AUTHOR=Wang Liang , Du Lei , Li Qinying , Li Fang , Wang Bei , Zhao Yuanqi , Meng Qiang , Li Wenyu , Pan Juyuan , Xia Junhui , Wu Shitao , Yang Jie , Li Heng , Ma Jianhua , ZhangBao Jingzi , Huang Wenjuan , Chang Xuechun , Tan Hongmei , Yu Jian , Zhou Lei , Lu Chuanzhen , Wang Min , Dong Qiang , Lu Jiahong , Zhao Chongbo , Quan Chao 

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

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.947974

DOI=10.3389/fneur.2022.947974

ISSN=1664-2295

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 Cox proportional hazards (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 novel prediction model of relapse in NMOSD patients, and compare the performance with conventional Cox-PH model.
Methods: This retrospective cohort study included NMOSD patients with AQP4-ab in 10 researches centers. 1135 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 same therapy, EDSS score at treatment initiation, maintenance therapy, age at treatment initiation, disease duration, 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), LogisiticHazard, DeepHit and DeepSurv.
Results: When including all variables, RSF performed best of C-index in the training set (0.739), followed by DeepHit (0.737), LogisticHazard (0.722), DeepSurv (0.698) and Cox-PH (0.679) model. 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) model. Maintenance therapy was calculated to be the most important variable for relapse prediction.
Conclusion: This study confirmed the superiority of deep learning to design prediction model of relapse in AQP4-ab-positive NMOSD patients, with LogisticHazard model showing the best predictive power in validation.