Quantification of lean and fat tissue repletion following critical illness: a case report.

INTRODUCTION: Muscle wasting is a recognised feature of critical illness and has obvious implications for patient rehabilitation and recovery. Whilst many clinicians believe lean tissue repletion to be a slow process following critical illness, and a probable explanation for poor functional recovery of patients many months after resolution of the illness, we have found no studies quantifying body composition changes during patient recovery. METHODS: A combination of assessment techniques were used to monitor changes in body composition (that is, fat, water, protein and mineral), following intensive care unit (ICU) discharge, in a 38-year-old female recovering from extrapontine myelinolysis. Assessments were made at discharge from the ICU and then again 1 month, 3 months, 6 months and 12 months later. Functional recovery (respiratory muscle and hand-grip strength) and quality of life (36-item Short-form Health Survey) were assessed at these same timepoints. RESULTS: Twelve months after discharge from the ICU, and despite an extensive rehabilitation programme and improvements in respiratory muscle and hand-grip muscle strength, our patient was unable to return to full-time employment and continued to complain of fatigue. She had successfully regained weight and was back to her pre-illness body weight. Body composition measurements showed that an incredible 73% of the weight gained was due to an increase in body fat. CONCLUSION: It is difficult to extrapolate the results of a single case to the wider ICU population, not least because the present patient sustained a significant neurological injury, but our data are the first to support the long-held belief that patient weight gain following critical illness is largely attributable to a gain in fat mass. The magnitude of body composition changes in the present patient are startling and support the need for longitudinal body composition data in a wider ICU population.

Poor agreement between continuous measurements of energy expenditure and routinely used prediction equations in intensive care unit patients.

BACKGROUND & AIMS: A wide variation in 24h energy expenditure has been demonstrated previously in intensive care unit (ICU) patients. The accuracy of equations used to predict energy expenditure in critically ill patients is frequently compared with single or short-duration indirect calorimetry measurements, which may not represent the total energy expenditure (TEE) of these patients. To take into account this variability in energy expenditure, estimates have been compared with continuous indirect calorimetry measurements. METHODS: Continuous (24h/day for 5 days) indirect calorimetry measurements were made in patients requiring mechanical ventilation for 5 days. The Harris-Benedict, Schofield and Ireton-Jones equations and the American College of Chest Physicians recommendation of 25 kcal/kg/day were used to estimate energy requirements. RESULTS: A total of 192 days of measurements, in 27 patients, were available for comparison with the different equations. Agreement between the equations and measured values was poor. The Harris-Benedict, Schofield and ACCP equations provided more estimates (66%, 66% and 65%, respectively) within 80% and 110% of TEE values. However, each of these equations would have resulted in clinically significant underfeeding (<80% of TEE) in 16%, 15% and 22% of patients, respectively, and overfeeding (>110% of TEE) in 18%, 19% and 13% of patients, respectively. CONCLUSIONS: Limits of agreement between the different equations and TEE values were unacceptably wide. Prediction equations may result in significant under or overfeeding in the clinical setting.

Nutritional requirements of surgical and critically-ill patients: do we really know what they need?

Malnutrition remains a problem in surgical and critically-ill patients. In surgical patients the incidence of malnutrition ranges from 9 to 44%. Despite this variability there is a consensus that malnutrition worsens during hospital stay. In the intensive care unit (ICU), 43% of the patients are malnourished. Although poor nutrition during hospitalisation may be attributable to many factors, not least inadequacies in hospital catering services, there must also be the question of whether those patients who receive nutritional support are being fed appropriately. Indirect calorimetry is the 'gold standard' for determining an individual's energy requirements, but limited time and financial resources preclude the use of this method in everyday clinical practice. Studies in surgical and ICU patient populations have been reviewed to determine the 'optimal' energy and protein requirements of these patients. There are only a small number of studies that have attempted to measure energy requirements in the various surgical patient groups. Uncomplicated surgery has been associated with energy requirements of 1.0-1.15 x BMR whilst complicated surgery requires 1.25-1.4 x BMR in order to meet the patient's needs. Identifying the optimal requirements of ICU patients is far more difficult because of the heterogeneous nature of this population. In general, 5.6 kJ (25 kcal)/kg per d is an acceptable and achievable target intake, but patients with sepsis or trauma may require almost twice as much energy during the acute phase of their illness. The implications of failing to meet and exceeding the requirements of critically-ill patients are also reviewed.

Muscle wasting and energy balance in critical illness.

BACKGROUND: In nine patients with multiple organ failure ultrasound was able to identify muscle wasting despite the presence of oedema (Campbell et al., J Clin Nutr 62 (1995) 533). AIMS: The purpose of the present study was twofold: one was to determine whether this technique was applicable to a much larger ICU population, many of whom were not as ill as the original subjects. The second reason was to determine whether a relationship could be identified between rates of wasting and energy balance. METHODS: Serial measurements of both mid-upper arm circumference (MAC) and muscle thickness, using ultrasound, were made at 1-3 day intervals between 5 and 39 (median 7) days in 50 critically ill patients. RESULTS: Muscle thickness decreased in 48 of the 50 patients at a median rate of 1.6%/day with a range of 0.2-5.7%/day. In 33 patients, in whom MAC did not change significantly with time, muscle thickness decreased by between 0.3 and 4.2 (median 1.6)%/day. In three patients MAC increased significantly with time but muscle thickness decreased by between 1.3 and 5.7 (median 2.6)%/day. Twelve patients showed a significant decrease in MAC with time and muscle thickness in this group decreased by between 0.2 and 4.0 (median 1.3)%/day. The percentage decrease in muscle thickness between the groups, in whom MAC decreased or did not change, was not significantly different (P = 0.475). CONCLUSION: We have demonstrated that an ultrasound technique devised to identify muscle wasting in the presence of severe fluid retention works in the majority (48/50) of patients when applied to a wider ICU population. Energy balance made no difference to the rate of wasting.

Genetic variation in proinflammatory and anti-inflammatory cytokine production in multiple organ dysfunction syndrome.

OBJECTIVES: The objectives of this study were to examine the prevalence of genetic variation for cytokine production (tumor necrosis factor [TNF]-alpha, interleukin-10, transforming growth factor-beta1) in patients with multiple organ dysfunction syndrome, to measure circulating cytokine levels and relate these to genotype, and to identify the relationship between genetic variation and outcome. DESIGN: Prospective analysis. SETTING: Intensive care unit of a university teaching hospital. PATIENTS: Eighty-eight critically ill patients with multiple organ dysfunction syndrome. MEASUREMENTS AND MAIN RESULTS: The frequency of the different interleukin-10 genotypes (corresponding to high, intermediate, and low interleukin-10 production ) were significantly different between controls and multiple organ dysfunction syndrome patients. High interleukin-10 producers were under-represented in the multiple organ dysfunction syndrome group: This genotype occurred in 30% of controls but in only 6% of patients ( <.001). There was no relationship between interleukin-10 genotype and mortality. The frequency of TNF-alpha genotypes was also significantly different between patients and controls. Intermediate TNF-alpha producers were under-represented (5.7% vs. 23%) and high TNF-alpha producers over-represented (35.2% vs. 16%) in the patient group (p <.001). TNF-alpha genotype was not related to mortality. The distribution of TNF-beta genotypes (homozygous B1, homozygous B2, and heterozygotes) was also different between controls and patients (p =.008). The B2/B2 genotype (associated with high TNF-alpha production) tended to occur less frequently in the intensive care unit population (31% vs. 50%) and was associated with a higher mortality rate than either the B1/B1 or B1/B2 genotypes (48% vs. 11% and 33% respectively, p=.115). The combination of proinflammatory (TNF-alpha/TNF-beta) and anti-inflammatory (interleukin-10/transforming growth factor-beta1) cytokine genotypes was associated with prolonged patient survival time. Patients predisposed to produce a balanced cytokine response (e.g., intermediate interleukin-10/TNF-alpha producers) demonstrated the longest survival times, although overall mortality was no different. CONCLUSION: A genetic predisposition to high interleukin-10 production or intermediate TNF-alpha production may be protective of admission to the intensive care unit, although once admitted, any protection provided by these genotypes seems to be lost. TNF-beta genotype conferred no advantage to patients with multiple organ dysfunction syndrome, the TNFB2 allele being associated with increased mortality. The combination of proinflammatory and anti-inflammatory cytokine genotypes supports the idea that a balanced cytokine response is favorable and was associated with prolonged patient survival time.