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Predicting epilepsy after new onset refractory status epilepticus due to autoimmune encephalitis: The DAME score.

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Title: Predicting epilepsy after new onset refractory status epilepticus due to autoimmune encephalitis: The DAME score.
Authors: Lattanzi, Simona (AUTHOR), Matricardi, Sara (AUTHOR), Vogrig, Alberto (AUTHOR), Pauletto, Giada (AUTHOR), Nosadini, Margherita (AUTHOR), Sartori, Stefano (AUTHOR), Massa, Federico (AUTHOR), Benedetti, Luana (AUTHOR), Meletti, Stefano (AUTHOR), Bisulli, Francesca (AUTHOR), Freri, Elena (AUTHOR), Operto, Francesca Felicia (AUTHOR), Bozzetti, Silvia (AUTHOR), Mariotto, Sara (AUTHOR), Beretta, Simone (AUTHOR), Rosati, Eleonora (AUTHOR), Cesaroni, Elisabetta (AUTHOR), Marini, Carla (AUTHOR), Granata, Tiziana (AUTHOR), Villani, Flavio (AUTHOR)
Source: Epilepsia (Series 4). Apr2026, Vol. 67 Issue 4, p1792-1801. 10p.
Subjects: Autoimmune encephalitis, Prediction algorithms, Epilepsy, Electroencephalography, Status epilepticus, Disease risk factors, Magnetic resonance imaging
Abstract: Objective: This study aimed to identify risk factors and develop a predictive scoring system for autoimmune‐associated epilepsy in subjects with autoimmune encephalitis presenting with new onset refractory status epilepticus (NORSE). Methods: This retrospective, multicenter, cohort study included subjects who presented with NORSE at the onset of autoimmune encephalitis and had at least 24 months of follow‐up after immunotherapy. The outcome was the development of autoimmune‐associated epilepsy, defined as persistent seizures despite adequate immunotherapy and absence of active inflammation. Factors independently associated with the outcome were identified through a backward stepwise selection. Adjusted regression coefficients of each independent predictor were transformed to produce a points‐based risk‐scoring system. Results: Seventy participants were included (median age = 24.2 years, 38.6% male). During a median follow‐up of 53 months, 54.3% of subjects developed autoimmune‐associated epilepsy. Status epilepticus duration ≥ 10 days (odds ratio [OR] = 31.14, 95% confidence interval [CI] = 2.12–456.87, p =.012), positivity for antibodies against surface antigens (OR =.12, 95% CI =.02–.85, p =.034), bitemporal magnetic resonance imaging (MRI) abnormalities suggestive of autoimmune encephalitis during acute stage (OR = 49.80, 95% CI = 2.95–841.77, p =.007), and interictal epileptiform discharges during electroencephalographic (EEG) follow‐up (OR = 71.32, 95% CI = 6.48–785.32, p <.001) were independently associated with the study outcome. The duration–antibodies–MRI–EEG (DAME) score was developed as an integer‐based scoring system predictive of autoimmune‐associated epilepsy. With an optimal cutoff of ≥3 points, it yielded a sensitivity of 86.8%, a specificity of 87.5%, and an overall accuracy of 87.1%. Significance: The DAME score could serve as a user‐friendly score to predict the risk of autoimmune‐associated epilepsy in patients with NORSE due to autoimmune encephalitis. [ABSTRACT FROM AUTHOR]
Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Predicting epilepsy after new onset refractory status epilepticus due to autoimmune encephalitis: The&#160;DAME&#160;score.
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  Data: Objective: This study aimed to identify risk factors and develop a predictive scoring system for autoimmune‐associated epilepsy in subjects with autoimmune encephalitis presenting with new onset refractory status epilepticus (NORSE). Methods: This retrospective, multicenter, cohort study included subjects who presented with NORSE at the onset of autoimmune encephalitis and had at least 24 months of follow‐up after immunotherapy. The outcome was the development of autoimmune‐associated epilepsy, defined as persistent seizures despite adequate immunotherapy and absence of active inflammation. Factors independently associated with the outcome were identified through a backward stepwise selection. Adjusted regression coefficients of each independent predictor were transformed to produce a points‐based risk‐scoring system. Results: Seventy participants were included (median age = 24.2 years, 38.6% male). During a median follow‐up of 53 months, 54.3% of subjects developed autoimmune‐associated epilepsy. Status epilepticus duration ≥ 10 days (odds ratio [OR] = 31.14, 95% confidence interval [CI] = 2.12–456.87, p =.012), positivity for antibodies against surface antigens (OR =.12, 95% CI =.02–.85, p =.034), bitemporal magnetic resonance imaging (MRI) abnormalities suggestive of autoimmune encephalitis during acute stage (OR = 49.80, 95% CI = 2.95–841.77, p =.007), and interictal epileptiform discharges during electroencephalographic (EEG) follow‐up (OR = 71.32, 95% CI = 6.48–785.32, p &lt;.001) were independently associated with the study outcome. The duration–antibodies–MRI–EEG (DAME) score was developed as an integer‐based scoring system predictive of autoimmune‐associated epilepsy. With an optimal cutoff of ≥3 points, it yielded a sensitivity of 86.8%, a specificity of 87.5%, and an overall accuracy of 87.1%. Significance: The DAME score could serve as a user‐friendly score to predict the risk of autoimmune‐associated epilepsy in patients with NORSE due to autoimmune encephalitis. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Epilepsia (Series 4) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.1002/epi.70081
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