Acute Hemorrhagic Necrotizing Encephalopathy in a COVID-19 Patient With a Response to Intravenous Immunoglobulin Therapy
Bibliographic record
Abstract
BACKGROUND Acute hemorrhagic necrotizing encephalopathy (AHNE) is a rare, immune-mediated condition that follows viral infection and has a poor prognosis. Previously reported cases of AHNE have described a favorable clinical response to intravenous immunoglobulin (IVIG) therapy combined with corticosteroids. This report describes the case of a 67-year-old Bahraini man with AHNE associated with Coronavirus Disease-19 (COVID-19) with a response to IVIG therapy. CASE REPORT We report a 67-year-old Bahraini man with type 2 diabetes, hypertension, and a prior hemi-colectomy, presenting with acute febrile encephalopathy and a positive SARS-CoV-2 test result. The patient presented with confusion and rapid neurological decline. A magnetic resonance imaging (MRI) scan demonstrated symmetrical hemorrhagic lesions in the bilateral thalami, pons, and periventricular white matter, consistent with AHNE. Despite high-dose intravenous methylprednisolone, his condition worsened. Subsequent intravenous immunoglobulin therapy (IVIG) led to gradual neurological improvement. After a 4-month hospital stay with intensive rehabilitation, he was discharged ambulatory with minimal assistance, residual spastic dysarthria, and pyramidal weakness. A follow-up brain MRI scan showed marked lesion regression. CONCLUSIONS This case highlights the need to consider AHNE in adults with unexplained acute encephalopathy, especially amid recent viral infections like SARS-CoV-2. The patient's improvement with IVIG draws attention to its potential therapeutic role in selected adults, while also revealing the limitations of pediatric prognostic tools such as Acute Necrotizing Encephalopathy severity score (ANE-SS) in this population. Further research is needed to clarify AHNE pathophysiology, define adult-specific outcome predictors, and establish standardized diagnostic and treatment protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".