Energy Expenditure and Liver Transplantation: What We Know and Where We Are.
Bárbara Chaves SantosMaria Isabel Toulson Davisson CorreiaLucilene Rezende AnastácioPublished in: JPEN. Journal of parenteral and enteral nutrition (2020)
Patients with end-stage liver disease (ESLD) and undergoing liver transplantation (LTx) commonly present with malnutrition attributed to various etiologies. One of the causes is potential hypermetabolism resulting from increased resting energy expenditure (REE). After the surgery, it is hypothesized that these patients show a reduction in REE, which may contribute to the weight gain observed in this population. However, there have been controversial results regarding the metabolic status of ESLD patients and liver recipients, which has led us to critically review the pertinent literature. We enrolled studies with the following goals: assessment of REE of these patients either before or after surgery by using indirect calorimetry (measured REE [mREE]) and comparison of these mREE values with those of healthy controls or with REE values obtained using predictive equations (predicted REE [pREE]). For most patients, mREE and pREE values were comparable. However, ≥5.3% of patients exhibited hypermetabolism when the mREE was compared with the pREE using the Harris-Benedict formula. Three follow-up studies that were conducted postsurgery showed a progressive reduction in the mREE for ≤1 year. However, conflicting data have been published, and cross-sectional studies have not reported hypometabolic patients. In conclusion, there is no consensus regarding the metabolic status of pre-LTx and post-LTx patients, which may be due to differences in the methods used for comparison. Therefore, we highlight this aspect of LTx patient management, which impacts the quality of nutrition therapy required by these patients.
Keyphrases
- cross sectional
- end stage renal disease
- newly diagnosed
- ejection fraction
- peritoneal dialysis
- prognostic factors
- stem cells
- weight gain
- machine learning
- acute coronary syndrome
- risk assessment
- minimally invasive
- artificial intelligence
- patient reported
- physical activity
- patient reported outcomes
- percutaneous coronary intervention
- weight loss
- deep learning
- heart rate
- atrial fibrillation
- heart rate variability
- big data
- replacement therapy
- case report