Clinical variables that help in predicting the presence of autoantibodies in patients with acute encephalitis

Recent advances in non-Huntington's disease choreas

INTRODUCTION

Chorea is primarily due to an imbalance of basal ganglia output pathways, often due to dysfunction or degeneration of the caudate nucleus and putamen, and can be due to many causes.

METHODS

We reviewed the recent literature to identify newly-recognized causes of chorea, including auto-immune, metabolic, and genetic. We also focused upon developments in mechanisms relating to underlying pathophysiology of certain genetic choreas and advances in therapeutics.

RESULTS

Novel autoantibodies continue to be identified as causes of chorea. Both COVID-19 infection and vaccination are reported to result rarely in chorea, although in some cases causality is not clearly established. Advances in genetic testing continue to find more causes of chorea, and to expand the phenotype of known genetic disorders. Deep brain stimulation can be successful in certain circumstances.

CONCLUSION

Our understanding of mechanisms underlying this movement disorder continues to advance, however much remains to be elucidated.

Movement disorders in autoimmune encephalitis: an update

Autoimmune encephalitis (AE) is a form of encephalitis resulting from an immune response targeting central nervous system antigens, which is characterized by cognitive impairment, neuropsychiatric symptoms, seizures, movement disorders (MDs), and other encephalopathy symptoms. MDs frequently manifest throughout the progression of the disease, with recurrent involuntary movements leading to discomfort and, in some cases, necessitating admission to the intensive care unit. Prompt identification and management of MDs can aid in the diagnosis and prognosis of AE. This review synthesizes current knowledge on the characteristics, underlying mechanisms, and treatment options for MDs in the context of AE.

Movement disorders in cell surface antibody mediated autoimmune encephalitis: a meta-analysis

Background

Autoimmune encephalitis (AE) is an increasingly recognized neuroinflammatory disease entity in which early detection and treatment leads to the best clinical outcomes. Movement disorders occur in AE but their characteristics are not well defined.

Objectives

To identify the frequency, classification, and prognostic significance of movement disorders in AE.

Methods

We conducted a systematic review and random-effects meta-analysis of movement disorders in cell surface antibody mediated AE. The frequency of any movement disorder as well as the classification of movement disorders in AE serotypes was determined. We looked at adults 18 years and older and included publications that described at least 10 cases. We used the following four electronic databases: Medline (Ovid), EMBASE (Ovid), APA Psychinfo, and Cochrane library.

Results

A total of 1,192 titles and abstracts were reviewed. Thirty-seven studies were included in the final meta-analysis. At least one kind of movement disorder was present in 40% of the entire AE cohort, 53% with anti-NMDA receptor antibodies, 33% with anti-CASPR2 antibodies, 30% with anti-LGI1 antibodies and 13% with anti-GABA receptor antibodies. Dyskinesia was the commonest movement disorder in anti-NMDA antibody mediated AE and faciobrachial dystonic seizures were most frequent in anti-LGI1 antibody mediated AE. Patients with a movement disorder tended to have a higher mortality. The risk of bias in the included studies was mostly moderate or high.

Conclusion

Movement disorders are common in AE and their identification, in conjunction with other clinical and paraclinical features, may facilitate earlier diagnosis. The prognostic implications of movement disorders in AE warrant further dedicated study.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier: CRD42023386920.

Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI

BACKGROUND

Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources.

PURPOSE

To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE.

STUDY TYPE

Retrospective.

POPULATION

One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation.

FIELD STRENGTH/SEQUENCE

3.0 T; fast spin-echo (T₁ WI, T₂ WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging.

ASSESSMENT

Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists.

STATISTICAL TESTS

The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant).

RESULTS

In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%).

DATA CONCLUSION

The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.