Predicted estimates of resting energy expenditure have limited clinical utility in patients with cirrhosis.

BACKGROUND & AIMS: Malnutrition is associated with adverse clinical outcomes in patients with cirrhosis. Accurate assessment of energy requirements is needed to optimize dietary intake. Resting energy expenditure (REE), the major component of total energy expenditure, can be measured using indirect calorimetry (mREE) or estimated using prediction equations (pREE). This study assessed the usefulness of predicted estimates of REE in this patient population. METHODS: Individual mREE data were available for 900 patients with cirrhosis (mean [±1 SD] age 55.7±11.6 years-old; 70% men; 52% south-east Asian) and 282 healthy controls (mean age 36.0±12.8 years-old; 52% men; 18% south-east Asian). Metabolic status was classified using thresholds based on the mean ± 1 SD of the mREE in the healthy controls. Comparisons were made between mREE and pREE estimates obtained using the Harris-Benedict, Mifflin, Schofield and Henry equations. Stepwise regression was used to build 3 new prediction models which included sex, ethnicity, body composition measures, and model for end-stage liver disease scores. RESULTS: The mean mREE was significantly higher in patients than controls when referenced to dry body weight (22.4±3.8 cf. 20.8±2.6 kcal/kg/24 hr; p <0.001); there were no significant sex differences. The mean mREE was significantly higher in Caucasian than Asian patients (23.1±4.4 cf. 21.7±2.9 kcal/kg/24 hr; p <0.001). Overall, 37.1% of Caucasian and 25.3% of Asian patients were classified as hypermetabolic. The differences between mREE and pREE were both statistically and clinically relevant; in the total patient population, pREE estimates ranged from 501 kcal/24 hr less to 548 kcal/24 hr more than the mREE. Newly derived prediction equations provided better estimates of mREE but still had limited clinical utility. CONCLUSIONS: Prediction equations do not provide useful estimates of REE in patients with cirrhosis. REE should be directly measured. LAY SUMMARY: People with cirrhosis are often malnourished and this has a detrimental effect on outcome. Provision of an adequate diet is very important and is best achieved by measuring daily energy requirements and adjusting dietary intake accordingly. Prediction equations, which use information on age, sex, weight, and height can be used to estimate energy requirements; however, the results they provide are not accurate enough for clinical use, particularly as they vary according to sex and ethnicity.

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.