Nutritional support in critical care: estimating resting energy expenditure in the intensive care unit when indirect calorimetry is limited

careful selection of the appropriate mode of feeding and monitoring the success of the feeding strategy. The use of specific nutrients, which possess a drug-like effect on the immune or inflammatory state during critical illness, continues to be an exciting area of investigation. The lack of systematic research and clinical trials on various aspects of nutrition support in the PICU is striking and makes it challenging to compile evidence based practice guidelines. There is an urgent need to conduct well-designed, multicenter trials in this area of clinical practice. The extrapolation of data from adult critical care literature is not desirable and many of the interventions proposed in adults will have to undergo systematic examination and careful study in critically ill children prior to their application in this population. In the following sections, we will discuss some of the key aspects of nutrition support therapy in the PICU; examine the literature and provide best practice guidelines based on evidence from PICU patients, where available. While some PICU popu lations include neonates, A.S.P.E.N. Clinical Guidelines for neonates will be published as a separate series.

Nutritional support is a critical component of managing patients in intensive care units (ICUs). Critical illness triggers a hypermetabolic state, leading to significant nutritional demands and muscle wasting. Proper nutritional interventions can positively impact clinical outcomes, reduce the duration of mechanical ventilation, and improve overall recovery. However, delivering adequate nutrition to critically ill patients present several challenges, including the patient's unstable condition, varying metabolic needs, gastrointestinal dysfunction, and difficulties in achieving nutritional goals. Recent advances in understanding the nutritional requirements of ICU patients, the role of early enteral nutrition, and the development of specialized formulas have led to improved patient care. Strategies such as personalized nutrition, immunonutrition, and monitoring tools like indirect calorimetry have become essential components of ICU nutrition management. Additionally, managing critically ill patients with comorbidities, such as sepsis or multi-organ failure, requires tailored approaches to prevent malnutrition and overfeeding.This review highlights the key challenges associated with nutritional support in critical care, current strategies employed to optimize nutrition, and the recent advances in the field. Evidence-based practices with individualized care, nutritional support can enhance patient recovery, reduce ICU stay, and lower morbidity and mortality rates.

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Objective To evaluate the difference and correlation of 24 h energy expenditure in patients with sepsis by indirect calorimetry (IC) and HB coefficient equation. Methods A prospective comparative study including 60 patients with sepsis who was suitable for nutritional support and respiratory indirect calorimetry in the intensive care unit (ICU) from January to October 2015 was conducted. Resting energy expenditure (REE) was measured simultaneously by respiratory indirect calorimetry and HB coefficient (Harris-Benedict formula×stress coefficient) in 60 patients with sepsis at 0 day, 3 day, 7 day, and 14 day after nutritional support, and the differences in dynamic REE were compared between the two methods. The consistency of REE by indirect calorimetry (IC) and HB coefficient equation was evaluated by Bland-Altman. The correlation of IC and HB methods was determined by Pearson analysis. The linear regression equation was determined by linear regression analysis. Results Within 2 weeks after nutritional support, 188 times of measures by IC method and HB method respectively were finished in all patients with sepsis. The dynamic REE in respiratory indirect calorimetry group was significantly higher than that in HB coefficient method group (P<0.05). The average bias of REE between the IC method and the HB method was (1 930.9±597.7)kJ/24 h. For the consistency boundaries was over large and beyond the scope of clinical acceptance, there was a bias between the two methods and they could not directly substituted. There was a linear correlation of REE between the IC method and the HB coefficient (r=0.757, P=0.000). The equation associated with the HB coefficient method is fitted using a one-way regression: Y=1.17X+ 834.11 (kJ/24 h), and X was the 24 h energy expenditure measured by the HB coefficient method. Conclusion The energy metabolism of patients with sepsis is not obvious in the first 2 weeks. The HB coefficient method significantly underestimates the 24 h energy expenditure of patients with sepsis. The prediction equation can be used to correct the HB coefficient method and improve the HB coefficient method to predict the energy expenditure of patients with sepsis. Key words: Sepsis; Energy Consumption; HB coefficient; Respiratory indirect calorimetry; Correction formula

As we write this final chapter, there is a foot of new snow on the ground in R.G.’s backyard in Salt Lake City, and the snow continues to fall. Last night three local television weather forecasters predicted we would only have two inches of snow, and all they had to do was predict one day into the future! With some trepidation, and without the equivalent of weather satellites and 40 years experience with forecasting, the authors will try to predict the future of computers and decision support systems in critical care. Our projections are based on two decades of experience and a generally optimistic outlook. We believe that seven broad areas will determine the pace of the future of computerized decision support in critical care: 1. Human, cultural, and sociological issues relating to how computers will be used in the intensive care unit (ICU). 2. Standardization in medicine and the ability to share medical knowledge will be essential. 3. Expanded medical knowledge will lead to better patient care. 4. Hardware and software will continue to advance at a rapid rate. 5. Data acquisition methods and instrumentation will provide more accurate, timely, and less expensive measurements. 6. Sharing of computer and clinical knowledge in computer form will become common and encouraged by government and the clinical community. 7. Better methods for prognostic decision-making will enable medical practitioners and society to make better ethical decisions about health care.

PP026-MON: Nutritional Support Team Performance is Related to Exclusive Dedication to the Team

CLINICAL NUTRITION WEEK 2015: Long Beach, CA February 14–17, 2015

To investigate the changing laws of rest energy expenditure (REE) in intensive care unit (ICU) patients and the intervention effect for nutritional support. A prospective randomized control trial was conducted. Fifty-eight critically ill patients who were expected to be able to receive sustained enteral and (or) parenteral nutrition for more than 7 days admitted to ICU of the First Affiliated Hospital of Bengbu Medical College from December 2016 to June 2017 were enrolled. The patients were divided into REE group (n = 29) and HBREE group (n = 29) according to the random number table. On the 1st to 7th day after ICU admission, the indirect calorimetry and the Harris-Benedict (HB) formula were used to obtain the REE and HBREE values, and nutritional support was given according to REE and HBREE values respectively. The data of hemoglobin (Hb), albumin (Alb), prealbumin (PA), C-reactive protein (CRP), oxygenation index (OI) on 1st, 3rd, 5th, 7th and discharged day, and insulin dosage, vasopressor time, mechanical ventilation time, the length of ICU stay, and 28-day mortality were collected. (1) At the beginning, the REE level was high, and then decreased gradually with the extension of hospitalization, and the decline was obvious on the 2nd to 3rd day (kJ/d: 7 088.38±559.41, 6 751.34±558.72 vs. 7 553.44±645.55, both P < 0.05), and was stable from the 5th day, the changing laws showed high at first, then the low, the first rapid decline, then the slow decline, and then reached the steady, there was a 2-day plateau in the middle. During the first 2 days, the REE value was significantly higher than the HBREE value (kJ/d: 7 553.44±645.55 vs. 6 759.21±668.14, 7 088.38±559.41 vs. 6 759.21±668.14, both P < 0.01); on the 3rd, 4th day, the REE value was almost the same as the HBREE value (kJ/d: 6 751.34±558.72 vs. 6 759.21±668.14, 6 568.03±760.19 vs. 6 759.21±668.14, both P > 0.05). After that, the REE value was significantly lower than the HBREE value (kJ/d: 6 089.55±560.70 vs. 6 759.21±668.14, 5 992.55±501.82 vs. 6 759.21±668.14, 5 860.84±577.59 vs. 6 759.21±668.14, all P < 0.01). (2) After the initiation of nutritional support, Hb in the REE group (the first 3 days) and HBREE group (the first 7 days) all increased slowly in the early stage. It increased obviously on the 5th day in the REE group. Compared with the REE group, Hb increased more slowly in the HBREE group, however, there was no difference between the two groups at the time of discharge (g/L: 113.75±17.28 vs. 110.86±15.35, P > 0.05). PA and OI all enhanced significantly on the 3rd day since the nutritional support was initiated, but the daily increase of the REE group was significantly higher than that of the HBREE group [3rd day, PA (mg/L): 110.38±27.65 vs. 96.28±18.06, OI (mmHg, 1 mmHg = 0.133 kPa): 259.29±49.36 vs. 231.74±28.02, both P < 0.05]. The Alb and CRP in the REE group began to improve on the 3rd day, while the index in the HBREE group was delayed on the 5th day, overall, at the time of discharge, the PA, CRP and OI were lower in the HBREE group than in the REE group [PA (mg/L): 252.28±56.94 vs. 295.86±57.26, CRP (mg/L): 73.14±17.63 vs. 56.52±14.91, OI (mmHg): 353.59±70.36 vs. 417.52±71.58, all P < 0.01]. (3) The vasopressor was used in both groups for less than 3 days, but the REE group was shorter (days: 2.26±0.82 vs. 2.95±1.22, P < 0.05), the insulin dosage in the HBREE group was much more than that in the REE group (U: 101.97±21.05 vs. 84.59±22.21, P < 0.01); compared with the REE group, the time of mechanical ventilation and the length of ICU stay in the HBREE group were longer (hours: 113.07±25.96 vs. 93.41±27.25, days: 10.41±3.11 vs. 8.45±2.44, both P < 0.01). There was no significant difference in the 28-day mortality between the REE group and HBREE group (17.24% vs. 24.14%, P > 0.05). Indirect calorimetry can more accurately grasp the changing laws of REE in critically ill patients. Nutritional support with REE value can make relevant nutritional indicators as good as possible, and reduce insulin dosage, shorten vasopressor use time, the length of ICU stay and mechanical ventilation time, but does not change the 28-day mortality.

ObjectivesHyperthermic intraperitoneal chemotherapy (HIPEC) is increasingly used for patients with stage III ovarian cancer undergoing interval cytoreductive surgery (CRS). It is uncertain whether routine postoperative admittance to an intensive care...

Introduction/BackgroundHyperthermic intraperitoneal chemotherapy (HIPEC) is increasingly used for patients with stage III ovarian cancer undergoing interval cytoreductive surgery (CRS). It is uncertain whether routine postoperative admittance to an intensive care...

An ever-thorny issue: Defining key elements of critical care nursing and its relation to staffing.

Critically ill patients in the Intensive Care Unit (ICU) should receive nutritional support matched to their metabolic needs as both under- and overfeeding energy has been shown to increase mortality. Critical illness can significantly affect metabolism. Consequently, resting energy expenditure (REE) can vary markedly during critical illness. Therefore, indirect calorimetry to estimate REE is recommended to determine energy requirements in individual ICU patients and to guide optimal nutritional support. Currently, the Quark metabolic monitor is considered the gold standard in our ICU, but novel mechanical support devices are also equipped with indirect calorimetry functionalities. This study aimed to evaluate the performance of a currently unevaluated device. A cross-sectional analysis in mechanically ventilated patients was conducted in a mixed medical-surgical ICU. The primary outcome was a numerical and visual comparison of the performance of the Beacon indirect calorimeter to calculate REE compared to the Quark device using Bland Altman plots. Performance was evaluated using bias, precision, accuracy, and reliability. Secondary analysis included a comparison with REE estimated by predictive equations. Seventy-one measurements were obtained in 27 mechanically ventilated subjects. An underestimation by the Beacon device in calculated REE of-96.2kcal/day (4.5%) was found. There was a bias towards higher VCO2 and lower VO2 values with Beacon as compared to Quark. The reliability of the Beacon was good, with an absolute intraclass correlation coefficient of 0.897 (95%CI 0.751-0.955; p=0.000). There was a poor correlation (<0.40) between the separate indirect calorimetry devices and most predictive equations. Only the Faisy predictive equations had good reliability (ICC 0.687, p=0.002). Beacon indirect calorimetry accurately determined REE in mechanically ventilated critically ill patients compared to the gold standard in our ICU (Quark indirect calorimeter), although confidence intervals were wide. There was low bias and good reliability. On the other hand, predictive equations performed poorly compared to both devices, underestimating the true metabolic needs of mechanically ventilated ICU patients.

Number of recently published studies on nutritional support in the intensive care unit (ICU) have resulted in a paradigm shift of clinical practices. This review summarises the latest evidence in four main topics in the ICU, namely: (1) function of validated nutrition screening/assessment tools, (2) types and validity of body composition measurements, (3) optimal energy and protein goals, and (4) delivery methods. Recent studies that investigated the above aims were outlined and discussed. In addition, recent guidelines were also compared to highlight the similarities and differences in their approach to the nutrition support of critically ill patients. Regardless of nutritional status and body composition, all patients with >48 hours of ICU stay are at nutrition risk and should receive individualised nutrition support. Although a recent trial did not demonstrate an advantage of indirect calorimetry over predictive equations, it was recommended that indirect calorimetry be used to set energy targets with better accuracy. Initiation of enteral nutrition (EN) within 24-48 hours was shown to be associated with improved clinical outcomes. The energy and protein goals should be achieved gradually over the first week of ICU stay. This practice should be protocolised and regularly audited as critically ill patients receive only part of their energy and protein goals. Metabolic demands of critically ill patients can be variable and nutrition support should be tailored to each patient. Given that many nutrition studies are on-going, we anticipate improvements in the individualisation of nutrition support in the near future.