The nomogram model predicts relapse risk in myelin oligodendrocyte glycoprotein antibody-associated disease: a single-center study Article Swipe
Jun Cheng
,
Zhuoran Wang
,
J. Wang
,
Xiaomin Pang
,
JianLi Wang
,
Meini Zhang
,
Junhong Guo
,
Huaxing Meng
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1527057
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fimmu.2025.1527057
Background Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is an autoimmune disorder of the central nervous system, characterized by seropositive MOG antibodies. MOGAD can present with a monophasic or relapsing course, where repeated relapses may lead to a worse prognosis and increased disability. Currently, little is known about the risk factors for predicting MOGAD relapse in a short period, and few established prediction models exist, posing a challenge to timely and personalized clinical diagnosis and treatment. Methods From April 2018 to December 2023, we enrolled 88 patients diagnosed with MOGAD at the First Hospital of Shanxi Medical University and collected basic clinical data. The data were randomly divided into a training cohort (80%) and a validation cohort (20%). Univariate logistic regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were used to identify independent risk factors for 1-year relapse. A prediction model was constructed, and a nomogram was developed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate and internally validate model performance. Results Among 88 MOGAD patients, 29 relapsed within 1 year of onset (33%). A total of 4 independent risk factors for predicting relapse were identified: female sex ( P =0.040), cortical encephalitis phenotype ( P =0.032), serum MOG antibody titer ≥1:32 ( P =0.007), and immunosuppressive therapy after the first onset ( P = 0.045). The area under curve (AUC) value of the nomogram prediction model constructed with these four factors was 0.866 in the training cohort, and 0.864 in the validation cohort. The cutoff value of the total nomogram score was 140 points, distinguishing the low relapse risk group from the high relapse risk group ( P < 0.001). The calibration curve demonstrated high consistency in prediction, and the DCA showed excellent net benefit in the prediction model. Tested by ROC curve, calibration curve, and DCA, the nomogram model also demonstrates significant value in predicting MOGAD relapse within 2 years. Conclusion The nomogram model we developed can help accurately predict the relapse risk of MOGAD patients within one year of onset and assist clinicians in making treatment decisions to reduce the chance of relapse.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fimmu.2025.1527057
- https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1527057/pdf
- OA Status
- gold
- Cited By
- 2
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408095583
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408095583Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fimmu.2025.1527057Digital Object Identifier
- Title
-
The nomogram model predicts relapse risk in myelin oligodendrocyte glycoprotein antibody-associated disease: a single-center studyWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-03-03Full publication date if available
- Authors
-
Jun Cheng, Zhuoran Wang, J. Wang, Xiaomin Pang, JianLi Wang, Meini Zhang, Junhong Guo, Huaxing MengList of authors in order
- Landing page
-
https://doi.org/10.3389/fimmu.2025.1527057Publisher landing page
- PDF URL
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1527057/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2025.1527057/pdfDirect OA link when available
- Concepts
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Medicine, Nomogram, Logistic regression, Cohort, Receiver operating characteristic, Internal medicine, Area under the curve, Single Center, ImmunologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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43Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.DCA, | 310 |
| abstract_inverted_index.From | 76 |
| abstract_inverted_index.also | 314 |
| abstract_inverted_index.area | 230 |
| abstract_inverted_index.data | 103 |
| abstract_inverted_index.four | 243 |
| abstract_inverted_index.from | 274 |
| abstract_inverted_index.help | 332 |
| abstract_inverted_index.high | 276, 288 |
| abstract_inverted_index.into | 107 |
| abstract_inverted_index.lead | 34 |
| abstract_inverted_index.risk | 48, 137, 192, 272, 278, 337 |
| abstract_inverted_index.used | 133, 166 |
| abstract_inverted_index.were | 104, 132, 165, 197 |
| abstract_inverted_index.with | 24, 87, 241 |
| abstract_inverted_index.year | 183, 343 |
| abstract_inverted_index.(80%) | 111 |
| abstract_inverted_index.(AUC) | 233 |
| abstract_inverted_index.(DCA) | 164 |
| abstract_inverted_index.(ROC) | 156 |
| abstract_inverted_index.0.864 | 252 |
| abstract_inverted_index.0.866 | 246 |
| abstract_inverted_index.2023, | 81 |
| abstract_inverted_index.Among | 175 |
| abstract_inverted_index.April | 77 |
| abstract_inverted_index.First | 91 |
| abstract_inverted_index.MOGAD | 21, 52, 88, 177, 320, 339 |
| abstract_inverted_index.about | 46 |
| abstract_inverted_index.after | 221 |
| abstract_inverted_index.basic | 99 |
| abstract_inverted_index.curve | 162, 232, 286 |
| abstract_inverted_index.data. | 101 |
| abstract_inverted_index.first | 223 |
| abstract_inverted_index.group | 273, 279 |
| abstract_inverted_index.known | 45 |
| abstract_inverted_index.least | 120 |
| abstract_inverted_index.model | 144, 172, 239, 313, 328 |
| abstract_inverted_index.onset | 185, 224, 345 |
| abstract_inverted_index.score | 264 |
| abstract_inverted_index.serum | 210 |
| abstract_inverted_index.short | 56 |
| abstract_inverted_index.these | 242 |
| abstract_inverted_index.titer | 213 |
| abstract_inverted_index.total | 188, 262 |
| abstract_inverted_index.under | 231 |
| abstract_inverted_index.value | 234, 259, 317 |
| abstract_inverted_index.where | 30 |
| abstract_inverted_index.worse | 37 |
| abstract_inverted_index.(20%). | 116 |
| abstract_inverted_index.(33%). | 186 |
| abstract_inverted_index.1-year | 140 |
| abstract_inverted_index.Myelin | 1 |
| abstract_inverted_index.Shanxi | 94 |
| abstract_inverted_index.Tested | 303 |
| abstract_inverted_index.assist | 347 |
| abstract_inverted_index.chance | 356 |
| abstract_inverted_index.cohort | 110, 115 |
| abstract_inverted_index.curve, | 157, 159, 306, 308 |
| abstract_inverted_index.cutoff | 258 |
| abstract_inverted_index.exist, | 63 |
| abstract_inverted_index.female | 199 |
| abstract_inverted_index.little | 43 |
| abstract_inverted_index.making | 350 |
| abstract_inverted_index.model. | 302 |
| abstract_inverted_index.models | 62 |
| abstract_inverted_index.posing | 64 |
| abstract_inverted_index.reduce | 354 |
| abstract_inverted_index.showed | 295 |
| abstract_inverted_index.timely | 68 |
| abstract_inverted_index.within | 181, 322, 341 |
| abstract_inverted_index.years. | 324 |
| abstract_inverted_index.(LASSO) | 126 |
| abstract_inverted_index.(MOGAD) | 6 |
| abstract_inverted_index.0.001). | 283 |
| abstract_inverted_index.0.045). | 228 |
| abstract_inverted_index.Medical | 95 |
| abstract_inverted_index.Methods | 75 |
| abstract_inverted_index.Results | 174 |
| abstract_inverted_index.benefit | 298 |
| abstract_inverted_index.central | 13 |
| abstract_inverted_index.cohort, | 250 |
| abstract_inverted_index.cohort. | 256 |
| abstract_inverted_index.course, | 29 |
| abstract_inverted_index.disease | 5 |
| abstract_inverted_index.divided | 106 |
| abstract_inverted_index.factors | 49, 138, 193, 244 |
| abstract_inverted_index.nervous | 14 |
| abstract_inverted_index.period, | 57 |
| abstract_inverted_index.points, | 267 |
| abstract_inverted_index.predict | 334 |
| abstract_inverted_index.present | 23 |
| abstract_inverted_index.relapse | 53, 196, 271, 277, 321, 336 |
| abstract_inverted_index.system, | 15 |
| abstract_inverted_index.therapy | 220 |
| abstract_inverted_index.≥1:32 | 214 |
| abstract_inverted_index.&lt; | 282 |
| abstract_inverted_index.=0.007), | 217 |
| abstract_inverted_index.=0.032), | 209 |
| abstract_inverted_index.=0.040), | 203 |
| abstract_inverted_index.December | 80 |
| abstract_inverted_index.Hospital | 92 |
| abstract_inverted_index.absolute | 121 |
| abstract_inverted_index.analysis | 163 |
| abstract_inverted_index.antibody | 212 |
| abstract_inverted_index.clinical | 71, 100 |
| abstract_inverted_index.cortical | 204 |
| abstract_inverted_index.decision | 161 |
| abstract_inverted_index.disorder | 10 |
| abstract_inverted_index.enrolled | 83 |
| abstract_inverted_index.evaluate | 168 |
| abstract_inverted_index.identify | 135 |
| abstract_inverted_index.logistic | 118, 130 |
| abstract_inverted_index.nomogram | 149, 237, 263, 312, 327 |
| abstract_inverted_index.operator | 125 |
| abstract_inverted_index.patients | 85, 340 |
| abstract_inverted_index.randomly | 105 |
| abstract_inverted_index.receiver | 153 |
| abstract_inverted_index.relapse. | 141, 358 |
| abstract_inverted_index.relapsed | 180 |
| abstract_inverted_index.relapses | 32 |
| abstract_inverted_index.repeated | 31 |
| abstract_inverted_index.training | 109, 249 |
| abstract_inverted_index.validate | 171 |
| abstract_inverted_index.challenge | 66 |
| abstract_inverted_index.collected | 98 |
| abstract_inverted_index.decisions | 352 |
| abstract_inverted_index.developed | 330 |
| abstract_inverted_index.diagnosed | 86 |
| abstract_inverted_index.diagnosis | 72 |
| abstract_inverted_index.excellent | 296 |
| abstract_inverted_index.increased | 40 |
| abstract_inverted_index.operating | 154 |
| abstract_inverted_index.patients, | 178 |
| abstract_inverted_index.phenotype | 206 |
| abstract_inverted_index.prognosis | 38 |
| abstract_inverted_index.relapsing | 28 |
| abstract_inverted_index.selection | 124 |
| abstract_inverted_index.shrinkage | 122 |
| abstract_inverted_index.treatment | 351 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.Conclusion | 325 |
| abstract_inverted_index.Currently, | 42 |
| abstract_inverted_index.Univariate | 117 |
| abstract_inverted_index.University | 96 |
| abstract_inverted_index.accurately | 333 |
| abstract_inverted_index.autoimmune | 9 |
| abstract_inverted_index.clinicians | 348 |
| abstract_inverted_index.developed. | 151 |
| abstract_inverted_index.internally | 170 |
| abstract_inverted_index.monophasic | 26 |
| abstract_inverted_index.predicting | 51, 195, 319 |
| abstract_inverted_index.prediction | 61, 143, 238, 301 |
| abstract_inverted_index.regression | 127, 131 |
| abstract_inverted_index.treatment. | 74 |
| abstract_inverted_index.validation | 114, 255 |
| abstract_inverted_index.antibodies. | 20 |
| abstract_inverted_index.calibration | 158, 285, 307 |
| abstract_inverted_index.consistency | 289 |
| abstract_inverted_index.constructed | 240 |
| abstract_inverted_index.disability. | 41 |
| abstract_inverted_index.established | 60 |
| abstract_inverted_index.identified: | 198 |
| abstract_inverted_index.independent | 136, 191 |
| abstract_inverted_index.prediction, | 291 |
| abstract_inverted_index.regression, | 119 |
| abstract_inverted_index.significant | 316 |
| abstract_inverted_index.constructed, | 146 |
| abstract_inverted_index.demonstrated | 287 |
| abstract_inverted_index.demonstrates | 315 |
| abstract_inverted_index.encephalitis | 205 |
| abstract_inverted_index.glycoprotein | 3 |
| abstract_inverted_index.multivariate | 129 |
| abstract_inverted_index.performance. | 173 |
| abstract_inverted_index.personalized | 70 |
| abstract_inverted_index.seropositive | 18 |
| abstract_inverted_index.characterized | 16 |
| abstract_inverted_index.characteristic | 155 |
| abstract_inverted_index.distinguishing | 268 |
| abstract_inverted_index.oligodendrocyte | 2 |
| abstract_inverted_index.immunosuppressive | 219 |
| abstract_inverted_index.antibody-associated | 4 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5101896380, https://openalex.org/A5032164866 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I17721919, https://openalex.org/I4210125748 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.94733322 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |