige SuHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Xiaoqing LiuDepartment of Neurology, Chifeng Municipal Hospital, Chifeng, China.
Zongtai WuDepartment of Public Health and Primary Care, University of Cambridge, Cambridge, CB2 0BB, UK.
Jie SongHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Xiao HuanHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Huahua ZhongHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Rui ZhaoHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Chongbo ZhaoHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China.
Yali ZhangDepartment of Neurology, Chifeng Municipal Hospital, Chifeng, China. a8350028@126.com.
Sushan LuoHuashan Rare Disease Centre and Department of Neurology, Huashan Hospital, Shanghai Medical College, National Centre for Neurological Disorders, Fudan University, Shanghai, 200040, China. luosushan@fudan.edu.cn.ORCID 0000-0002-9033-7568

Funding

National Key research and development plan 2022YFC3501303National Key research and development plan 2022YFC3501305National Natural Science Foundation of China 82001335National Natural Science Foundation of China 82071410
6 · The paper itself

Abstract

backgroundMyasthenia gravis (MG) is an autoimmune disease mediated by autoantibodies primarily affecting the neuromuscular junction. This study aims to identify risk factors for pregnancy-related MG relapse and develop a predictive model to improve clinical outcomes.

methodsWe enrolled 113 MG female patients with a pregnancy history during follow-up at Huashan Hospital affiliated with Fudan University, between January 2015 and October 2021. The study analyzed relapse rates and risk factors during pregnancy and postpartum using multivariate logistic regression. A nomogram was constructed to predict relapse probability, with model performance evaluated by discrimination and calibration metrics.

resultsOf the 113 patients, 52 (46.02%) experienced 115 relapses, including 52 (45.22%) occurring during the first trimester of pregnancy, 11 (9.56%) during the second trimester of pregnancy, and 52 relapses (45.22%) during the three months after delivery/abortion. Significant factors associated with pregnancy-relate relapse, included age at delivery/abortion (OR 0.21, 95% CI 0.06-0.65), MG stable duration (OR 0.24, 95% CI 0.09-0.63), thymic hyperplasia (OR 3.45, 95% CI 1.35-9.3), pre-pregnancy thymectomy (OR 0.08, 95% CI 0.01-0.36), and inadequate treatment during pregnancy (OR 4.44, 95% CI 1.35-17.76). The Nomogram model demonstrated robust predictive performance.

conclusionThe first trimester of pregnancy and three months following delivery or abortion are high-risk periods for MG relapse. Younger ages, shorter MG stable duration before pregnancy, thymic hyperplasia, and inadequate treatments during pregnancy increase relapse risk.

Indexed as

Myasthenia GravisNomogramsRecurrenceAdultFemaleHumansPregnancyPregnancy ComplicationsRisk FactorsYoung AdultMyasthenia gravisNomogramPregnancyRisk factorsThymectomy

Identifiers

PMID39617942
PMCPMC11610150

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.