ABSTRACT
Background@#and Purpose: Contrast enhancement of the oculomotor nerve in MRI was recently noticed in patients with clinical ischemic isolated oculomotor nerve palsy (iIONP). The opinions about whether this is a sign of inflammation and whether or not to administer steroids vary between doctors. The study aimed to determine the associations between this enhancement and vascular-disease risk factors (VRFs) and inflammatory factors in iIONP patients. @*Methods@#The study recruited patients who had experienced iIONP during the previous 2 years. They were divided into groups A and B based on whether or not they exhibited an enhanced oculomotor nerve in MRI of the cavernous sinus using thin-section, fat-suppressed, and contrast-enhanced sequences. VRFs, inflammatory factors, and improvement scores were compared between the two groups. @*Results@#Most (71.1%) of the 45 included iIONP patients had enhanced oculomotor nerves in MRI. VRFs, periorbital pain, elevated C-reactive protein and erythrocyte sedimentation rate, the neutrophil-to-lymphocyte ratio, and the platelet-to-lymphocyte ratio were not significantly associated with the enhancement. Four of the five patients in group A exhibited an elevated cerebrospinal fluid (CSF) IgG synthesis rate. The improvement score of eight patients who received 80 mg of methylprednisolone in addition to the routine therapy was not significantly different from the scores of the other patients (p=0.485). @*Conclusions@#More than half of the iIONP patients had an enhanced oculomotor nerve in MRI. A few of them also had elevated CSF IgG synthesis rate, but no further evidence for inflammation was found. The administration of steroids seemed to have no benefit other than increasing the blood glucose level.
ABSTRACT
Objective To compare the accuracy of electroencephalography (EEG) grading scale with amplitude-integrated electroencephalography (aEEG) in predicting poor outcomes (3-month), who sustained coma after cardiopulmonary resuscitation (CPR) in adults. Methods A retrospective study was conducted. The patients with post-anoxic coma admitted to intensive care unit (ICU) of Tongren Hospital, Capital Medical University from March 2010 to June 2017 were enrolled. EEG was registered and recorded at least once within 7 days of coma after CPR, while not being subjected to therapeutic hypothermia. General data, Glasgow coma scale (GCS), EEG grading and aEEG model were collected. According to Glasgow prognosis score (GOS) of 3-month outcome, patients were divided into poor prognosis group (GOS 1-2) and good prognosis group (GOS 3-5), and the differences of related indexes between the two groups were compared. The predictive ability of aEEG model and EEG grading for brain function prognosis was evaluated by receiver operating characteristic (ROC) curve. Results Fifty-four patients were included, with 31 males and 23 females, and age of (53.9±19.3) years. Among the EEG Young grades, 17 cases (31.5%) were grade 1, 4 cases (7.4%) were grade 2-5, and 33 cases (61.1%) were grade 6. Among the aEEG model grades, 26 cases (48.1%) had slow wave pattern grade 1, 23 cases (42.6%) had suppressed mode grade 4, 4 cases (7.4%) had status epilepticus mode grade 2, and 1 case (1.9%) had burst suppression mode grade 3. Thirty-six patients had poor prognosis 3-month after onset, 26 of them died and 10 had persistent vegetative state. The prognosis was good in 18 cases, including 16 cases with severe neurological disability and 2 cases with moderate neurological disability. There was no significant difference in gender, age, anoxic time between two groups with different prognosis, while the degree of consciousness disorder in poor prognosis group was more severe than that in good prognosis group (GCS score: 4.1±1.7 vs. 5.0±2.1, P < 0.05). The consistency test showed that different physicians had good consistency in EEG grading and aEEG model (Kappa values were 0.917 and 0.932, respectively). It was shown by ROC curve analysis that the area under ROC curve (AUC) of aEEG model and EEG grading for predicting poor prognosis of coma patients after CPR were 0.815 and 0.720, respectively (both P < 0.01); when the cut-off value of aEEG was 2.5, the sensitivity was 79.3%, the specificity was 77.4%, the positive likelihood ratios (PLR) was 3.508, and the negative likelihood ratios (NLR) was 0.267; when the cut-off value of EEG grading was 4.5, the sensitivity was 82.8%, the specificity was 61.3%, the PLR was 2.140, and NLR was 0.281. Conclusions aEEG model was more accurate in prognosticating poor outcomes (3-month) in patients with post-anoxic coma, when compared to EEG grading. Its operation was simple, so aEEG is very suitable in ICU.
