Electroencephalogram variability in patients with cirrhosis associates with the presence and severity of hepatic encephalopathy.

BACKGROUND & AIMS: The outputs of physiological systems fluctuate in a complex manner even under resting conditions. Decreased variability or increased regularity of these outputs is documented in several disease states. Changes are observed in the spatial and temporal configuration of the electroencephalogram (EEG) in patients with hepatic encephalopathy (HE), but there is no information on the variability of the EEG signal in this condition. The aim of this study was to measure and characterize EEG variability in patients with cirrhosis and to determine its relationship to neuropsychiatric status. METHODS: Eyes-closed, awake EEGs were obtained from 226 patients with cirrhosis, classified, using clinical and psychometric criteria, as neuropsychiatrically unimpaired (n=127) or as having minimal (n=21) or overt (n=78) HE, and from a reference population of 137 healthy controls. Analysis of EEG signal variability was undertaken using continuous wavelet transform and sample entropy. RESULTS: EEG variability was reduced in the patients with cirrhosis compared with the reference population (coefficient of variation: 21.2% [19.3-23.4] vs. 22.4% [20.8-24.5]; p<0.001). A significant association was observed between EEG variability and neuropsychiatric status; thus, variability was increased in the patients with minimal HE compared with their neuropsychiatrically unimpaired counterparts (sample entropy: 0.98 [0.87-1.14] vs. 0.83 [0.75-0.95]; p=0.02), and compared with the patients with overt HE (sample entropy: 0.98 [0.87-1.14] vs. 0.82 [0.71-1.01]; p=0.01). CONCLUSIONS: Variability of the EEG is associated with both the presence and severity of HE. This novel finding may provide new insights into the pathophysiology of HE and provide a means for monitoring patients over time. LAY SUMMARY: Decreased variability or increased regularity of physiological systems is documented in several disease states. Variability of the electroencephalogram was found to be associated with both the presence and severity of brain dysfunction in patients with chronic liver disease.

New spectral thresholds improve the utility of the electroencephalogram for the diagnosis of hepatic encephalopathy.

OBJECTIVE: The utility of the electroencephalogram (EEG) for the diagnosis of hepatic encephalopathy, using conventional spectral thresholds, is open to question. The aim of this study was to optimise its diagnostic performance by defining new spectral thresholds. METHODS: EEGs were recorded in 69 healthy controls and 113 patients with cirrhosis whose neuropsychiatric status was classified using clinical and psychometric criteria. New EEG spectral thresholds were calculated, on the parietal P3-P4 lead derivation, using an extended multivariable receiver operating characteristic curve analysis. Thresholds were validated in a separate cohort of 68 healthy controls and 113 patients with cirrhosis. The diagnostic performance of the newly derived spectral thresholds was further validated using a machine learning technique. RESULTS: The diagnostic performance of the new thresholds (sensitivity 75.0%; specificity 77.4%) was better balanced than that of the conventional thresholds (58.3%; 93.2%) and comparable to the performance of a machine learning technique (72.9%; 76.8%). The diagnostic utility of the new thresholds was confirmed in the validation cohort. CONCLUSIONS: Adoption of the new spectral thresholds would significantly improve the utility of the EEG for the diagnosis of hepatic encephalopathy. SIGNIFICANCE: These new spectral EEG thresholds optimise the performance of the EEG for the diagnosis of hepatic encephalopathy and can be adopted without the need to alter data recording or the initial processing of traces.

A novel semi-automatic segmentation method for volumetric assessment of the colon based on magnetic resonance imaging.

PURPOSE: To develop a novel semi-automatic segmentation method for quantification of the colon from magnetic resonance imaging (MRI). METHODS: Fourteen abdominal T2-weighted and dual-echo Dixon-type water-only MRI scans were obtained from four healthy subjects. Regions of interest containing the colon were outlined manually on the T2-weighted images. Segmentation of the colon and feces was obtained using k-means clustering and image registration. Regional colonic and fecal volumes were obtained. Inter-observer agreement between two observers was assessed using the Dice similarity coefficient as measure of overlap. RESULTS: Colonic segmentations showed wide variation in volume and morphology between subjects. Colon volumes of the four healthy subjects for both observers were (median [interquartile range]) ascending colon 200 mL [169.5-260], transverse 200.5 mL [113.5-242.5], descending 148 mL [121.5-178.5], sigmoid-rectum 277 mL [192-345], and total 819 mL [687-898.5]. Overlap agreement for the total colon segmentation between the two observers was high with a Dice similarity coefficient of 0.91 [0.84-0.94]. The colon volume to feces volume ratio was on average 0.7. CONCLUSION: Regional colon volumes were comparable to previous findings using fully manual segmentation. The method showed good agreement between observers and may be used in future studies of gastrointestinal disorders to assess colon and fecal volume and colon morphology. Novel insight into morphology and quantitative assessment of the colon using this method may provide new biomarkers for constipation and abdominal pain compared to radiography which suffers from poor reliability.