Evidence map›Paper›PMID 35989911›Full record

ArticleFrontiers in neurology2022

Deep learning-based relapse prediction of neuromyelitis optica spectrum disorder with anti-aquaporin-4 antibody.

Liang Wang, Lei Du, Qinying Li, Fang Li, Bei Wang, Yuanqi Zhao, Qiang Meng, Wenyu Li, Juyuan Pan, Junhui Xia and 16 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2022. 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
0.7field-weighted citation impact, top 30% of its field
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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Review
  2. 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

26 authors at 14 institutions in 1 country.

Liang WangDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Lei DuDepartment of Neurology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Qinying LiDepartment of Rehabilitation Medicine, Jing'an District Centre Hospital of Shanghai, Fudan University, Shanghai, China.
Fang LiNational Center for Neurological Disorders (NCND), Shanghai, China.
Bei WangDepartment of Neurology, Jing'an District Centre Hospital of Shanghai, Fudan University, Shanghai, China.
Yuanqi ZhaoDepartment of Neurology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Qiang MengDepartment of Neurology, The First People's Hospital of Yunnan Province, Kunming, China.
Wenyu LiDepartment of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Juyuan PanDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Junhui XiaDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Shitao WuDepartment of Neurology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jie YangDepartment of Neurology, Wuhan No.1 Hospital, Wuhan, China.
Heng LiDepartment of Neurology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China.
Jianhua MaDepartment of Neurology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China.
Jingzi ZhangBaoDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Wenjuan HuangDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Xuechun ChangDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Hongmei TanDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Jian YuDepartment of Ophthalmology and Vision Science, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Lei ZhouDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Chuanzhen LuDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Min WangDepartment of Ophthalmology and Vision Science, Eye and ENT Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Qiang DongDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Jiahong LuDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Chongbo ZhaoDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Chao QuanDepartment of Neurology, Huashan Rare Disease Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Huashan Hospital · CNFudan University · CNEye & ENT Hospital of Fudan University · CNJinshan Hospital of Fudan University · CNFifth Affiliated Hospital of Zhengzhou University · CNFirst Affiliated Hospital of Wenzhou Medical University · CNFirst Affiliated Hospital of Xinjiang Medical University · CNFirst People's Hospital of Yunnan Province · CNGuangzhou University of Chinese Medicine · CNJinan Central Hospital · CNWenzhou Medical University · CNWuhan No.1 Hospital · CNXinjiang Medical University · CNZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: We previously identified the independent predictors of recurrent relapse in neuromyelitis optica spectrum disorder (NMOSD) with anti-aquaporin-4 antibody (AQP4-ab) and designed a nomogram to estimate the 1- and 2-year relapse-free probability, using the Cox proportional hazard (Cox-PH) model, assuming that the risk of relapse had a linear correlation with clinical variables. However, whether the linear assumption fits real disease tragedy is unknown. We aimed to employ deep learning and machine learning to develop a novel prediction model of relapse in patients with NMOSD and compare the performance with the conventional Cox-PH model. Methods: This retrospective cohort study included patients with NMOSD with AQP4-ab in 10 study centers. In this study, 1,135 treatment episodes from 358 patients in Huashan Hospital were employed as the training set while 213 treatment episodes from 92 patients in nine other research centers as the validation set. We compared five models with added variables of gender, AQP4-ab titer, previous attack under the same therapy, EDSS score at treatment initiation, maintenance therapy, age at treatment initiation, disease duration, the phenotype of the most recent attack, and annualized relapse rate (ARR) of the most recent year by concordance index (C-index): conventional Cox-PH, random survival forest (RSF), LogisticHazard, DeepHit, and DeepSurv. Results: When including all variables, RSF outperformed the C-index in the training set (0.739), followed by DeepHit (0.737), LogisticHazard (0.722), DeepSurv (0.698), and Cox-PH (0.679) models. As for the validation set, the C-index of LogisticHazard outperformed the other models (0.718), followed by DeepHit (0.704), DeepSurv (0.698), RSF (0.685), and Cox-PH (0.651) models. Maintenance therapy was calculated to be the most important variable for relapse prediction. Conclusion: This study confirmed the superiority of deep learning to design a prediction model of relapse in patients with AQP4-ab-positive NMOSD, with the LogisticHazard model showing the best predictive power in validation.

Indexed as

anti-aquaporin-4 antibodydeep learningmachine learningneuromyelitis optica spectrum disorderrelapse prediction

Identifiers

PMID35989911
PMCPMC9389264
OpenAlexW4289868850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.