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Hypoglycemic encephalopathy mimicking acute ischemic stroke in clinical presentation and magnetic resonance imaging: a case report.

ABSTRACT: BACKGROUND:The imaging findings of hypoglycemic encephalopathy can be considerably similar to those of ischemic infarction or toxic leukoencephalopathy. We demonstrated unusual magnetic resonance (MR) imaging features of hypoglycemic encephalopathy which can be confused with other pathology both on imaging and acute clinical presentation. The diffusion-weighted imaging (DWI) and apparent diffusion coefficients (ADC) map findings in our case further supports the hypothesis of hypoglycemia-induced "excitotoxic injury" of glial cells and myelin sheath that might protect neuron axons from intracellular edema and irreversible damage. CASE PRESENTATION:A 72-year-old woman presented with poor appetite and was initially drowsy at home; the symptoms progressed to loss of consciousness accompanied by mild incontinence. The initial glucose level was 44 mg/dL, but no nausea, vomiting, fever, or cold sweating was reported. Physical examination after intravenous glucose supplementation revealed the absence of focal neurological signs, facial palsy, and tongue or eye deviations. The images obtained 24 h after symptoms onset revealed symmetrical hyperintensities on DWI (b-value: 1000) associated with hypointensities on ADC map along the corticospinal tract, from the levels of the cerebral peduncle and the posterior limbs of the internal capsule to the level of the corona radiata, which may mimic the imaging findings of acute ischemic infarction or amyotrophic lateral sclerosis. The patient received sliding-scale insulin therapy and rehabilitation, and she recovered consciousness without motor function deficits after 1 month. Moreover, repeat DWI and ADC map showed the complete disappearance of the lesions. CONCLUSIONS:In the phenomenon of excitotoxic injury, axons could be protected from intracellular edema and irreversible damage, which may explain the reversible clinical symptoms and imaging abnormality after controlling for blood glucose because of the preserved motor axon. The diagnosis of acute symptomatic hypoglycemic encephalopathy through clinical and imaging features can be challenging. It is crucial to differentiate it from ischemic encephalopathy since the management and clinical outcome are different.

SUBMITTER: Chuang KI

PROVIDER: S-EPMC6344988 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportancePredicting infarct size and location is important for decision-making and prognosis in patients with acute stroke.ObjectivesTo determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods.Design, setting, and participantsIn this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019.Main outcomes and measuresArea under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of ≥0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion.ResultsAmong the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, -14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance.Conclusions and relevanceThe deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

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Hypoglycemic encephalopathy mimicking acute ischemic stroke in clinical presentation and magnetic resonance imaging: a case report.

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