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Keywords:

  • aspirate volume;
  • energy deficit;
  • intensive care unit;
  • nutrition support;
  • sedation;
  • ventilation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

Background:  Critically ill patients frequently receive inadequate nutrition support as a result of under- or overfeeding. Malnutrition in intensive care unit (ICU) patients is associated with increased morbidity and mortality. The present study aimed to identify the significant factors that influence energy deficit in the ICU.

Methods:  ICU patients with a length of stay of ≥3 days were studied for 30 days over two consecutive years at a large university teaching hospital. Fifty-six Patients were studied, with a total of 530 records of feeding days. Information was collected for: day when feed initiated, age, gender, length of stay, Acute Physiological and Chronic Health Evaluation score (APACHE II), fed within 24 h, speciality, type of ventilation, feeding route, outcome (survived/died), diarrhoea (yes/no), aspirate volume, dietitian observed nutritional status (malnourished/not), sedation, estimated energy requirements and energy received. Mixed linear models for longitudinal data were used with energy deficit (energy received – energy requirements) as the dependent variable.

Results:  Factors that were found to have a significant association with energy deficit were: day feeding was initiated (P < 0.001), whether fed within 24 h (P < 0.001) and whether sedated (P < 0.001). Furthermore, three combined effects were found: ventilation mode and aspirate volume (P < 0.007), fed within 24 h and ventilation mode (P < 0.001), fed within 24 h and sedation (P < 0.017).

Conclusions:  The number of days after feeding was initiated, initiation of feeding within 24 h and sedation have been identified as factors that predict energy deficit during ICU stay. Efforts to initiate feeding as soon as possible and minimise interruptions to feeding may reduce energy deficits in these vulnerable patients.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

Critically ill patients are at high risk of receiving inadequate nutritional support (McClave et al., 1999) and this has led to a renewed interest in the factors that inhibit adequate nutritional support in this vulnerable patient population.

Malnutrition is a common problem in hospitalised patients, with as many as 40% of adult patients being seriously malnourished at the time of their hospital admission (Barr et al., 2004). Two-thirds of all patients experience deterioration of their nutritional status during their hospital stay (McWhirter & Pennington, 1994). Acute illness further exacerbates a patient’s poor nutritional status by increasing their metabolic rate and by impairing their utilisation of nutritional substrates (Cerra et al., 1997; Stroud, 2007).

Nutritional support plays a vital role in the prevention and treatment of nutritional deficiencies in critically ill patients (Zaloga, 1994). Studies have shown that initiating nutritional support in intensive care unit (ICU) patients within 48 h of ICU admission is associated with improved clinical outcomes, lower infection rates and a reduced length of hospital stay (Doig et al., 2008, 2009, 2011; Heyland et al., 2003; Roberts & Zaloga, 2000; Zaloga, 1994).

However, critically ill patients frequently receive inadequate nutritional support during their ICU stay because of underestimation of their nutritional needs, which is further compounded by a delayed initiation of nutritional support (Dempsey et al., 1988; Heyland et al., 1995). Feeding is often stopped or delayed as result of large gastric residual volumes (> 200 mL), premature cessation of feeding with gastric residual volumes (GRV) below 200 mL, feeding tube displacement, prolonged fasting for procedures (such as bronchoscopy, tracheostomy, endoscopy, surgical intervention), airway management and routine nursing care (McClave et al., 1999; Montejo, 1999; Reid, 2006). Gastrointestinal complications clearly impede adequate delivery of enteral feeding. These include nausea, vomiting, abdominal distention, prolonged ileus (sedation or surgery related), retro-peritoneal haematoma, sepsis and delayed gastric emptying after head injury (Heyland et al., 2003; McClave et al., 1999; Montejo, 1999; Reid, 2006).

Malnutrition in ICU patients is associated with increased morbidity, mortality (Alberda et al., 2009; Artinian et al., 2006) and length of ICU stay as a result of increased ventilator dependency, higher rates of infection and impaired wound healing Klein et al. (1998). There are numerous studies demonstrating the inadequacy of feeding in critically ill patients. Average energy intakes between 49% and 79% of requirements have been reported (Binnekade et al., 2005; De Johghe et al., 2001; Heyland et al., 2003; Huang et al., 2000; Krishnan et al., 2003; McClave et al., 1998; Reid, 2006; Rubinson et al., 2004). A recent multicentre study showed that ICU patients were uniformly underfed on calories and protein, on average 4326 kJ (1034 kcal) and 47 g, respectively (Alberda et al., 2009). A further recent international multicentre study reported average nutritional adequacy as 59% for energy and 60.3% for protein requirements (Cahill et al., 2010).

This inadequate nutrition is partly a result of difficulties in determining optimal nutritional requirements; in this diverse patient group, it remains a complex feat (Reid, 2006; Stroud, 2007). It is acknowledged that attention should not only be focused on energy and nitrogen, but also on micronutrient provision. In addition to this, many studies have failed to demonstrate improved clinical outcomes in patients that have been considered to be optimally fed. There is evidence that underfeeding and overfeeding is associated with adverse consequences, including an increased risk of infection, metabolic complications and prolonged weaning from mechanical ventilation (Huang et al., 2000; McClave et al., 1998; Rubinson et al., 2004). Not only is adequate nutrition support therefore difficult to define, but also it may prove challenging to attain. The present study aimed to identify the significant factors that influence energy deficit in the adult ICU.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

Study design and setting

This was a prospective observational study conducted in adult ICUs at a large university teaching hospital in the UK. Two audits were conducted where sequential patients in March/April 2005 and 2006 were audited and data were collected for 30 days. The study was registered with the hospital Clinical Audit Office and no ethical approval was required because only routine clinical data were collected to evaluate nutritional intake.

Subjects

The inclusion criteria were patients admitted to ICU with a stay of ≥3 days. Patients that were enterally and parenterally fed were included in the audit. Those patients on an oral diet were excluded from the study.

Data collection

Information was collected from patient observation charts, medical notes and through liaising with medical, nursing and dietetic staff. Energy intake was calculated from artificial feeding records and product information supplied by the manufacturers (Abbott Laboratories, Maidenhead, UK; B. Braun Medical Ltd, Sheffield UK; and Fresenius Kabi Ltd, Runcorn, UK). Where patients failed to meet their target requirements, the reasons for failing to do so were recorded. Actual practices were then compared with the guidelines established in the ICU’s feeding protocol and nutritional support policy. This policy has been based on the Canadian Clinical Practice Guidelines for Nutrition Support in Mechanically Ventilated, Critically Ill Adult Patients (Heyland et al., 2003). For each patient, data were collected from day of admission until discharge from the ICU, death or study end (patients remaining in ICU after the 30-day audit period).

Patients had a nutrition status assessment completed within 48 h of admission to the ICU and recorded (malnourished/not). The assessment was based on dietetic observer opinion because often weight and height information is difficult to obtain from ICU patients. The dietitian observed for signs of malnutrition (temporal muscle wasting, square shoulders, prominent clavicles) and enquired from relatives about weight loss history, along with reduced oral intake to determine whether the patient met the malnourished category. Estimated energy requirements, estimated protein requirements and the feeding regimen were calculated by the ICU dietitian on admission and when the clinical condition changed significantly. This was carried out using the Schofield equation (Schofield, 1985) and Parenteral and Enteral Nutrition Group of the British Dietetic Association guidelines (Smith & Durman, 2004). Because fluid retention and oedema frequently occur in critically ill patients, preillness or pre-ICU admission weights were used in predictive equations. In the absence of a documented weight, information was sought from patient relatives. Where no such information was available, patient weight was estimated by the ICU dietitian.

The information collected for each patient comprised: age, gender, date of admission, diagnostic category, APACHE II score, date of discharge from ICU, length of stay on ICU and ICU outcome (returned to ward or death).

Nutritional data were also collected for nutritional status (malnourished/not), energy and protein requirements, prescribed feeding regimen (target volume and rate), feeding route, whether the patient was fed within 24 h of ICU admission, what day the feed was initiated and the target rate of feed achieved.

Information that was recorded daily included: feeding route (if this had changed), type and rate of feed, volume of feed, total calorie and protein intake from feed, ventilatory support [endotracheal tube (ETT), tracheostomy, non-invasive (NIV), self], GRV, blood glucose range, drugs used for sedation or prokinetic use, information on frequency of stool output and the presence of diarrhoea, along with the reasons behind patients not receiving the prescribed volume of feed, if applicable.

Statistical analysis

Statistical analysis was carried out by an independent statistician to assess those factors that best predict energy deficit. The method applied was linear mixed models for longitudinal data with energy deficit (energy received – energy requirements) as the dependent variable. The present study required the analysis of longitudinal data because the outcome of interest was measured repeatedly over time. Longitudinal data require special statistical methods because observations made on the same patient are correlated. Linear mixed models takes this into account aiming to draw valid statistical inferences. The modelling and estimation of the effects of interest was carried out using spss, version 15 (SPSS, Inc., Chicago, IL, US).

The interactions tested were those involving factors or factors and continuous variables. All possible combinations were considered and those excluded were the ones that either: (i) did not satisfy sufficient observations to test the interaction term through cross tabs between factors or (ii) were nonsignificant. An exploratory analysis that included graphs also helped to indicate possible interactions.

Because the number of measurements was unbalanced, the approach used was random coefficients, and not the covariance pattern models approach. The former also allows for correlation within patients. The residuals were normally distributed.

The effects of the following variables were analysed: day that feed was started on, age, length of stay, APACHE II score, fed within 24 h (yes/no), sex, diagnostic category, type of ventilation, feeding route, outcome (survived/died), diarrhoea (>3 episodes of loose stools per 24 h), gastric residual volume, dietitian observed nutritional status (malnourished/not), sedation, estimated energy requirements and energy received.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

Fifty-six patients required an ICU stay for ≥ 3 days and were included in the present study. A total of 530 possible feeding days were assessed. The median age of patients was 68 years and the median length of stay on the unit was 15 days. The majority of patients were male (71%). Detailed patient characteristics are shown in Table 1.

Table 1.   Patient characteristics (= 56)
Characteristic 
  1. *Negative energy balance refers to energy deficit. APACHE, Acute Physiological and Chronic Health Evaluation score.

Age (years), median (range)68 (19–83)
Length of stay (days), median (range)15 (3–53)
Mortality in intensive care unit13% (7/56)
Sex71%♂ (40/56) 29%♀ (16/56)
Nutritional status
 Thin/wasted27% (15/56)
 Normal weight45% (25/56)
 Overweight28% (16/56)
Diagnostic category
 Medicine41% (23/56)
 Surgery36% (20/56)
 Neuroscience23% (13/56)
APACHE II score
 Median (range)21 (8–38)
Ventilation (% of days)
 Endotracheal tube47% (250/530)
 Tracheostomy38% (201/530)
 Non-invasive ventilation6% (32/530)
 Self-ventilating9% (47/530)
Sedation (% of days)
 Yes28% (148/530)
 No72% (382/530)
Feeding route (% of days)
 Enteral81% (430/530)
 Total parenteral nutrition14% (74/530)
 Nasogastric and total parenteral nutrition5% (26/530)
Diarrhoea (% of days)
 Yes17% (87/530)
 No83% (442/530)
Fed within 24 h (= 56)
 Yes75% (42/56)
 No25% (14/56)
Aspirate volume (% of days)
 < 50 mL42% (221/530)
 50–100 mL5% (26/530)
 100–200 mL2% (10/530)
 > 200 mL2% (13/530)
 None taken49% (260/530)
Energy balance (% of days)
 Positive33% (176/530)
 Even3% (18/530)
 Negative*64% (336/530)

The adequacy of nutritional support was examined in relation to the dietetic prescribed regimen (Table 2). This clearly demonstrates a wide range in under and over feeding. Energy balance was seen to be as varied as 9439 kJ (−2256 kcal) to 4138 kJ (+989 kcal).

Table 2.   Median energy and protein intake compared with median target requirements (n = 530)
MedianTarget requirement (range)Intake (range)Intake as % of target requirement (range)Energy balance (range)
  1. *Percentages < 100% represent overfeeding.

Energy (MJ/day)7.37 (5.6–10)6.7 (0–11.6)97 (0–162)*−1.45 (−9.4 to +4.13)
Energy (kcal/day)1762 (1327–2406)1612 (0–2783)97 (0–162)*−358 (−2256 to +989)
Protein (g/day)75 (53–102)60 (0–117)84 (0–163)*

The mixed linear model analysis showed that the factors significantly affecting energy deficit were: day feeding was initiated (P < 0.001), whether fed within 24 h (P < 0.001) and whether sedated (P < 0.001). Furthermore, three combined effects were found (Table 3). The first interaction was between ventilation mode and aspirate volume (P < 0.007): the effect of ventilation on energy deficit depended on aspirate volume. Patients with ETT and aspirate volume > 200 mL had much higher energy deficits compared to those patients with other types of ventilation or lower aspirate volumes (Table 3). The second interaction was between fed within 24 h and ventilation mode (P < 0.001); Patients not fed within the first 24 h and ventilated with ETT or NIV had much higher energy deficits compared to other forms of ventilation (Table 3). Lastly, an interaction was found between fed within 24 h and sedation (P < 0.017); patients who were fed within 24 h had lower energy deficits and sedation had little effect on this. However, in the group who were not fed within 24 h, sedated patients had much greater deficits than nonsedated patients (Table 3).

Table 3.   Significant variables and interactions associated with energy deficit in kJ (kcal)
VariablesEstimated95% Confidence Interval
Effects kJ (kcal)Lower BoundUpper Bound
  1. *P < 0.0001.

  2. **P < 0.007.

  3. ***P < 0.001.

  4. ****P < 0.017.

  5. ETT, endotracheal tube; NIV, non-invasive.

Day of feed (Day of feed)2*316.7 (75.7)210 (50.2)123.4 (101.2) 
−10.5 (−2.5) −14.6 (−3.5) −6.3 (−1.5) 
Age<40913.4 (−218.3) −2068.9 (−494.5) 24207 (58.0)
40–60 −3473.9 (−830.3) −4374.3 (−1045.5) −2573 (−615.0)
> 60−2350.9 (−561.9) −3106.2 (−742.4) −1596.2 (−381.5)
Aspirate Volume by form of Ventilation support**
Aspirate VolumeVentilation   
<50ETT−2213.3 (−529.0) −3209.1 (−767.0) −1217.9 (−291.I)
NIV−1906.6 (−455.7) −2763 (−660.4) −1049.7 (−250.9)
Trachy−1533 (−366.4) −2404.4 (−574.2) −664 (−158.7)
Self ventilating−1707 (−408.0) −2776.9 (−663.7) −636.8 (−152.2)
50–100ETT−3373.1 (−806.2)−6597.3 (−1576.8)−149.4 (−35.7)
NIV−1718.4 (−410.7) −3032.5 (−724.8) −404.6 (−96.7)
Trachy−2468.1 (−589.9) −4171.9 (−997.1) −764.4 (−182.7)
Self−3974.8 (−950.0) −7091 (−1694.8) −858.9 (−205.3)
ventilating   
100–200 ETT−2480.7 (−592.9) −6836.6 (−1634.3) 1876.9 (448.6)
N IV−2601.1 (−621.7) −4776.5 (−1141.6) −426.3 (−101.9)
Trachy−1426.3 (−340.9) −3994.8 (−954.8) 1142.7 (273.1)
Self−529.3 (−126.5) −3656.4 (−873.9) 2597.8 (620.9)
ventilating   
> 200ETT−5564.3 (−1329.9) −7588.5 (−181 3.7) −3540.1 (−846.1)
N IV−2956.4 (−706.6) −5123.3 (−1224.5) −789.1 (−188.6)
Trachy−34.7 (−8.3) −4297.4 (−1027.1) 4228.4 (101 0.6)
Self−735.9 (−175.9) −5089.4 (−1216.4) 3617.5 (864.6)
ventilating   
No aspirate takenETT−3265.6 (−780.5) −4102.4 (−980.5) −2428.8 (−580.5)
N IV−2158.5 (−515.9) −3198.7 (−764.5) −1118.8 (−267.4)
Trachy−673.6 (−161.0) −1482.4 (−354.3) 135.6 (32.4)
Self−3599.9 (−860.4) −4817.9 (−1151.5) −2382.4 (−569.4)
ventilating   
Patients fed within 24h by form of ventilatory support***
Fed within 24hVentilation   
YesETT−1634.6 (−390.7) −3320.8 (−793.7) 51.9 (12.4)
NIV−466.1 (−111.4) −1423.8 (−340.3) 491.6 (117.5)
Trachy−1082.8 (−258.8) −2423.8 (−579.3) 258.2 (61.7)
Self−1668.2 (−398.7) −3032.6 (−724.8) −303.8 (−72.6)
ventilating   
NoETT−5124.6 (−1224.8) −6447.1 (−1540.9) −3801.6 (−908.6)
N IV−4070.6 (−972.9) −5536.3 (−1323.2) −2604.5 (−622.5)
Trachy−1371.5 (−327.8) −2805.4 (−670.5) 62.3 (14.9)
Self−2550.9 (−609.7) −4675.6 (−1117.5) −425.9 (−101.8)
ventilating   
Patients fed within 24h by drug sedation****
Fed within 24hDrug sedation   
YesYes−1383.2 (−330.6) −2295.8 (−548.7) −470.7 (−112.5)
No−1042.7 (−249.2) −1857.7 (−444.0) −227.2 (−54.3)
NoYes−4333.8 (−1035.8)−5491.5 (−1312.5) −3176.5 (−759.2)
No−2224.6 (−531.7)−3244.3 (−775.4)−1204.9 (−288.0)

Energy deficit was greatest during the first week, although this reduced and became more stable thereafter. Figure 1 shows the observed and predicted energy deficit versus day of feed. We can see that the model fits the data quite well. Additionally, the relationship between energy deficit and day of feed is nonlinear. A polynomial of degree two appeared to be more appropriate than a linear trend. Both terms were very significant (Table 3).

image

Figure 1.  Plot of energy deficit and predicted energy values obtained from the statistical model.

Download figure to PowerPoint

Baseline APACHE II score, speciality, feeding route, the presence of diarrhoea and nutritional status were not included in the final model to best predict energy deficit.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

The present study demonstrates that the three factors that independently affect energy deficit in ICU patients were whether or not the patients were fed within 24 h, the day feeding was initiated, and whether the patients were sedated. The time on ICU before commencing feeding and whether they are fed in the first 24 h will obviously affect overall energy deficit and, clearly, these initial deficits are not regained during ICU stay. Sedation can affect gastric motility, which could lead to the observed effect on energy deficit. This is an important finding for ICU clinicians to note because these patients appear to be at greater risk of underfeeding. These findings confirm those of previous studies (Heyland et al., 2003; Reid, 2006).

Sedative agents are known to cause gastric dysmotility in the ICU setting (Herbert & Holzer, 2008), which could exacerbate energy deficits. Studies have shown significant delays in the gastric emptying of patients receiving morphine and midazolam as sedation (Nguyen et al., 2008), and delays in gastric emptying have also been found when neuromuscular blockade has been used (Tamion et al., 2003). Combinations of fentanyl, midazolam, propofol and atracurium had been used for analgesia, sedation and neuromuscular blockade in the present study.

In addition, the present study also showed that aspirate volume, type of ventilation, whether fed within 24 h and sedation were involved in combined effects. These results suggest that, of those patients who were not able to be fed in the first 24 h, patients with ETT or those who were sedated were most likely to have an energy deficit. Also, ETT patients had the highest energy deficits and this was linked to larger GRV (>200 mL) than in patients ventilated by other methods. A plausible explanation for these findings is that sedated and ventilated patients are the sickest patients and their nutrition support is frequently interrupted by procedures, surgery and poor feed tolerance.

Certain interruptions to feeding are unavoidable as a result of the nature of critical illness (such as hypotension, shock and severe ileus). However, the majority of feed interruptions on ICU have been attributed to avoidable causes (e.g. prolonged fasting for procedures and premature cessation of feeding with GRV <200 mL) (McClave et al., 1999; Montejo, 1999).

Clearly avoidable interruptions should be minimised and feeding protocols should support this. McClave et al. (1999) and Montejo (1999) both recommend the use of small bowel feeding and early parenteral nutrition. This has been supported by (Heyland et al., 2003; Martindale et al., 2009). It is recommended that future studies should explore small bowel feeding techniques, the use of prokinetic agents and the appropriate use of sedation (Herbert & Holzer, 2008; Martindale et al., 2009; McClave et al., 1999; Montejo, 1999). Disparity remains between expert groups on what action to take once an energy deficit has been recognised. The American Society of Parenteral and Enteral Nutrition hesitate to recommend the use of parenteral nutrition where adequate enteral feeding has not been established in non-malnourished ICU patients. Conversely, the European Society of Clinical Nutrition and Metabolism recommend the use of early parenteral nutrition (24–48 h after enteral nutrition has failed) to compensate for deficits (Singer et al., 2010).

There is neither consensus, nor data to support a specific cut-off value for GRV. In the UK, 200–500 mL is widely used, where researchers in Canada and the USA vary their recommendations between 250-mL GRV alongside the use of prokinetics (Heyland et al., 2003) and 500-mL GRV with consideration of other signs of gastric intolerance (Martindale et al., 2009). A 200-mL GRV cut-off was demonstrated to result in the underfeeding of ICU patients (Reid, 2006).

It is clear from the literature that no difference in aspiration risk is found between 200- and 500-mL GRV (McClave et al., 2005). A limit of 500 mL is furthermore not associated with adverse gastrointestinal complications, nor in increased ventilator associated pneumonia, as found in a recent multicentre randomised study (Montejo et al., 2010).

The results of the present study therefore suggest that the patients most vulnerable to energy deficits are those who are sedated or ventilated, and those who are not fed within 24 h for whatever reason, and so the recommendations from these previous studies are most significant for these patients.

Interestingly, the final model that best predicts energy deficit does not include baseline APACHE II score, speciality, feeding route, the presence of diarrhoea and nutritional status. Given all these existing variables in the model, adding other covariates would not add more information that could explain the variability in the response. These are factors that are generally assumed to directly influence feeding, although the present study suggests otherwise. The focus in clinical practice should be on prompt feeding of sedated and ventilated patients, including the exploration of alternative routes, use of prokinetics and minimising sedation if possible. Given the frequent interruptions described above, some of which are unavoidable, the duration of these interruptions needs to be minimised.

Research clearly shows that negative energy balance, as found in the present study, is detrimental and has been strongly associated with increased infectious complications on ICU (Villet et al., 2005) and may prolong ICU stay (Dvir et al., 2006). Previous study findings concur that nutrition support should be optmised to prevent the onset of early energy deficits (Thibault & Pichard, 2010). Alberda et al. (2009) furthermore reported improvements in clinical outcomes in lean ICU patients by increasing energy and the protein given (Alberda et al., 2009).

Overfeeding was also detected in the present study (Fig. 1) and evidence suggests this may be equally harmful to the ICU patient with metabolic complications and may prolong weaning from mechanical ventilation (Huang et al., 2000; McClave et al., 1998; Rubinson et al., 2004). Overfeeding mostly occurred when patients were being weaned from one feeding modality to another (i.e. parenteral to enteral feeding). This issue has been raised in a previous study (Reid, 2006).

A possible limitation of the present study was that feed lost via vomiting and discarded GRV were not taken into account when received feed was calculated for each patient. Nutritional intakes therefore reflected the amount delivered and not the retained nutrition. Nevertheless, these volumes are likely to be minimal and this would not affect this model significantly. A further possible limitation was that vasopressor treatment was not considered. High-dose vasopressors may decrease gastric motility and therefore energy balance. Also, the mortality rate in the present study group may appear to be low; however, the ICU mortality rate on this unit has been below the national average for a number of years. These limitations are balanced by a number of particular strengths; the present study included a large number of feeding days and data were collected over two consecutive years from two large ICUs. A variety of patients with diverse medical and surgical diagnosis are represented. This research also reflects usual dietetic practice within the UK.

It has been well documented that critically ill patients receive inadequate nutrition support. Nutritional status and functional recovery after a period of critical illness may be compromised by energy deficits. Day when feeding was initiated, whether fed within 24 h and sedation have been identified as independent factors that predict energy deficit during ICU stay. Clinicians should aim to feed patients early, minimise, as much as possible, the sedation given to patients, and minimise any interruptions of feeding, particularly to sedated and ventilated patients. It may also be prudent to increase the conventional non-evidence-based 200-mL gastric residual volume cut-off to 500 mL to minimise energy deficits. Future studies need to investigate what an appropriate upper limit for GRV may be and how clinicians could best interpret these values in clinical practice.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

We would like to thank all the ICU staff at Imperial College Healthcare NHS Trust for allowing us to conduct our research, without which our work would not have been possible. We would also like to thank Mike Gribbon (Clinical Audit Facilitator) for providing the APACHE II scores.

Conflict of interests, source of funding and authorship

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References

The authors declare that they have no competing interests.

A small grant was received from the Hammersmith Hospital Charity Trustees for the statistical analysis. We are also grateful for support from the NIHR Biomedical Research Centre funding scheme, which supports the research infrastructure at our Trust.

JOF, LW and MH conceived the study and participated in the design of the project. JOF, LW and BS were responsible for data acquisition and analysis of data. FG performed the statistical analysis. FG, MH, LW interpreted the data. LW and MH drafted the final manuscript. All authors read and approved the final manuscript submitted for publication.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Conflict of interests, source of funding and authorship
  9. References
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