Thakolwiboon, Smathorn

Background Data regarding long-term recovery from autoimmune encephalitis (AE) remain limited. Methods This retrospective observational study investigated outcomes in 182 patients who met the 2016 criteria for definite AE. Recovery data were available in 172 patients. Follow-up data at ≥24 months post-attack were available for 119. Recovery trajectory, residual symptoms, outcome predictors and causes of post-AE death were assessed. Results Of 172 patients, 138 (80%) achieved good recovery (modified Rankin Scale (mRS) ≤2) with a median recovery time of 4 months (95% CI: 2 to 6 months). Recovery varied by associated neural antibody, with the best recovery observed in leucine-rich glioma-inactivated 1 (97% good recovery, median recovery time 0 (0 to 2) months). Paraneoplastic AE (p=0.007), severe attacks (eg, mRS ≥4 at attack, p=0.007) and cerebrospinal fluid pleocytosis (p=0.005) were associated with a lower likelihood of good recovery, while seizure presentation (p=0.026) was associated with better recovery. Despite good recovery, several residual symptoms persisted ≥24 months post-AE, including cognitive deficits (53%), seizures (26%), depression (23%), sleep disorders (25%), brainstem/cerebellar symptoms (13%), other movement disorders (14%) and autonomic symptoms (12%). Predictors of long-term sequelae included disabling cognitive deficit at onset and delayed immunotherapy for post AE-dementia, and medial temporal atrophy as well as escalation to cyclophosphamide therapy for both drug-resistant epilepsy and chronic depression. Of 182 patients, 20 (11%) died; the most common cause of death was progression of AE (6/20 (30%)). Conclusion While the majority of patients achieved functional independence after AE, several residual symptoms persisted. Several clinical and paraclinical features were associated with long-term sequelae.

ABSTRACTBackground and AimsChronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is difficult to distinguish from mimicking disorders, with misdiagnosis resulting in IVIG overutilization. We evaluate a clinical‐electrophysiological model to facilitate CIDP versus mimic neuropathy prediction.MethodsUsing the European Academy of Neurology/Peripheral Nerve Society (EAN/PNS) 2021 CIDP guidelines we derived 26 clinical and 144 nerve conduction variables. The model was generated and validated utilizing total CIDP (n = 129) and mimics (n = 309); including (1) IgG4‐nodopathies; (2) POEMS (polyneuropathy–organomegaly–endocrinopathy–monoclonal protein‐skin changes); (3) anti‐myelin‐associated‐glycoprotein; (4) paraneoplastic; (5) Waldenström B‐cell lymphoma; (6) diabetic neuropathies; (7) amyloidosis; (8) Charcot–Marie–Tooth; (9) motor neuropathies/neuronopathies; and (10) idiopathic‐inflammatory‐myopathies.ResultsWe analyzed 9282 clinical and 51 408 electrophysiological data points. Univariate analysis identified 11 of 26 clinical variables with significant odds ratios. A multivariate regression model using four clinical and two electrophysiologic variables achieved 93% area‐under‐curve (95% CI 91–95): progression over 8 weeks (OR 40.66, 95% CI 5.31–311.36), absent autonomic involvement (OR 17.82, 95% CI 2.93–108.24), absent muscle atrophy (OR 16.65, 95% CI 3.27–84.73), proximal weakness (OR 3.63, 95% CI 1.58–8.33), ulnar motor conduction velocity slowing < 35.7 m/s (OR 5.21, 95% CI 2.13–12.76), and ulnar motor conduction block (OR 13.37, 95% CI 2.47–72.40). A web‐based probability calculator (https://news.mayocliniclabs.com/cidp‐calculator/) was developed, with 100% sensitivity and 68% specificity at a 92% probability threshold. Specificity improved to 93% when considering “red flags,” electrophysiologic criteria, and laboratory testing.InterpretationA probability calculator using clinical electrophysiological variables assists CIDP differentiation from mimics, with scores below 92% unlikely to have CIDP. The highest specificity is achieved by considering clinical “red flags,” electrophysiologic demyelination, and laboratory testing.

AbstractObjectiveWe describe neurologic phenotype, clinical associations, and outcomes in autoimmune brainstem encephalitis.MethodsMedical records of neural‐IgG positive autoimmune brainstem encephalitis patients diagnosed at Mayo Clinic (January 1, 2006–December 31, 2022) were reviewed.ResultsNinety‐eight patients (57 male) were included. Median age of symptom onset was 51 years (range, 8 months‐85 years). Frequent presenting features were ≥1: diplopia (80%), ataxia (78%), dysarthria (68%), vestibulocochlear symptoms (67%), dysphagia (61%), nausea/vomiting (42%), and facial weakness (32%). Altered mental status (11%) was uncommon. Neural antibodies detected were as follows: KLHL‐11 (26 patients), GAD65 (high titer, 12), ANNA‐1 (anti‐Hu, 8), ANNA‐2 (anti‐Ri, 8), Ma2 (7), IgLON‐5 (6), AQP4 (6), MOG (4), glycine receptor (4), GQ1B (4), PCA‐1 (anti‐Yo, 4), DPPX (2), neurochondrin (2), neurofilament (2), NMDA‐R (2), AGNA‐1 (SOX‐1, 1), ANNA‐3 (DACH1, 1), amphiphysin (1), CRMP‐5 (1), ITPR‐1 (1), PCA‐Tr (DNER, 1), and PDE10A (1). Cancer was identified in 55 patients: germ cell (23 patients; 3 extra‐testicular), ductal breast adenocarcinoma (8), small cell carcinoma (6, lung 4), adenocarcinomas (6), neuroendocrine carcinoma (3), hematologic (2), squamous cell (2), and other (7). Median modified Ranking score (mRS) at last follow‐up was 3 (range, 0–6). Factors associated with poor outcome included abnormal brain MRI, bulbar symptoms, and elevated CSF IgG index. Kaplan–Meier analysis revealed faster progression to wheelchair in patients who were immunotherapy refractory and with elevated CSF IgG index. Diagnostic criteria for autoimmune brainstem encephalitis (definite and probable) are proposed.InterpretationAutoimmune brainstem encephalitis is a distinct clinical subphenotype of autoimmune encephalitis. Abnormal brain MRI, bulbar symptoms, and elevated CSF‐IgG index associate with poor outcome.

Objective The aim of this study was to assess the diagnostic utility of cerebrospinal fluid (CSF) myelin oligodendrocyte glycoprotein antibodies (MOG‐IgG) testing. Methods We retrospectively identified patients for CSF MOG‐IgG testing from January 1, 1996, to May 1, 2023, at Mayo Clinic and other medical centers that sent CSF MOG‐IgG for testing including: controls, 282; serum MOG‐IgG positive MOG antibody‐associated disease (MOGAD), 74; serum MOG‐IgG negative high‐risk phenotypes, 73; serum false positive MOG‐IgG with alternative diagnoses, 18. A live cell‐based assay assessed CSF MOG‐IgG positivity (IgG‐binding‐index [IBI], ≥2.5) using multiple anti‐human secondary antibodies and end‐titers were calculated if sufficient sample volume. Correlation of CSF MOG‐IgG IBI and titer was assessed. Results The pan‐IgG Fc‐specific secondary was optimal, yielding CSF MOG‐IgG sensitivity of 90% and specificity of 98% (Youden's index 0.88). CSF MOG‐IgG was positive in: 4/282 (1.4%) controls; 66/74 (89%) serum MOG‐IgG positive MOGAD patients; and 9/73 (12%) serum MOG‐IgG negative patients with high‐risk phenotypes. Serum negative but CSF positive MOG‐IgG accounted for 9/83 (11%) MOGAD patients, and all fulfilled 2023 MOGAD diagnostic criteria. Subgroup analysis of serum MOG‐IgG low‐positives revealed CSF MOG‐IgG positivity more in MOGAD (13/16[81%]) than other diseases with false positive serum MOG‐IgG (3/15[20%]) ( p = 0.01). CSF MOG‐IgG IBI and CSF MOG‐IgG titer (both available in 29 samples) were correlated (Spearman's r = 0.64, p < 0.001). Interpretation CSF MOG‐IgG testing has diagnostic utility in patients with a suspicious phenotype but negative serum MOG‐IgG, and those with low positive serum MOG‐IgG results and diagnostic uncertainty. These findings support a role for CSF MOG‐IgG testing in the appropriate clinical setting. ANN NEUROL 2024;96:34–45