{"title":"用简单的评分(MOG-AR)来识别 MOGAD 攻击后复发风险高的个体。","authors":"Yun Xu,Huaxing Meng,Moli Fan,Linlin Yin,Jiali Sun,Yajun Yao,Yuzhen Wei,Hengri Cong,Huabing Wang,Tian Song,Chun-Sheng Yang,Jinzhou Feng,Fu-Dong Shi,Xinghu Zhang,De-Cai Tian","doi":"10.1212/nxi.0000000000200309","DOIUrl":null,"url":null,"abstract":"BACKGROUND AND OBJECTIVES\r\nTo identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to develop and validate a simple risk score for predicting relapse.\r\n\r\nMETHODS\r\nIn China National Registry of Neuro-Inflammatory Diseases (CNRID), we identified patients with MOGAD from March 2023 and followed up prospectively to September 2023. The primary endpoint was MOGAD relapse, confirmed by an independent panel. Patients were randomly divided into model development (75%) and internal validation (25%) cohorts. Prediction models were constructed and internally validated using Andersen-Gill models. Nomogram and relapse risk score were generated based on the final prediction models.\r\n\r\nRESULTS\r\nA total of 188 patients (comprising 612 treatment episodes) were included in cohorts. Female (HR: 0.687, 95% CI 0.524-0.899, p = 0.006), onset age 45 years or older (HR: 1.621, 95% CI 1.242-2.116, p < 0.001), immunosuppressive therapy (HR: 0.338, 95% CI 0.239-0.479, p < 0.001), oral corticosteroids >3 months (HR 0.449, 95% CI 0.326-0.620, p < 0.001), and onset phenotype (p < 0.001) were identified as factors associated with MOGAD relapse. A predictive score, termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype), derived in prediction model, demonstrated strong predictive ability for MOGAD relapse. MOG-AR score of 13-16 indicates a higher risk of relapse (HR: 3.285, 95% CI 1.473-7.327, p = 0.004).\r\n\r\nDISCUSSION\r\nThe risk of MOGAD relapse seems to be predictable. Further validation of MOG-AR score developed from this cohort to determine appropriate treatment and monitoring frequency is warranted.\r\n\r\nTRIAL REGISTRATION INFORMATION\r\nCNRID, NCT05154370, registered December 13, 2021, first enrolled December 15, 2021.","PeriodicalId":19472,"journal":{"name":"Neurology® Neuroimmunology & Neuroinflammation","volume":"1 1","pages":"e200309"},"PeriodicalIF":7.8000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack.\",\"authors\":\"Yun Xu,Huaxing Meng,Moli Fan,Linlin Yin,Jiali Sun,Yajun Yao,Yuzhen Wei,Hengri Cong,Huabing Wang,Tian Song,Chun-Sheng Yang,Jinzhou Feng,Fu-Dong Shi,Xinghu Zhang,De-Cai Tian\",\"doi\":\"10.1212/nxi.0000000000200309\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"BACKGROUND AND OBJECTIVES\\r\\nTo identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to develop and validate a simple risk score for predicting relapse.\\r\\n\\r\\nMETHODS\\r\\nIn China National Registry of Neuro-Inflammatory Diseases (CNRID), we identified patients with MOGAD from March 2023 and followed up prospectively to September 2023. The primary endpoint was MOGAD relapse, confirmed by an independent panel. Patients were randomly divided into model development (75%) and internal validation (25%) cohorts. Prediction models were constructed and internally validated using Andersen-Gill models. Nomogram and relapse risk score were generated based on the final prediction models.\\r\\n\\r\\nRESULTS\\r\\nA total of 188 patients (comprising 612 treatment episodes) were included in cohorts. Female (HR: 0.687, 95% CI 0.524-0.899, p = 0.006), onset age 45 years or older (HR: 1.621, 95% CI 1.242-2.116, p < 0.001), immunosuppressive therapy (HR: 0.338, 95% CI 0.239-0.479, p < 0.001), oral corticosteroids >3 months (HR 0.449, 95% CI 0.326-0.620, p < 0.001), and onset phenotype (p < 0.001) were identified as factors associated with MOGAD relapse. A predictive score, termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype), derived in prediction model, demonstrated strong predictive ability for MOGAD relapse. MOG-AR score of 13-16 indicates a higher risk of relapse (HR: 3.285, 95% CI 1.473-7.327, p = 0.004).\\r\\n\\r\\nDISCUSSION\\r\\nThe risk of MOGAD relapse seems to be predictable. Further validation of MOG-AR score developed from this cohort to determine appropriate treatment and monitoring frequency is warranted.\\r\\n\\r\\nTRIAL REGISTRATION INFORMATION\\r\\nCNRID, NCT05154370, registered December 13, 2021, first enrolled December 15, 2021.\",\"PeriodicalId\":19472,\"journal\":{\"name\":\"Neurology® Neuroimmunology & Neuroinflammation\",\"volume\":\"1 1\",\"pages\":\"e200309\"},\"PeriodicalIF\":7.8000,\"publicationDate\":\"2024-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurology® Neuroimmunology & Neuroinflammation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1212/nxi.0000000000200309\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurology® Neuroimmunology & Neuroinflammation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1212/nxi.0000000000200309","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack.
BACKGROUND AND OBJECTIVES
To identify predictors for relapse in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and to develop and validate a simple risk score for predicting relapse.
METHODS
In China National Registry of Neuro-Inflammatory Diseases (CNRID), we identified patients with MOGAD from March 2023 and followed up prospectively to September 2023. The primary endpoint was MOGAD relapse, confirmed by an independent panel. Patients were randomly divided into model development (75%) and internal validation (25%) cohorts. Prediction models were constructed and internally validated using Andersen-Gill models. Nomogram and relapse risk score were generated based on the final prediction models.
RESULTS
A total of 188 patients (comprising 612 treatment episodes) were included in cohorts. Female (HR: 0.687, 95% CI 0.524-0.899, p = 0.006), onset age 45 years or older (HR: 1.621, 95% CI 1.242-2.116, p < 0.001), immunosuppressive therapy (HR: 0.338, 95% CI 0.239-0.479, p < 0.001), oral corticosteroids >3 months (HR 0.449, 95% CI 0.326-0.620, p < 0.001), and onset phenotype (p < 0.001) were identified as factors associated with MOGAD relapse. A predictive score, termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype), derived in prediction model, demonstrated strong predictive ability for MOGAD relapse. MOG-AR score of 13-16 indicates a higher risk of relapse (HR: 3.285, 95% CI 1.473-7.327, p = 0.004).
DISCUSSION
The risk of MOGAD relapse seems to be predictable. Further validation of MOG-AR score developed from this cohort to determine appropriate treatment and monitoring frequency is warranted.
TRIAL REGISTRATION INFORMATION
CNRID, NCT05154370, registered December 13, 2021, first enrolled December 15, 2021.
期刊介绍:
Neurology Neuroimmunology & Neuroinflammation is an official journal of the American Academy of Neurology. Neurology: Neuroimmunology & Neuroinflammation will be the premier peer-reviewed journal in neuroimmunology and neuroinflammation. This journal publishes rigorously peer-reviewed open-access reports of original research and in-depth reviews of topics in neuroimmunology & neuroinflammation, affecting the full range of neurologic diseases including (but not limited to) Alzheimer's disease, Parkinson's disease, ALS, tauopathy, and stroke; multiple sclerosis and NMO; inflammatory peripheral nerve and muscle disease, Guillain-Barré and myasthenia gravis; nervous system infection; paraneoplastic syndromes, noninfectious encephalitides and other antibody-mediated disorders; and psychiatric and neurodevelopmental disorders. Clinical trials, instructive case reports, and small case series will also be featured.