
==== Front
Neurol Neuroimmunol Neuroinflamm
Neurol Neuroimmunol Neuroinflamm
nnn
NEURIMMINFL
Neurology® Neuroimmunology & Neuroinflammation
2332-7812
Lippincott Williams & Wilkins Hagerstown, MD

39250723
NXI-2024-100027
10.1212/NXI.0000000000200309
00009
3
17
23
40
Research Article
A Simple Score (MOG-AR) to Identify Individuals at High Risk of Relapse After MOGAD Attack
Xu Yun MD *
Meng Huaxing MD *
Fan Moli MD
Yin Linlin PhD
Sun Jiali MD
Yao Yajun MD
Wei Yuzhen MD
Cong Hengri MD
Wang Huabing PhD
Song Tian MD
Yang Chun-Sheng MD
Feng Jinzhou MD
https://orcid.org/0000-0002-9675-4637
Shi Fu-Dong MD, PhD
Zhang Xinghu PhD †
https://orcid.org/0000-0002-5153-2491
Tian De-Cai MD, PhD †
From the Department of Neurology (Y.X., L.Y., J.S., Y.Y., Y.W., H.C., H.W., T.S., F.-D.S., X.Z., D.-C.T.), China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University; Department of Neurology (H.M.), First Hospital of Shanxi Medical University, Taiyuan; Department of Neurology (M.F., C.-S.Y., F.-D.S.), Tianjin Neurological Institute, Tianjin Medical University General Hospital; and Department of Neurology (J.F.), The First Affiliated Hospital of Chongqing Medical University, China.
Correspondence Dr. Tian decaitian@hotmail.com
Go to Neurology.org/NN for full disclosures. Funding information is provided at the end of the article.

The Article Processing Charge was funded by the authors.

Submitted and externally peer reviewed. The handling editor was Associate Editor Friedemann Paul, MD.

* These authors contributed equally to this work as co-first authors.

† These authors contributed equally to this work as co-senior authors.

11 2024
9 9 2024
9 9 2024
11 6 e20030910 1 2024
19 7 2024
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.
2024
American Academy of Neurology
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND), which permits downloading and sharing the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

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.

OPEN-ACCESSTRUE
==== Body
pmcIntroduction

Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a newly recognized distinct CNS demyelinating disorder characterized by a relapsing course and significant disability in certain patients.1-3 The presence of MOG-IgG, a specific marker of MOGAD, targets the myelin oligodendrocyte glycoprotein located on the outer membrane of the myelin sheath, inducing transient destruction of the oligodendrocyte.4 MOGAD differs significantly from multiple sclerosis and aquaporin-4 IgG-seropositive neuromyelitis optica spectrum disorder regarding pathology, clinical presentations, imaging, relapse characteristics, and prognosis, suggesting that a different medical management is needed.5-8

Attack-independent neuroaxonal, astrocytic injury, as well as new or enlarging MRI lesions were absent in MOGAD,9,10 highlight the importance of preventing relapse attacks in this condition. Previous studies have suggested that a variety of factors, including age, gender, clinical phenotype, MOG-IgG titer, and treatments, are associated with MOGAD recurrence.11-14 However, practical tools to stratify the relapse risk of MOGAD were absent, and achieving precise treatment was difficulties.

Our goal was to develop and validate a simple risk score, using data from multiple centers, to predict MOGAD relapse. To this end, we take into consideration of 3 practical issues: enabling front-line physicians to identify patients at risk of relapse who would benefit from maintenance immunotherapy; secondary-care physicians in identifying patients likely to experience relapse for more intensive monitoring and follow-up; and increasing awareness among patients and their relatives about the MOGAD relapse.

Methods

Participants

We used data from the China inflammatory demyelinating diseases registry, China National Registry of Neuro-Inflammatory Diseases (CNRID, NCT05154370), a prospective observational (noninterventional) national multicenter cohort study to collect clinical information from patients diagnosed with idiopathic inflammatory demyelinating diseases (IDD) who provided informed consent. The CNRID routinely and systematically follows up with patients with IDD across multiple clinical indicators and evaluates clinical outcomes. Demographic and longitudinal clinical data were extracted from the CNRID cohort at 4 medical centers (Beijing Tiantan Hospital, Tianjin Medical University General Hospital, First Hospital of Shanxi Medical University, and First Affiliated Hospital of Chongqing Medical University) in March 2023. The latest follow-up outcomes, representing the most recent assessment of relapse, were retrieved from the patient's medical records in September 2023. If medical records were (e.g., no records within the last 3 months or incomplete records), the assessment was conducted through video call–based follow-up. Patients suspected of relapse during the video consultations were instructed to visit local medical centers, and relevant medical records and MRI films were sent to the adjudication group (D.-C.T., M.L.F., H.X.M., and J.Z.F.) for assessment. Recurrence was determined based on medical records and MRI findings. All data were deidentified.

Participants were included based on the following criteria: (1) diagnosis of MOGAD according to the proposed criteria by the International MOGAD Panel in 2023,1 (2) presence of MOG antibodies (MOG-Abs) detected at onset or during a clinical relapse using cell-based assay (CBA), and (3) follow-up duration of at least 12 months. Patients with incomplete data, failing to meet the minimal data set requirements, were excluded. The minimal data set consisted of the patient's birth data, sex, dates of MOGAD onset and clinical follow-up, disease course and disability status (at inclusion and censoring), start and end dates of immunosuppressive treatment exposure, and list of clinical relapses (including date of onset and treatment status at relapse).

Procedure

Clinical data were anonymized and input by each participating investigator into a unified electronic case report form, capturing record patients' demographics, clinical features (including characteristics of first and subsequent attacks and treatment details), and laboratory findings (MOG-Abs and AQP4-Ab, CSF white blood cell count, protein level, oligoclonal bands, and virologic test results). The study data set was divided into training and validation cohorts (approximately 3:1), with predefined criteria ensuring comparability between the cohorts. A dedicated quality management team oversaw data cleaning, while a disease specialist group reviewed the data. All relapse events were independently re-evaluated by the respective medical center leads (D.-C.T., M.L.F., H.X.M., J.Z.F.), followed by 2 investigators’ analyses (Y.X., H.X.M.).

The demyelinating phenotype at onset was determined based on the patient's clinical presentation, following the 2023 criteria.1 All patients had underwent brain, spinal cord, and orbital MRI scans as per local medical center MRI protocols, serving as essential diagnostic procedures. However, owing to differences of MRI scanners and protocols adopted among the participating medical centers, quantitative or semiquantitative MRI analysis was not included in this work. The MRI sequences used to assess recurrence included at least the following: T1 precontrast and postcontrast, susceptibility weighted imaging, axial T2 and/or FLAIR for brain and orbit; sagittal T2, axial T2, and T1 postcontrast for spinal. All MRI were clinically identified by both neurologists and radiologist.

For acute attack, all patients received daily high-dose IV methylprednisolone (IVMP) (500–1,000 mg) for 5 consecutive days starting on the time of diagnosis then reduced sequentially, followed by a tapering dose of oral prednisone (start at 60 mg/d) reduced by 5–10 mg per week or every 2 weeks. If high-dose IVMP was deemed ineffective (no improvement in symptoms), more intensive treatments were administered. IV immunoglobulin therapy (0.4 g/kg/d for 5 days) was used as additional therapy, and immunoadsorption (IA) was performed every other day for a total of 3–5 sessions. Physicians selected different immunosuppressive therapies according to the patient's condition, including oral mycophenolate mofetil every day, IV rituximab (RTX) (300–500 mg IV, repeated after 2 weeks and 6 months), or other immunotherapies such as azathioprine, tacrolimus, or tocilizumab. Using immunosuppressive therapy after an attack was defined as follows: receipt of any these treatments ≥6 months after attack; receipt immunosuppressant regularly until the next attack or follow-up; patients switched drugs need to recalculate the timing of their medication when starting the new drug.

Endpoint Definitions

MOGAD relapses were characterized by the emergence of a new neurologic abnormalities or worsening of previously stable or improving pre-existing neurologic abnormality (persisting for at least 24 hours and in the absence of fever or known infection), occurring at least at least 30 days after the onset of a preceding clinical demyelinating event lasting at least 24 hours.1,15,16 The term “first onset” referred to the initial attack that marked disease onset, while “first relapse” denoted the second attack experienced after disease onset.

MOG-Ab Testing

Within 1 month of an acute event (first attack or relapse), patients underwent serum MOG antibody testing, using a fixed CBA on HEK293 cells transfected with MOG. Serum MOG-IgG titers were determined through serial 2-fold dilutions from beginning from 1:10. The highest dilution resulting in positive staining of transfected HEK293 cells, detected using a fluorescence microscope, was considered the endpoint titer. Each sample was independently evaluated by 2 experienced assessors who were masked to the clinical details. Samples with titers of ≥1:100 were classified as clearly positive. Serum MOG antibody testing was conducted by NEW TERRAIN (Tianjin, China17) or Kingmed Diagnostics (Guangzhou, China18).

Statistical Analysis

Statistical analysis was analyzed using SPSS 25.0 (International Business Machines Corporation, Chicago, IL) and R (version 4.3.1; R Foundation for Statistical Computing, Vienna, Austria19). Graphs were generated using GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA) and R.

The patient cohort was randomly divided into 2 groups using simple randomizations. The training cohort, consisting of 75% of the patients, was used to develop the model and scoring system for predicting MOGAD relapse. The internal validation cohort, comprising 25% of the patients, was used to validate the model and scoring approach. Continuous variables were presented as mean and SD, or median and interquartile range (IQR), while categorical variables were summarized by frequency and percentage. Student t tests/Mann-Whitney U and χ2 test/Fisher exact test were used to contrast baseline characteristics between the 2 cohorts for continuous variables and proportions, respectively.

The Kaplan-Meier method was used to estimate relapse risk. The prognostic value of baseline characteristics for subsequent relapse was assessed using Andersen-Gill models, where patient identification served as a cluster variable, and the outcomes of interest were “time to relapse” or “time to last follow-up” (for the monophasic course). This model, developed by Andersen and Gill (1982), is a famous multiplicative intensity model that accounts for patient heterogeneity or clustering of recurring attacks.20 The proportional hazard assumption was not violated (Schoenfeld individual test p > 0.05).

We developed a nomogram to predict relapse in patients with MOGAD in 4 steps: (1) conducting univariable regression analysis (AG model) with one variable at a time; (2) performing multivariable regression analysis (AG model) by including variables identified as potential independent risk factors for relapse (independent variables with p < 0.1 in the univariable model or those clinically relevant); (3) constructing the nomogram according to the multivariable AG model. Model discrimination was evaluated using the area under the receiver operating characteristic (ROC) curve (c statistic), and calibration was evaluated using the Hosmer-Lemeshow test.

We developed a risk score tool for MOGAD relapse based on multiple regression models (AG model). Risk factor β coefficients were divided by the minimum coefficient, and the resulting values were rounded to the nearest integer.21-23 A significance level of p < 0.05 was used for statistical inference, and all p values were 2-sided.

Data Availability

Data that have been anonymized and not included in this article are available on request from the corresponding author, subject to reasonable conditions, for any qualified investigator.

Standard Protocol Approvals, Registrations, and Consents

Patients included in this study received respective review board/ethical committee approvals (KY2021-150-01).

Results

Characteristics of the MOGAD Cohorts

A total of 1800 patients underwent screening, of whom 226 met the eligibility criteria and were subsequently included in prospective follow-up. Among these, 38 patients were excluded: 6 because of other causes of death, 9 withdrew consent, and 23 were unresponsive during telephone follow-up. Ultimately, 188 participants (612 treatment episodes) were included in the derivation and internal validation cohorts, with 142 in the derivation cohort and 46 in the internal validation cohort (Figure 1). Additional details regarding excluded patients with antibody positivity, along with MOG-ab, presented in eTable 1.

Figure 1 Study Flowchart of Patient Disposition

The demographics of the derivation and internal validation groups are summarized in Table 1, demonstrating no significant differences between the 2 sets. The mean age at onset for participants was age 36.44 years, with a slight female predominance of 54.79%. The median disease duration at the last follow-up was 2.67 years (IQR, 1.33–4.40 years) in the derivation cohort and 2.88 years (IQR, 1.44–4.88 years) in the internal validation cohort (p = 0.658). The median annualized rate of relapse was 0.67 (IQR, 0.43–0.92) in the derivation cohort and 0.56 (IQR, 0.36–0.99) in the internal validation cohort (p = 0.280). A total of 69 (48.59%) patients experienced a relapsing course in the derivation cohort, compared with 21 (45.65%) in the validation group. The time to first relapse was slightly longer in the internal validation cohort but did not reach statistical significance [17.50 (IQR 9.25–33.50) vs 12.00 (6.00–25.00), p = 0.230]. Optic neuritis was the most prevalent onset feature (38.30%), followed by transverse myelitis (34.04%), with other presentations being less common. There were significant differences in onset phenotype between pediatric-onset and adult-onset cohort (p = 0.011) (eTable 2). After the first attack, only a minority of patients received maintenance immunosuppressive therapy, including rituximab (6.88%), mycophenolate mofetil (8.51%), and others (6.38%). The median serum MOG-ab titer at diagnosis was 1:32 (IQR, 1:10–1:100) in both the derivation and internal validation groups. Characteristics of patients tested for MOG-ab were shown in eTable 3. The median EDSS score at the time of the first attack was 3.0 (IQR, 2.5–4.0) in the derivation group and 3.5 (IQR, 3.0–4.0) in the validation group (p = 0.460). At the last follow-up, the median EDSS score was 1.0 (IQR, 0–2.0) in both the derivation and validation groups (p = 0.460).

Table 1 Demographic and Clinical Features of Patients With MOGAD

	Total cohort (n = 188)	Derivation cohort (n = 142)	Validation cohort (n = 46)	p Value	
Age at onset, mean (SD), y	36.44 (15.03)	36.45 (15.61)	36.39 (13.26)	0.981	
Female, n (%)	103 (54.79)	75 (52.82)	28 (60.87)	0.340	
Disease duration at last follow up, median (IQR), y	2.75 (1.33–4.50)	2.67 (1.33–4.40)	2.88 (1.44–4.88)	0.658	
ARR, median (IQR)	0.67 (0.41–0.99)	0.67 (0.43–0.92)	0.56 (0.36–0.99)	0.280	
Relapsing course, n (%)	90 (47.87)	69 (48.59)	21 (45.65)	0.729	
Time to first relapse, median (IQR), mo	13.50 (7.00–28.00)	12.00 (6.00–25.00)	17.50 (9.25–33.50)	0.230	
Onset phenotype, n (%)					
 Optic neuritis	72 (38.30)	52 (36.62)	20 (43.48)	0.406	
 Myelitis	64 (34.04)	49 (34.51)	15 (32.61)	0.813	
 ADEM	7 (3.72)	7 (4.93)	0 (0.00)	0.277	
 Cerebral monofocal or polyfocal deficits	30 (15.96)	22 (15.49)	8 (17.39)	0.760	
 Brainstem or cerebellar deficits	32 (17.02)	26 (18.31)	6 (13.04)	0.409	
 Cerebral cortical encephalitis	24 (12.77)	18 (12.68)	6 (13.04)	0.948	
 Mix	37 (19.68)	28 (19.72)	9 (19.57)	0.982	
Immunosuppressive therapy after first attack, n (%)				0.243	
 RTX	13 (6.88)	10 (7.09)	3 (6.25)		
 MMF	16 (8.51)	9 (6.34)	7 (15.22)		
 Other	12 (6.38)	10 (7.04)	2 (4.35)		
 Not use	147 (78.19)	114 (80.28)	33 (71.74)		
Serum MOG-ab titre at diagnosis, median (IQR)	1:32 (1:10–1:100)	1:32 (1:10 - 1:100)	1:32 (1:10 - 1:100)	0.540	
CSF leucocyte count, median (IQR), 106/L	16.00 (4.00–70.00)	17.00 (4.00–61.00)	15.00 (6.00–75.00)	0.626	
CSF protein, median (IQR), mg/L	42.64 (31.75–55.18)	42.64 (31.57–61.25)	40.85 (33.25–51.25)	0.435	
OB positive, n (%)	31 (34.44)	24 (35.29)	7 (31.82)	0.766	
EDSS score at first attack, median (IQR)	3.50 (2.50–4.00)	3.00 (2.50–4.00)	3.50 (3.00–4.00)	0.460	
EDSS score at last follow up, median (IQR)	1.00 (0.00–2.00)	1.00 (0.00–2.00)	1.00 (0.00–2.00)	0.932	
Abbreviations: ADEM = acute disseminated encephalomyelitis; ARR = annualized relapse rate; EDSS = Expanded Disability Status Scale; IQR = inter-quartile range; MMF = mycophenolate mofetil; MOGAD = myelin oligodendrocyte glycoprotein antibody-associated disorder; OB = oligoclonal bands; RTX = rituximab.

Factors Associated With Relapse

The survival curves depicting overall relapse based on clinical characteristics are illustrated in Figure 2 and eFigure 1. Significant differences in relapse were observed between patients aged older than 45 years compared with those aged 45 years or younger (HR: 1.621, 95% CI 1.242–2.116, p < 0.001). Male patients exhibited a lower risk of relapse compared with female patients (HR: 0.687, 95% CI 0.524–0.899, p = 0.006). Notably, there were significant differences in relapse risk between patients with different attack phenotypes (p < 0.001). The use of any form of immunosuppressive therapy after an attack was linked to a reduced risk of relapse compared with patients who did not receive immunosuppressive treatment (HR: 0.338, 95% CI 0.239–0.479, p < 0.001). Moreover, treatment with corticosteroid treatment for 3 months or longer was linked to a decreased risk of relapse (HR 0.449, 95% CI 0.326–0.620, p < 0.001). The factors influencing relapse in pediatric-onset and adult-onset cohorts exhibited notable differences (eTable 4).

Figure 2 Relapse Risk for the Total Cohort

ADEM = acute disseminated encephalomyelitis; HR = hazard ratio.

Prediction of MOGAD Relapse in the Derivation Cohort

Table 2 presents the results of univariable and multivariable AG analyses of risk factors predictive of MOGAD relapse. In the univariable AG regression, female sex and onset age 45 years or older were associated with an increased HR (p = 0.032 and 0.010, respectively). Conversely, immunosuppressive therapy and oral corticosteroids >3 months were linked to a decreased HR (p < 0.001 for both). No significant associations were observed between relapse and onset younger than 18 years, EDSS at attack, elevated CSF leukocyte at onset, or elevated CSF protein at onset. In the multivariable AG regression, female and cerebral cortical encephalitis attack were independently related to an elevated HR for relapse (p = 0.010 and 0.002), while immunosuppressive therapy and oral corticosteroids >3 months were associated with a reduced HR for relapse (p < 0.001 and p = 0.004 respectively). The impact of IVIG (HR: 0.75, 95% CI 0.49–1.15, p = 0.700) and IA (HR: 0.49, 95% CI 0.16–1.53, p = 0.220) combined with steroids on relapse was significant. Various multivariable regression models for MOGAD relapse (in total cohort) were detailed in eTable 5 and eTable 6.

Table 2 Regression Analysis of Relapse Predicts Factors in MOGAD Attack With Anderson and Gill Model in Derivation Cohort

	Univariable analysis	Multivariable analysis	
HR	95% CI	p Value	HR	95% CI	p Value	
Sex, female	1.39	1.03–1.88	0.032*	1.54	1.11–2.14	0.010*	
Onset Younger than 18 years	1.02	0.68–1.53	0.924	—	—	—	
Onset 45 years or older	1.48	1.10–2.00	0.010*	1.33	0.96–1.84	0.090	
Attack phenotype							
 Myelitis	Reference						
 Optic neuritis	1.15	0.77–1.72	0.494	1.43	0.94–2.16	0.095	
 ADEM	1.07	0.46–2.52	0.875	2.08	0.89–5.34	0.087	
 Cerebral monofocal or polyfocal deficits	0.97	0.49–1.93	0.932	1.29	0.63–2.61	0.484	
 Brainstem or cerebellar deficits	0.74	0.40–1.38	0.341	1.20	0.63–2.30	0.582	
 Cerebral cortical encephalitis	1.98	1.18–3.33	0.009*	2.36	1.35–4.11	0.002*	
 MIX	1.05	0.69–1.62	0.813	1.43	0.92–2.23	0.113	
Immunosuppressive therapy							
 No treatment	Reference						
 Treatment	0.36	0.25–0.53	<0.001**	0.39	0.26–0.57	<0.001**	
Immunosuppressive therapy							
 Not use	Reference						
 RTX	0.35	0.21–0.60	<0.001**	—	—	—	
 MMF	0.31	0.14–0.70	0.005*	—	—	—	
 Others	0.58	0.33–1.03	0.064	—	—	—	
Oral corticosteroids ≥3 mo	0.49	0.33–0.73	<0.001**	0.54	0.35–0.81	0.004*	
EDSS at attack	0.93	0.81–1.05	0.161	—	—	—	
Serum MOG-ab titre at attack	1.00	0.96–1.00	0.657	—	—	—	
CSF MOG-ab titre at attack	1.00	0.98–1.03	0.884	—	—	—	
CSF leucocyte at onset							
 Normal	Reference						
 Elevateda	1.20	0.69–2.21	0.560	—	—	—	
CSF protein at onset							
 Normal	Reference						
 Elevatedb	1.10	0.66–1.83	0.700	—	—	—	
Add IVIG at attack	0.75	0.49–1.15	0.184	—	—	—	
Add IA at attack	0.49	0.16–1.53	0.220	—	—	—	
Abbreviations: ADEM = acute disseminated encephalomyelitis; EDSS = Expanded Disability Status Scale; IA = immunoadsorption; IVIG = iV immunoglobulin; MOG-ab = myelin oligodendrocyte glycoprotein antibody; MOGAD = myelin oligodendrocyte glycoprotein antibody-associated disorder. *p < 0.05, **p < 0.001 represent statistical significance.

a CSF leucocyte elevated is CSF leucocyte more than 5/mL.

b CSF protein elevated is CSF protein more than 45 mg/dL.

Relapse Prediction Nomogram

We developed a nomogram to predict 1-year and 2-year relapse-free probability based on the significant baseline correlates of MOGAD relapse found in the AG model (Figure 3). The contribution of each independent explanatory variable on the nomogram points ranked in descending order: receiving immunosuppressive therapy, experiencing a cerebral cortical encephalitis attack, using oral corticosteroids >3 months, being female, and having an onset age 45 years or older. In the nomogram, each variable value corresponds to a point, and the points for the 5 variables are summed to calculate an individual's total points. These total points are then projected onto a scale to determine the probability of relapse-free for an individual according to each profile. The ROC curve demonstrated the effectiveness of the model in predicting 1-year relapse-free both in the derivation group (AUC 0.712, 95% CI 0.650–0.773) (eFigure 2A) and the validation group (AUC 0.745, 95% CI 0.589–0.901) (eFigure 2B). The calibration curve showed accurate agreement between the predicted and actual relapse probabilities throughout the derivation and validation groups (Hosmer-Lemeshow p = 0.596 and 0.137) (eFigure 2, C and D).

Figure 3 Nomogram for Predicting Probability of Relapse-Free Among Patients With MOGAD

The nomogram illustrates the predicted probability of relapse-free at 1-year and 2-year follow-up based on 5 clinical factors. Each clinical feature's presence or absence corresponds to a specific number of points, listed on the top row. An absence of a feature is assigned 0 points (* 1 = Optic neuritis; 2 = Myelitis; 3 = ADEM; 4 = Cerebral monofocal or polyfocal deficits; 5 = Brainstem or cerebellar deficits; 6 = Cerebral cortical encephalitis; 7 = Mix).

MOG-AR Scores

Table 2 displays the linkages between the 5 potential independent risk factors and MOGAD relapse. The risk score, as detailed in Table 3 and eTable 7, assigns values based on specific criteria: not receive immunosuppressive therapy = 5, oral corticosteroids less than 3 months = 3, onset age 45 years or older = 2, female sex = 2 and attack phenotype (cerebral cortical encephalitis = 4, ADEM = acute disseminated encephalomyelitis [ADEM] = 3, optic neuritis = 2, cerebral monofocal or polyfocal deficits = 1, brainstem or cerebellar deficits = 1, myelitis = 0). The score was termed MOG-AR (Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype).

Table 3 Details of the MOG-AR Score (0–16)

Risk factor	Points	
Onset age (y)		
 45 y or older	2	
Sex		
 Female	2	
Attack phenotype		
 Cerebral cortical encephalitis	4	
 ADEM	3	
 Optic neuritis	2	
 Cerebral monofocal or polyfocal deficits	1	
 Brainstem or cerebellar deficits	1	
 Myelitis	0	
Immunosuppressive therapy		
 Not use	5	
Oral corticosteroids (mo)		
 <3	3	
Abbreviation: ADEM = acute disseminated encephalomyelitis.

Risk grade: grade 1 = 0–4; grade 2 = 5–8; grade 3 = 9–12; grade 4 = 13–16.

Table 4 presents the risk of relapse of MOGAD classified by the MOG-AR score. Only 7 relapses occurred in patients with a score of 0–4 [%risk (95% CI) = 33.3 (13.2–53.5)], whereas the relapse risk was 78.8% (95% CI = 67.7–89.9) in cases with a score between 13 and 16. The area under the ROC curve (Figure 4) calculated to be 0.709 (0.655–0.763).

Table 4 Relapse Risk of MOGAD Stratified to MOG-AR Score at First Assessment

Risk grade	MOG-AR score	Patients (%)	Relapse	%Risk (95% CI)	
Grade 1	0–4	21 (5.5%)	7 (3.0%)	33.3 (13.2–53.5)	
Grade 2	5–8	116 (30.4%)	50 (21.4%)	43.1 (34.1–52.1)	
Grade 3	9–12	193 (50.5%)	136 (58.1%)	70.5 (64.0–76.9)	
Grade 4	13–16	52 (13.6%)	41 (17.5%)	78.8 (67.7–89.9)	
Total	0–16	382 (100%)	234 (100%)	61.3 (56.4–66.1)	
Abbreviations: MOGAD = myelin oligodendrocyte glycoprotein antibody-associated disorder; MOG-AR = Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype.

Figure 4 ROC Curves for Predictive Value of the MOG-AR Score

Univariable regression analysis of the MOG-AR score in MOGAD relapse is presented in Figure 5. A MOG-AR score of 9–12 indicates a high risk of relapse (HR: 2.656, 95% CI 1.242–5.679, p = 0.012), while a MOG-AR score of 13–16 indicates an even more elevated risk of relapse (HR: 3.285, 95% CI 1.473–7.327, p = 0.004). A MOG-AR score of 9 or higher is predictive of MOGAD relapse.

Figure 5 Univariable Regression Analysis of MOG-AR Score in MOGAD Relapse

Abbreviations: MOGAD = myelin oligodendrocyte glycoprotein antibody-associated disorder; MOG-AR = Immunosuppressive therapy, oral Corticosteroids, Onset Age, Sex, Attack phenotype. *p < 0.05, **p < 0.01 represent statistical significance.

Discussion

Using data from the China multicenter neuro-inflammatory diseases registry (CNRID), we investigated predictors of relapse from the onset of MOGAD. Our study underscores the impact of sex, onset age 45 years or older, onset attack phenotype, use of immunosuppressive therapy, and duration of oral glucocorticoids on relapse risks. We have developed and validated a potentially valuable prognostic tool—a simple score (MOG-AR) to predict MOGAD relapse, based on these risk factors. This score could empower clinicians to anticipate relapse early in the course of MOGAD, enabling appropriate subsequent treatment and follow-up scheduling.

The relapse risk scoring system targets crucial stages in the management of MOGAD. First, the MOG-AR score is straightforward, allowing frontline health services and outpatient departments to quickly assess relapse risk in patients with MOGAD. Second, the score aids specialist physicians in stratifying the management and treatment of patients with MOGAD. In China, where patients with MOGAD typically visit outpatient clinics every 3–6 months, identification of high relapse risks using the MOG-AR score should prompt immediate treatment adjustments and more frequent follow-up visits. Third, the score enhances public awareness of the relapse risk associated with MOGAD and underscores the importance of seeking medical assistance.

Notably, our analysis suggests that patients with an onset age older than 45 years are more likely to experience disease relapse. Previous studies have indicated that children with MOGAD have lower relapse rates compared to adults, and patients with recurrence tend to have a significantly higher median age than those without recurrence.11,24,25 The relationship between relapse and age may be influenced by variations in immune status across different age groups, necessitating further exploration. Research examining humoral immune responses in patients with MOGAD has revealed that age is a crucial determinant of MOG-Ab immune signatures in this condition.26

We observed a higher risk of relapse among female patients with MOGAD, which aligns with several prior reports.7,12,25 A recent study found that in female patients experience shorter intervals to first relapse and higher risks of relapsing.12 However, another study reported a higher risk of recurrence among female patients only within a pediatric cohort.7 While many studies, including ours, have noted a slight preponderation of women in MOGAD, research focusing on the impact of sex or sex hormones on MOGAD remains scarce.27-30 Further investigation into the role of gender in MOGAD is warranted.

Another factor linked to disease relapse throughout our group was the onset phenotype. We found that ADEM phenotype was independently associated with an increased risk of MOGAD relapse (p = 0.039). Consistent with previous studies, the ADEM phenotype appears to indicate a higher recurrence rate in both children and adults.31,32 However, some studies have reported a higher recurrence rate among patients with ON or TM phenotypes,11,33,34 and the differences observed in those studies may also be linked to age distribution. We categorized the risk of recurrence based on the pathogenetic phenotype.

Treatment significantly influences future recurrence in patients with MOGAD. Our findings indicate that patients receiving immunosuppressive therapy have a lower risk of relapse. Subgroup analysis further suggests that RTX and MMF treatments are particularly effective in reducing the risk of relapse. A wealth of previous research supports the role of maintenance immunotherapy in reducing recurrent MOGAD attacks and lowering ARR.14,35,36 Our study, along with a UK study, suggests that a course of oral prednisolone lasting more than 3 months after onset is associated with a reduced risk of relapse.27 Currently, the optimal duration of corticosteroid treatment postonset remains a topic of controversy and requires further investigation.33

Our study has several limitations worth noting. First, we lacked an external validation cohort. However, given that our data originated from multiple centers, internal validation provided valuable insights. Further validations of the MOG-AR score are warranted and likely to be refined in larger investigations. In addition, biases may exist because of patients lacking maintenance immunotherapy being lost to follow-up, although most centers actively follow-up with patients on long-term immunosuppression. This bias could result in the loss of patients with milder attacks from our study cohort. Furthermore, the minimum follow-up period among patients was 12 months, potentially underestimating the relapse risk for those with shorter follow-up periods because some patients with MOGAD experience relapses several years after the initial diagnosis. The predictive value of a relapse change based on the assessed time point was not performed, thus we advise using MOG-AR at attack to help clinicians determine the probability of recurrence within 1 year. Finally, the predictors we included in our scoring system are fundamental, and further research is needed to expand the current scoring system and explore more comprehensive predictors.

In conclusion, predicting the risk of relapse after a MOGAD attack seems feasible. The current MOG-AR score, on further validation, can be used in routine clinical practice to identify high-risk individuals who require higher-intensity immunotherapy and more frequent monitoring.

Acknowledgment

The authors thank our patients for participating in this study, and all the members of Jing-Jin Neuroimmunology Teams for recruiting patients and other support. The authors are in debts to colleagues from the four Centers for caring MOGAD patients and support the study.

Study Funding

This work was supported by grants from the National Natural Science Foundation of China (82271374) and Beijing Natural Science Foundation grant (JQ23027).

Disclosure

The authors report no relevant disclosures. Go to Neurology.org/NN for full disclosures.

Appendix Authors

Name	Location	Contribution	
Yun Xu, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Drafting/revision of the manuscript for content, including medical writing for content; analysis or interpretation of data	
Huaxing Meng, MD	Department of Neurology, First Hospital of Shanxi Medical University, Taiyuan, China	Major role in the acquisition of data; analysis or interpretation of data	
Moli Fan, MD	Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, China	Major role in the acquisition of data	
Linlin Yin, PhD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data	
Jiali Sun, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data	
Yajun Yao, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data	
Yuzhen Wei, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data; analysis or interpretation of data	
Hengri Cong, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data	
Huabing Wang, PhD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Drafting/revision of the manuscript for content, including medical writing for content	
Tian Song, MD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Major role in the acquisition of data	
Chun-Sheng Yang, MD	Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, China	Major role in the acquisition of data	
Jinzhou Feng, MD	Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, China	Major role in the acquisition of data	
Fu-Dong Shi, MD, PhD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China; Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, China	Drafting/revision of the manuscript for content, including medical writing for content; study concept or design	
Xinghu Zhang, PhD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Drafting/revision of the manuscript for content, including medical writing for content; study concept or design	
De-Cai Tian, MD, PhD	Department of Neurology, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, China	Drafting/revision of the manuscript for content, including medical writing for content; study concept or design	

Glossary

ADEM acute disseminated encephalomyelitis

CBA cell-based assay

CNRID China National Registry of Neuro-Inflammatory Diseases

IA immunoadsorption

IDD inflammatory demyelinating diseases

IQR interquartile range

IVMP IV methylprednisolone

MOGAD myelin oligodendrocyte glycoprotein antibody-associated disease

RTX rituximab
==== Refs
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