Zum Hauptinhalt springen

Exploring the impact of arginine-supplemented immunonutrition on length of stay in the intensive care unit: A retrospective cross-sectional analysis.

Martin, ND ; Schott, LL ; et al.
In: PloS one, Jg. 19 (2024-04-26), Heft 4, S. e0302074
Online academicJournal

Exploring the impact of arginine-supplemented immunonutrition on length of stay in the intensive care unit: A retrospective cross-sectional analysis  Introduction

Background: Arginine-supplemented enteral immunonutrition has been designed to optimize outcomes in critical care patients. Existing formulas may be isocaloric and isoproteic, yet differ in L-arginine content, energy distribution, and in source and amount of many other specialized ingredients. The individual contributions of each may be difficult to pinpoint; however, all cumulate in the body's response to illness and injury. The study objective was to compare health outcomes between different immunonutrition formulas. Methods: Real-world data from October 2015 –February 2019 in the PINC AI Healthcare Database (formerly the Premier Healthcare Database) was reviewed for patients with an intensive care unit (ICU) stay and ≥3 days exclusive use of either higher L-arginine formula (HAF), or lower L-arginine formula (LAF). Multivariable generalized linear model regression was used to check associations between formulas and ICU length of stay. Results: 3,284 patients (74.5% surgical) were included from 21 hospitals, with 2,525 receiving HAF and 759 LAF. Inpatient mortality (19.4%) and surgical site infections (6.2%) were similar across groups. Median hospital stay of 17 days (IQR: 16) did not differ by immunonutrition formula. Median ICU stay was shorter for patients receiving HAF compared to LAF (10 vs 12 days; P<0.001). After adjusting for demographics, visit, severity of illness, and other clinical characteristics, associated regression-adjusted ICU length of stay for patients in the HAF group was 11% shorter [0.89 (95% CI: 0.84, 0.94; P<0.001)] compared to patients in the LAF group. Estimated adjusted mean ICU length of stay was 9.4 days (95% CI: 8.9, 10.0 days) for the HAF group compared to 10.6 days (95% CI: 9.9, 11.3 days) for the LAF group (P<0.001). Conclusions: Despite formulas being isocaloric and isoproteic, HAF use was associated with significantly reduced ICU length of stay, compared to LAF. Higher arginine immunonutrition formula may play a role in improving health outcomes in primarily surgical critically ill patients.

It is estimated that >25% of all hospital stays include an intensive care unit (ICU) admission [[1]]. Patients admitted to an ICU experience a 13% higher rate of in-hospital mortality, 15% higher rate of readmission, 8% higher rate of post-discharge emergency department visits, and 8% lower 1-year post-discharge survival rate compared to non-ICU patients or the general population [[2]]. Critical illness and ICU admission are associated with malnutrition, and malnutrition correlates with poor clinical outcomes, and increased hospital costs, length of stay (LOS), and readmission [[4]–[8]]. Nutritional intervention is essential for surgical and ICU patients [[9]].

Enteral nutrition (EN) is advised for critically ill patients in American and European intensive care and clinical nutrition society guidelines [[10]–[14]]. Benefits of EN include maintenance of gut integrity, modulation of the systemic immune response, and the attenuation of disease severity [[11]]. Immunonutrition enteral formulas, which include specialized nutrients to modulate the body's response to illness and injury, may be beneficial for surgical patients and those in the ICU [[11], [15]–[21]]. The literature suggests that for surgical and trauma patients, high-protein EN formulas containing supplemental arginine and other immunonutrients are associated with better outcomes, including decreased LOS and reduced infections [[11], [22]–[24]]. A meta-analysis examining immunonutrition versus standard nutrition formulas in surgical cancer patients found reduced risk of infections, including a sub-analysis of multiagent versus single-agent immunonutrition where the combination of arginine, nucleotides, and n-3 fatty acids significantly reduced the risk of wound and respiratory infections (by 36% and 39%, respectively) [[20]]. Other nutrients may also play a role in mitigating the immune response [[25]].

Recent work showed that critically ill patients who received standard high-protein EN were older, were less likely to be surgical or trauma patients, had shorter LOS, and had higher total cost per day in adjusted analyses compared to patients that received arginine-supplemented enteral immunonutrition [[27]]. These findings suggest the need for further exploration specific to immunonutrition formulas. Although similarly designed for nutrition and tolerance in critically ill patients, immunonutrition formulas differ in amount and type of immunonutrients, i.e., arginine and other micro and macronutrients [[17], [27]]. Research directly comparing commonly used immunonutrition formulas and clinical outcomes is sparse, and additional research on immunonutrition formulas in critically ill patients is recommended [[10], [29]]. This study used real-world data in a heterogeneous patient cohort from multiple US hospitals to examine differences in association between ICU LOS and the receipt of different isocaloric and isoproteic immunonutrition formulas containing either higher or lower amounts of L-arginine and other ingredient differences.

Materials and methods

Sample and study design

The sample consisted of inpatients age 18 years and older, discharged between October 2015 and February 2019, with a billing record of at least 3 days of use within 5 consecutive days of either higher L-arginine (18.7 g/L) formula (HAF), or lower L-arginine (11 g/L) formula (LAF), and a minimum of 1 billed day of ICU utilization. Both formulas contained equivalent amounts of protein and calories per liter (Table 1). A retrospective, cross-sectional study was conducted using data from the PINC AI Healthcare Database (formerly the Premier Healthcare Database) [[30]]. Immunonutrition formula type (i.e., HAF, LAF) was the exposure variable; ICU LOS was the primary outcome, and hospital LOS and surgical site infection were secondary outcomes. Presence of surgical site infection was determined via Medicare Severity Diagnosis Related Group (MS-DRG) at discharge.

Graph

Table 1 Nutrition comparison of the immunonutrition formulas (per liter).

EN Formula ContentsHAFLAF
Kcal/mL1.51.5
Protein, g (%)94 (25)93.8 (25)
Sourcehydrolyzed casein and argininehydrolyzed casein, whey, and arginine
Supplemental L-arginine, g18.711
Total arginine, g20.813
Carbohydrate, g (%)140 (38)172.4 (45)
Fiber, g--7.5
Fat, g (%)63.6 (37)51 (31)
Ω6:Ω31.5:11.7:1
EPA + DHA, g4.93.7
MCT:LCT50:5020:80
MCT, g31.810.2
Supplemental nucleotides, g1.8--
Select micronutrients
Vitamin C, mg1000304
Selenium, mcg10078
Zinc, mg3630.8

1 DHA, docosahexaenoic acid; EN, enteral nutrition; EPA, eicosapentaenoic acid; HAF, higher L-arginine (Impact® Peptide 1.5, Société des Produits Nestlé S.A) formula; LAF, lower L-arginine (Pivot® 1.5, Abbott Laboratories) formula; LCT, long-chain triglycerides; MCT, medium-chain triglycerides; Ω6:Ω3, ratio of omega 6 and omega 3 fatty acids

Data were from an all-payer, geographically diverse, hospital-based database representative of US hospitals, capturing service and billing information from all therapeutic areas for approximately 25% of all inpatient discharges in the country. The database serves as a reliable source used by national healthcare agencies, life science companies, and academic institutions [[30]]. Data consist of a nonrandom sample and are subject to limitations of an administrative database, including accuracy of coding and absence of certain clinical details. The database has been certified as de-identified and is not considered human subjects research. Study data and recorded information could not be identified directly or through identifiers linked to individuals. The authors accessed the data for research purposes on March 1, 2020, and could not identify individual participants during or after data collection. All data were compliant with the Health Insurance Portability and Accountability Act (HIPAA). As a result of these factors and US federal regulation 45 CFR 46, the study was deemed exempt from institutional review board evaluation and informed consent [[31]]. The study followed Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [[32]].

Baseline characteristics

Demographic information included patient age, self-reported sex, race, and ethnicity, and primary insurance payer. Clinical characteristics captured at discharge via ICD-10-CM and billing codes included trauma status, mechanical ventilation, rectal tube, diarrhea, constipation, and wound dehiscence/disruption; and via MS-DRG codes were surgery status, and extracorporeal membrane oxygenation (ECMO) or tracheostomy procedure. Severity of illness and risk of mortality were assessed via the 3M All Patient Refined DRG (APR DRG) Classification System (i.e., minor, moderate, major, or extreme). Elixhauser comorbidity score (overall) and each comorbidity assessed were evaluated at discharge via ICD-10-CM codes [[33]]. Additional comorbidities included malnutrition, pneumonia, septicemia, urinary tract infection, and C. difficile infection. Nutrition product utilization tracked total days used, EN pattern (consecutive vs not) and volume per day of feeding. Medication use evaluated prescription and number of days for antidiarrheal, antiemetic, and antibiotic class drugs. Visit characteristics included attending physician specialty, and admission type, point of origin, and discharge status, as submitted by hospitals according to Centers for Medicare and Medicaid Services criteria. Hospital characteristics included US census region (i.e., Midwest, Northeast, South, West), teaching status, urban/rural location, and bed size (i.e., <500 beds vs ≥500) of the facility where the patient was hospitalized.

Statistical analyses

The hypothesis was that ICU LOS would differ for patients receiving HAF and LAF. Comparisons between immunonutrition groups were done via Chi-square tests for dichotomous and categorical variables, which were reported as n (%). Continuous variables were reported as median (interquartile range, IQR) or mean (standard deviation, SD), and comparisons were made via Wilcoxon rank sum or t-tests, respectively. Prior to initiation, the study was powered at 80% and alpha value = 0.05 to detect a 2-day difference in ICU LOS, with a minimum sample size of 636 in each group. To minimize potential biases, hypotheses were established a priori, and design, analysis, and publication of the study were not contingent on the sponsor's approval or censorship.

A multivariable generalized linear model with negative binomial variance and log link function was used to evaluate associations between immunonutrition group and ICU LOS. For ease of interpretation, regression coefficients and 95% confidence intervals (CI) were exponentiated. Due to the log transformation, the exponentiated coefficients were interpreted as the percentage difference between the groups. In addition, least square means were used to estimate the adjusted mean ICU LOS for each group. Final covariates, chosen from clinically and statistically relevant variables, included patient demographics (i.e., age category, sex, and race), and visit (i.e., insurance payer, admission type, and discharge status), hospital (i.e., size and region), and clinical characteristics (i.e., APR DRG severity of illness and risk of mortality, surgery status, trauma status, EN pattern, nutrition units billed per day, septicemia, pneumonia, obesity, congestive heart failure, cancer, complicated diabetes, renal failure, malnutrition, ECMO or tracheostomy, mechanical ventilation, rectal tube, days of antidiarrheal medication use, and wound dehiscence/disruption). To assess model fit and properties of variables in the model, sensitivity analyses were completed. All analyses were conducted using SAS version 9.4. Statistical significance was defined as P<0.05.

Results

Sample characteristics

During the 3.5-year study period, 3,284 patients meeting inclusion criteria across 21 hospitals were identified (2,525 HAF, 759 LAF; Table 2). Mean age was 56.6 years (SD: 17.7). The sample was primarily male (67.5%), white (86.0%), and non-Hispanic/Latino or unknown ethnicity (97.3%). Primary insurance payer was Medicare (38.0%) or Medicaid (25.6%), followed by commercial (19.1%), managed care (8.1%), and other insurance (9.1%). Most patients were admitted from home (74.8%) and non-electively (88.0%). One-fifth of all study patients died during hospitalization, 22.3% were discharged home, and 47.2% were discharged to a long-term care or skilled nursing facility. Two-thirds of attending physicians were from a surgical specialty (66.9%). Comparisons between immunonutrition groups revealed differences in baseline characteristics. Patients receiving HAF had a higher frequency of being treated in a teaching hospital located in the Midwest/West, whereas patients receiving LAF had a higher frequency of being treated at a hospital in an urban setting, with 500+ beds, located in the Northeast/South (all P<0.001).

Graph

Table 2 Patient characteristics.

CharacteristicTotal N = 3,284 n (%)HAF N = 2,525 n (%)LAF N = 759 n (%)
Age, years, median (IQR)59 (25)59 (24)58 (28)
Age group, years*
 18–34492 (15.0)357 (14.1)135 (17.8)
 35–49509 (15.5)380 (15.0)129 (17.0)
 50–641,060 (32.3)836 (33.1)224 (29.5)
 65–79953 (29.0)745 (29.5)208 (27.4)
 ≥ 80270 (8.2)207 (8.2)63 (8.3)
Female**1,068 (32.5)866 (34.3)202 (26.6)
Race**
 White2,825 (86.0)2,219 (87.9)606 (79.8)
 Black244 (7.4)151 (6.0)93 (12.3)
 Other/unknown215 (6.5)155 (6.1)60 (7.9)
Hispanic or Latino ethnicity**89 (2.7)71 (2.8)18 (2.4)
Discharge status**
 Inpatient mortality636 (19.4)484 (19.2)152 (20.0)
 Home / Home healthcare733 (22.3)620 (24.6)113 (14.9)
 LTC/SNF/ICF/Rehabilitation1,547 (47.2)1,122 (44.4)425 (56.0)
 Other/Unknown368 (11.2)299 (11.8)69 (9.1)
EN pattern of 3 consecutive days (versus EN for 3 days in 5)*2,938 (89.5)2237 (88.6)701 (92.4)
Nutrition utilization, median (IQR)
 Days of EN use7 (8)7 (7)7 (7)
 Total 1000 mL units of EN billed*8.8 (10)9 (10)8 (9)
 EN units billed per day**1.18 (0.40)1.20 (0.50)1.17 (0.29)
ECMO or tracheostomy833 (25.4)641 (25.4)192 (25.3)
Mechanical ventilation**2,575 (78.4)1,930 (76.4)645 (85.0)
APR DRG severity of illness
 Minor/moderate418 (12.7)322 (12.8)98 (12.7)
 Major /extreme2,866 (87.3)2,203 (87.3)663 (87.4)
APR DRG risk of mortality**
 Minor/moderate644 (19.6)531 (21.0)113 (14.9)
 Major/extreme2,640 (80.4)1,994 (79.0)646 (85.1)
Elixhauser comorbidityascore, median (IQR)**5 (4)5 (4)6 (5)
Urinary tract infection444 (13.5)335 (13.3)109 (14.4)
C. difficile infection161 (4.9)116 (4.6)45 (5.9)
Wound dehiscence/disruption*101 (3.1)86 (3.4)15 (2.0)

  • 2 APR DRG, All Patient Refined Diagnosis Related Groups Classification System; ECMO, extracorporeal membrane oxygenation; EN, enteral nutrition; HAF, higher L-arginine (18.7 g/L) formula; ICF, intermediate care facility; IQR, interquartile range; LAF, lower L-arginine (11 g/L) formula; LTC, long-term care facility; SNF, skilled nursing facility
  • 3 * P < 0.05,
  • 4 ** P < 0.001
  • 5 a Elixhauser comorbidity score was assessed using Quan's algorithm of primary or secondary ICD-10-CM diagnosis codes at discharge. Prevalence of each comorbidity was evaluated, including congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorders, peripheral vascular disorders, hypertension, paralysis, other neurological disorders, chronic pulmonary disease, complicated diabetes, uncomplicated diabetes, hypothyroidism, renal failure, liver diseases, peptic ulcer disease excluding bleeding, HIV disease (human immunodeficiency virus disease), lymphoma, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis/collagen vascular disease, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, deficiency anemia, alcohol abuse, drug abuse, psychosis, and depression.
Clinical characteristics

Median days of feeding across groups was 7 days (IQR: 8). Total median volume of units billed was 1 unit higher for patients who received HAF compared to LAF, equating to a median daily difference of 30 mL (each P<0.01). Overall, 89.5% of patients received EN for 3 or more consecutive days. Prevalence of diarrhea was <5% and did not differ by EN formula. Prevalence of constipation was higher for patients receiving HAF (10.8%) compared to LAF (5.0%, P<0.001), whereas rectal tube use was lower (9.1% vs 19.4%, P<0.001, respectively). Across groups, prescription of antiemetic medications was 69.1% for a median of 2 days (IQR: 4). Patients who received HAF had a higher rate of antidiarrheal medication use compared to LAF (26.7% vs 15.7%, P<0.001) and for patients prescribed antidiarrheals, a higher median number of medication days (9 vs 3, P<0.001). A quarter of all patients had a MS-DRG coding of ECMO or tracheostomy, of which <1% received ECMO. Use of mechanical ventilation was reported 8.6% less frequently in patients receiving HAF compared to LAF (P<0.001). Wound dehiscence/disruption was noted in 3.1% of all patients.

Diagnosis of malnutrition did not differ between groups (28.9% overall), whereas prevalence of obesity was 7.5% lower for the HAF compared to LAF group (P<0.001) (Fig 1). Seventy-five percent of all patients had a surgical MS-DRG, and 33.6% had a trauma diagnosis. More patients in the HAF group were classified as surgical (difference 4.3%, P = 0.02), whereas more patients in the LAF group had a trauma diagnosis (difference 10.3%, P<0.001).

Graph: Fig 1 Clinical characteristics and comorbidities at discharge by immunonutrition formula group.HAF, higher L-arginine (18.7 g/L) formula; LAF, lower L-arginine (11 g/L) formula. * P < 0.05, ** P < 0.001.

Overall, most patients were classified as major or extreme severity of illness (87.3%) and risk of mortality (80.4%), with the latter being 6% less frequent in the HAF cohort (P<0.001). Median Elixhauser comorbidity score was 1 point lower in the HAF cohort (P<0.001). Several specific comorbidities differed by immunonutrition cohort. Patients in the HAF cohort more frequently had cancer compared to the LAF cohort (difference 8.6%, P<0.001) but a lower frequency of neurological disorders, coagulopathy, or cardiac arrhythmia (differences ≥10%, all P<0.001) as well as pneumonia, septicemia, complicated diabetes, complicated hypertension, congestive heart failure, valvular disease, peripheral vascular disorders, renal failure, pulmonary circulation disorders, and paralysis (differences 3–9%, all P<0.05). Immunonutrition groups did not differ in proportion of patients with chronic pulmonary disease (23.4% overall), depression (17.4%), or liver disease (12.7%) comorbidities.

Outcomes

Mean ICU LOS was 2.6 days shorter for the HAF cohort compared to the LAF cohort, and the estimated adjusted mean difference was 1.2 days shorter per least square means in the multivariable model (P<0.001) (Table 3, Fig 2). In accordance, regression-adjusted ICU LOS was 11% shorter for patients in the HAF group compared to patients in the LAF group (P<0.001), after adjusting for multiple patient, visit, severity of illness, and other clinical characteristics, including inpatient mortality (Fig 3). Shorter ICU LOS was also associated with older age category, large hospital size, major APR-DRG severity of illness, and cancer (P<0.001).

Graph: Fig 2 Mean ICU Length of Stay (days) by immunonutrition formula group a.HAF, higher L-arginine (18.7 g/L) formula; ICU, intensive care unit; LAF, lower L-arginine (11 g/L) formula. ** P < 0.001. a Error bars are 95% confidence intervals for mean. Adjusted mean value estimated via general linear model least squares means function. Adjusted model included the following covariates: (i.e., age category, sex, race, primary insurance payer, admission type, discharge status, hospital bed size and geographic region, EN pattern, nutrition units billed per day, severity of illness, risk of mortality, surgery status, trauma status, septicemia, pneumonia, obesity, congestive heart failure, cancer, complicated diabetes, renal failure, malnutrition, ECMO or tracheostomy, mechanical ventilation, rectal tube, days of antidiarrheal medication use, and wound dehiscence/disruption.

Graph: Fig 3 Multivariate regression estimates for ICU length of stay a.APR DRG, All Patient Refined Diagnosis Related Groups Classification System; ECMO, extracorporeal membrane oxygenation; EN, enteral nutrition; ICF, intermediate care facility; ICU, intensive care unit; LTC, long-term care facility; ref. = reference group/category; SNF, skilled nursing facility. a After adjustment for covariates, regression-adjusted ICU length of stay was 11% shorter for patients receiving higher L-arginine (18.7 g/L) formula compare with patients receiving lower L-arginine (11 g/L) formula.

Graph

Table 3 Unadjusted data.

CharacteristicTotal N = 3,284HAF N = 2,525LAF N = 759
ICU LOS, mean (SD)**13.3 (11.1)12.7 (11.0)15.3 (11.1)
ICU LOS, median (IQR)**10 (11)10 (11)12 (11)
Hospital LOS, median (IQR)17 (16)17 (16)18 (16)
LOS live discharges, median (IQR)19 (17)18 (17)19 (17)
Surgical site infections, n (%)203 (6.2)151 (6.0)52 (6.9)
Percent of patients with antibiotic use*94.994.297.1
 Days of antibiotic use, median (IQR)19 (16)19 (17)19 (16)

  • 6 ICU, intensive care unit; IQR, interquartile range; HAF, higher L-arginine (18.7 g/L) formula; IQR, interquartile range; LAF, lower L-arginine (11 g/L) formula; LOS, length of stay; SD, standard deviation
  • 7 * P < 0.05,
  • 8 ** P < 0.001

Longer ICU LOS was associated with several characteristics in the multivariable model. For example, use of ECMO or tracheostomy and mechanical ventilation were associated with a 61% and 36% longer ICU LOS, respectively. Surgery diagnosis, evidence of wound dehiscence/disruption, not being discharged home, rectal tube, and pneumonia were associated with 20–30% longer ICU LOS in the adjusted model.

Surgical site infections were noted in 6.2% of patients and were similar across groups. Antibiotics were prescribed to 94.9% of patients for a median of 19 days (IQR: 16). Overall, median hospital LOS was similar between immunonutrition groups.

Discussion

This study used real-world data to compare critical care patients, primarily surgical, receiving different immunonutrition formulas. After controlling for differences in demographic, visit, and clinical characteristics, on average, patients receiving HAF spent 1 day less in the ICU than patients receiving LAF. In univariate analysis, frequency of wound dehiscence/disruption and surgical site infections were low and clinically similar between cohorts.

The importance of nutritional assessment and protocols in the ICU and after surgery is well-documented [[6], [8], [14]]. Nutrition intervention has been shown to reduce LOS, readmissions, and costs and improve survival [[9], [35]–[37]]. ICU LOS in our study was similar to the 13–14 days reported elsewhere for patients receiving immunonutrition [[15], [23]]. As expected, several covariates in the current analysis showed a stronger association with ICU LOS than immunonutrition, including risk of mortality, tracheostomy, mechanical ventilation, surgical diagnosis, wound dehiscence/disruption, rectal tube, and pneumonia. Nonetheless, HAF immunonutrition was significantly associated with shorter ICU LOS.

Four out of 5 study patients were categorized as major or extreme severity of illness and risk of mortality; however, only 28.9% of patients had a diagnosis of malnutrition, most likely due to variation in coding. Given data showing 49% of patients hospitalized >7 days remain malnourished or decline [[38]], malnutrition rate was expected much higher in our sample. The median 7 days study patients received HAF or LAF is comparable to an 8–9 day mean observed in a multi-center trial comparing immunonutrition containing L-arginine, n-3 fatty acids, and nucleotides with standard formula [[15]]. Furthermore, 5 days has been suggested as the minimum number of days for immunonutrition to be efficacious [[39]].

The potential for nutritional modulation of the immune response supports the use of immunonutrition formulas for patients undergoing major surgery or suffering extensive injury [[11], [17], [22]]. Prior research suggests immunonutrition formulas show benefit versus standard EN in patients having major elective surgery, and in critically ill surgical and trauma patients [[11], [15], [20]–[24], [27], [40]]. Accordingly, 74.5% of patients in the current study had a surgical diagnosis across a range of conditions. Results of many studies report that immunonutrition is associated with shorter LOS and fewer postoperative infectious or other complications [[16], [22], [24], [42]]. Mean LOS of 19–23 days reported in several studies was similar to hospital LOS found in both groups here, although other studies reported both shorter and longer stays (10–16 and 25–28 days) [[15], [22], [24]]. Rate of surgical site infection (6.2%) in the current study was similar to prior studies, although the range is large (4–63%) [[15], [22], [24]]. An a priori sub-study in a meta-analysis of immunonutrition in major elective surgery found a significant reduction in risk of infectious complications for studies comparing formulas containing a blend of arginine, n-3 fatty acids, and nucleotides to standard EN versus other arginine-supplemented EN compared to standard [[22]]. Differences in study populations, comparator groups, infection types, timing, and immunonutrient content of formulas likely influenced variation in results.

Although a definitive effect cannot be assigned to single ingredients in immunonutrition formulas, research suggests that certain nutrients are associated with particular clinical functions and potential benefits [[17], [25], [28], [43]]. For critically ill surgical and trauma patients, nutrition guidelines suggest administration of formulas to meet higher protein needs that also provide adequate energy and contain L-arginine and fish oil [[11]]. European guidelines on surgical nutrition also include nucleotides on the list of EN immunonutrients suggested for malnourished patients having major cancer surgery [[45]].

Arginine serves as a conditionally essential amino acid, participating in fundamental metabolic pathways [[43], [46]]. The role of arginine in immune function modulation, nitric oxide synthesis, and wound healing may be particularly advantageous for critically ill surgical patients [[18], [28], [43], [46]]. The immunosuppression that commonly follows major surgery is linked to the depletion of arginine by myeloid deprived suppressor cells expressing arginase-1, and thereby causing T-lymphocyte dysfunction [[47]].

It has been estimated that patients receiving enteral immunonutrition in the ICU receive on average 15 to 30 g of supplemental L-arginine per day [[28]]. A randomized controlled trial comparing different daily doses of L-arginine (5.7 vs 12.3 vs 18.9 g) postoperatively in enterally fed head and neck cancer patients found LOS and fistula formation were minimized most in the group receiving the highest dose [[49]]. In the current study, all patients received about 1 liter of formula per day with the difference in supplemental L-arginine being approximately 7.7g/L between HAF and LAF.

In addition to the difference in L-arginine content, the HAF examined here contains a higher amount of omega-3 fatty acids than LAF, as well as supplemental nucleotides. Omega-3 fatty acids and eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from fish oil have anti-inflammatory actions and may help mitigate arginine deficiency by producing less inflammatory prostaglandins, thereby reducing induction of arginase-1 [[50]]. Nucleotides support replication of rapidly dividing cells (i.e., lymphocytes) by providing a source of purine and pyrimidine bases for DNA/RNA production, and in pre-clinical studies have been shown to help clear pathogens through the action of macrophages and natural killer cells [[20], [52]]. In particular, surgical stress or episodes of infection following injury show an increased demand for nucleotides to synthesize immune cells, for tissue repair, and to maintain organ function; however, there are very little data studying nucleotides in adults separate from immunonutrition formulas [[52]].

In association with immune dysfunction, inflammation, and oxidative stress, the body's response to critical illness and injury involves trace elements and select vitamins. As such, immunonutrition formulas often include higher amounts of zinc, selenium, and vitamin C. Guidelines have identified these micronutrients, among others, for higher risk of deficiency during critical illness as well as inadequacy associated with worse outcomes [[26]]. Both LAF and HAF have increased amounts of vitamin C, selenium, and zinc compared to standard formula. Given the variability in nutrients and dosing of immunonutrition diets, guidelines suggest additional evaluation of specialized nutrition formulas [[10]].

Study limitations reflecting our use of observational administrative data include reliance upon diagnoses and procedure coding (e.g., codes do not differentiate between diagnoses on admission vs clinical outcomes). Further, because the study did not include chart review, the administration, adequacy, and timing of nutrition provided, as well as the total dose of antidiarrheals and the magnitude and severity of diarrhea could not be accounted for. Whether patients received additional supplementation of certain micronutrients outside of an EN formula was also not assessed. Although many potential confounders were included in adjusted analyses, distinctions between immunonutrition cohorts, including lack of hospital overlap, were evident. Differences in unmeasured characteristics may exist and could have influenced outcomes. Because authors were employees or received direct or indirect payment from the study sponsor for this work, potential biases were minimized by presenting both significant and non-significant results.

The importance of maximizing nutritional benefits in critically ill patients is validated by the associations between ICU stay, nutritional status, and clinical and healthcare utilization outcomes. Given that charges from ICU services may represent nearly 50% of aggregate total hospital charges [[1]], the decrease in ICU LOS measured in the present study helps explain earlier work showing HAF associated with a lower hospital cost per day than either LAF or standard high-protein EN [[27]]. In the US, 5 million patients annually are admitted to the ICU at an average of $4,300 per day [[53]]. Given the average charge per day outside the ICU is $1,143 [[54]], the savings estimated from one less day in the ICU are significant. From a clinical standpoint, ICUs admirably provide lifesaving care for critically ill patients. Nonetheless, the risk of a hospital-acquired infection or a complication caused by immobility or medication error increases the longer a patient is in the ICU [[55]].

Conclusions

In sum, higher L-arginine-supplemented immunonutrition also containing fish oil and nucleotides may play a role in improving health outcomes in the critically ill as evidenced by shorter ICU LOS, primarily in surgical patients. In this study of administrative data, immunonutrition formula type was not associated with significant unadjusted differences in hospital stay, surgical site infections, or mortality. The reduction in ICU LOS begets further questions about other clinical outcomes relevant to immunonutrition and ICU LOS. Possible candidates for future study include post-admission sepsis, pneumonia, and surgical site infection, although each would require extensive analysis involving chart review. Using a select immunonutrition formula for patients in the ICU may provide healthcare utilization savings, nonetheless, given the heterogeneity of patients, diagnoses, and phase of illness, individual circumstances should guide nutrition intervention for patients.

Cate Polacek, senior medical writer employed by PINC AI Applied Sciences, Premier Inc., provided manuscript editing and publication support.

Footnotes 1 I have read the journal's policy and the authors of this manuscript have the following competing interests: NDM is employed by University of Pennsylvania-Perelman School of Medicine and provided consulting services to Nestlé Health Science on this project. LLS and ZC are employees of Premier Inc., Charlotte, NC, which was contracted by Nestlé Health Science for this project. MKM, AMD, CCL and KAT are employees of Nestlé Health Science. This does not alter our adherence to PLOS ONE policies on sharing data and materials. References Barrett ML, Smith MW, Elixhauser A, Honigman LS, Pines JM. Utilization of Intensive Care Services, 2011. HCUP Statistical Brief [Internet]. 2014; December. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb185-Hospital-Intensive-Care-Units-2011.pdf. 2 Doherty Z, Kippen R, Bevan D, Duke G, Williams S, Wilson A, et al. Long-term outcomes of hospital survivors following an ICU stay: A multi-centre retrospective cohort study. PLoS One. (2022); 17(3):e0266038. Epub 20220328. doi: 10.1371/journal.pone.0266038, 35344543. 3 Hill AD, Fowler RA, Pinto R, Herridge MS, Cuthbertson BH, Scales DC. Long-term outcomes and healthcare utilization following critical illness—a population-based study. Crit Care. (2016); 20:76. Epub 20160331. doi: 10.1186/s13054-016-1248-y, 27037030. 4 Fingar KR, Weiss AJ, Barrett ML, Elixhauser A, Steiner CA, Guenter P, et al. All-Cause Readmissions Following Hospital Stays for Patients With Malnutrition, 2013. HCUP Statistical Brief [Internet]. 2016; (December). https://www.hcup-us.ahrq.gov/reports/statbriefs/sb218-Malnutrition-Readmissions-2013.pdf. 5 Lew CCH, Yandell R, Fraser RJL, Chua AP, Chong MFF, Miller M. Association Between Malnutrition and Clinical Outcomes in the Intensive Care Unit: A Systematic Review [Formula: see text]. JPEN J Parenter Enteral Nutr. (2017); 41(5):744–58. Epub 20160202. doi: 10.1177/0148607115625638, 26838530. 6 Moisey LL, Merriweather JL, Drover JW. The role of nutrition rehabilitation in the recovery of survivors of critical illness: underrecognized and underappreciated. Crit Care. (2022); 26(1):270. Epub 20220908. doi: 10.1186/s13054-022-04143-5, 36076215. 7 Weiss AJ, Fingar KR, Barrett ML, Elixhauser A, Steiner CA, Guenter P, et al. Characteristics of Hospital Stays Involving Malnutrition, 2013. HCUP Statistical Brief [Internet]. 2016; September. https://www.hcup-us.ahrq.gov/reports/statbriefs/sb210-Malnutrition-Hospital-Stays-2013.pdf. 8 Guenter P, Abdelhadi R, Anthony P, Blackmer A, Malone A, Mirtallo JM, et al. Malnutrition diagnoses and associated outcomes in hospitalized patients: United States, 2018. Nutr Clin Pract. (2021); 36(5):957–69. Epub 2021/09/07. doi: 10.1002/ncp.10771, 34486169. 9 Martinez-Ortega AJ, Pinar-Gutierrez A, Serrano-Aguayo P, Gonzalez-Navarro I, Remon-Ruiz PJ, Pereira-Cunill JL, et al. Perioperative Nutritional Support: A Review of Current Literature. Nutrients. (2022); 14(8). Epub 20220412. doi: 10.3390/nu14081601, 35458163. Compher C, Bingham AL, McCall M, Patel J, Rice TW, Braunschweig C, et al. Guidelines for the provision of nutrition support therapy in the adult critically ill patient: The American Society for Parenteral and Enteral Nutrition. JPEN J Parenter Enteral Nutr. (2022); 46(1):12–41. Epub 20220103. doi: 10.1002/jpen.2267, 34784064. Taylor BE, McClave SA, Martindale RG, Warren MM, Johnson DR, Braunschweig C, et al. Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Adult Critically Ill Patient: Society of Critical Care Medicine (SCCM) and American Society for Parenteral and Enteral Nutrition (A.S.P.E.N.). Crit Care Med. (2016); 44(2):390–438. Epub 2016/01/16. doi: 10.1097/CCM.0000000000001525, 26771786. Reintam Blaser A, Starkopf J, Alhazzani W, Berger MM, Casaer MP, Deane AM, et al. Early enteral nutrition in critically ill patients: ESICM clinical practice guidelines. Intensive Care Med. (2017); 43(3):380–98. Epub 20170206. doi: 10.1007/s00134-016-4665-0, 28168570. Singer P, Blaser AR, Berger MM, Alhazzani W, Calder PC, Casaer MP, et al. ESPEN guideline on clinical nutrition in the intensive care unit. Clin Nutr. (2019); 38(1):48–79. Epub 20180929. doi: 10.1016/j.clnu.2018.08.037, 30348463. Singer P, Blaser AR, Berger MM, Calder PC, Casaer M, Hiesmayr M, et al. ESPEN practical and partially revised guideline: Clinical nutrition in the intensive care unit. Clin Nutr. (2023); 42(9):1671–89. Epub 20230715. doi: 10.1016/j.clnu.2023.07.011, 37517372. Lopez-Delgado JC, Grau-Carmona T, Trujillano-Cabello J, Garcia-Fuentes C, Mor-Marco E, Bordeje-Laguna ML, et al. The Effect of Enteral Immunonutrition in the Intensive Care Unit: Does It Impact on Outcomes? Nutrients. (2022); 14(9). Epub 20220501. doi: 10.3390/nu14091904, 35565870. Shen J, Dai S, Li Z, Dai W, Hong J, Huang J, et al. Effect of Enteral Immunonutrition in Patients Undergoing Surgery for Gastrointestinal Cancer: An Updated Systematic Review and Meta-Analysis. Front Nutr. (2022); 9:941975. Epub 20220629. doi: 10.3389/fnut.2022.941975, 35845793. Pollock GR, Van Way CW. Immune-enhancing nutrition in surgical critical care. Mo Med. (2012); 109(5):388–92., 23097945. McCarthy MS, Martindale RG. Immunonutrition in Critical Illness: What Is the Role? Nutr Clin Pract. (2018); 33(3):348–58. doi: 10.1002/ncp.10102, 29878555. Brown B, Roehl K, Betz M. Enteral nutrition formula selection: current evidence and implications for practice. Nutr Clin Pract. (2015); 30(1):72–85. Epub 20141216. doi: 10.1177/0884533614561791, 25516537. Yu K, Zheng X, Wang G, Liu M, Li Y, Yu P, et al. Immunonutrition vs Standard Nutrition for Cancer Patients: A Systematic Review and Meta-Analysis (Part 1). JPEN J Parenter Enteral Nutr. (2020); 44(5):742–67. Epub 20191111. doi: 10.1002/jpen.1736, 31709584. Kavalukas S, McClave SA. Immunonutrition vs standard nutrition for patients with cancer. Nutr Clin Pract. (2023). Epub 20230214. doi: 10.1002/ncp.10963, 36788760. Drover JW, Dhaliwal R, Weitzel L, Wischmeyer PE, Ochoa JB, Heyland DK. Perioperative use of arginine-supplemented diets: a systematic review of the evidence. J Am Coll Surg. (2011); 212(3):385–99, 99 e1. Epub 20110117. doi: 10.1016/j.jamcollsurg.2010.10.016, 21247782. Prest PJ, Justice J, Bell N, McCarroll R, Watson CM. A Volume-Based Feeding Protocol Improves Nutrient Delivery and Glycemic Control in a Surgical Trauma Intensive Care Unit. JPEN J Parenter Enteral Nutr. (2020); 44(5):880–8. Epub 2019/09/19. doi: 10.1002/jpen.1712, 31529520. Rowan NR, Johnson JT, Fratangelo CE, Smith BK, Kemerer PA, Ferris RL. Utility of a perioperative nutritional intervention on postoperative outcomes in high-risk head & neck cancer patients. Oral Oncol. (2016); 54:42–6. Epub 20160121. doi: 10.1016/j.oraloncology.2016.01.006, 26803343. Weyh C, Kruger K, Peeling P, Castell L. The Role of Minerals in the Optimal Functioning of the Immune System. Nutrients. (2022); 14(3). Epub 20220202. doi: 10.3390/nu14030644, 35277003. Berger MM, Shenkin A, Schweinlin A, Amrein K, Augsburger M, Biesalski HK, et al. ESPEN micronutrient guideline. Clin Nutr. (2022); 41(6):1357–424. Epub 20220226. doi: 10.1016/j.clnu.2022.02.015, 35365361. Bozeman MC, Schott LL, Desai AM, Miranowski MK, Baumer DL, Lowen CC, et al. Healthcare Resource Utilization and Cost Comparisons of High-Protein Enteral Nutrition Formulas Used in Critically Ill Patients. J Health Econ Outcomes Res. (2022); 9(2):1–10. Epub 20220701. doi: 10.36469/001c.36287, 35854856. Rosenthal MD, Carrott PW, Patel J, Kiraly L, Martindale RG. Parenteral or Enteral Arginine Supplementation Safety and Efficacy. J Nutr. (2016); 146(12):2594S–600S. Epub 20161109. doi: 10.3945/jn.115.228544, 27934650. Howes N, Atkinson C, Thomas S, Lewis SJ. Immunonutrition for patients undergoing surgery for head and neck cancer. Cochrane Database Syst Rev. (2018); 8(8):CD010954. Epub 20180830. doi: 10.1002/14651858.CD010954.pub2, 30160300. PINC AI™ Applied Sciences, Premier Inc. PINC AI™ Healthcare Database: Data that informs and performs. [whitepaper] April 2023. Accessed 23 May 2023. https://offers.pinc-ai.com/PINC-AI-Healthcare-Database-White-Paper-LP.html. U.S. Department of Health & Human Services, Office for Human Research Protection, 45 CFR 46. Updated March 10, 2021. Accessed 07 Mar 2023. https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. (2007); 370(9596):1453–7. Epub 2007/12/08. doi: 10.1016/S0140-6736(07)61602-X, 18064739. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. (1998); 36(1):8–27. Epub 1998/02/07. doi: 10.1097/00005650-199801000-00004, 9431328. van Walraven C, Austin PC, Jennings A, Quan H, Forster AJ. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. (2009); 47(6):626–33. Epub 2009/05/13. doi: 10.1097/MLR.0b013e31819432e5, 19433995. Gomes F, Baumgartner A, Bounoure L, Bally M, Deutz NE, Greenwald JL, et al. Association of Nutritional Support With Clinical Outcomes Among Medical Inpatients Who Are Malnourished or at Nutritional Risk: An Updated Systematic Review and Meta-analysis. JAMA Netw Open. (2019); 2(11):e1915138. Epub 2019/11/21. doi: 10.1001/jamanetworkopen.2019.15138, 31747030. Pratt KJ, Hernandez B, Blancato R, Blankenship J, Mitchell K. Impact of an interdisciplinary malnutrition quality improvement project at a large metropolitan hospital. BMJ Open Qual. (2020); 9(1). doi: 10.1136/bmjoq-2019-000735, 32213547. Tyler R, Barrocas A, Guenter P, Araujo Torres K, Bechtold ML, Chan LN, et al. Value of Nutrition Support Therapy: Impact on Clinical and Economic Outcomes in the United States. JPEN J Parenter Enteral Nutr. (2020); 44(3):395–406. Epub 2020/01/30. doi: 10.1002/jpen.1768, 31994761. Allard JP, Keller H, Jeejeebhoy KN, Laporte M, Duerksen DR, Gramlich L, et al. Decline in nutritional status is associated with prolonged length of stay in hospitalized patients admitted for 7 days or more: A prospective cohort study. Clin Nutr. (2016); 35(1):144–52. Epub 20150121. doi: 10.1016/j.clnu.2015.01.009, 25660316. Kudsk KA. Discrepancies between nutrition outcome studies: is patient care the issue? JPEN J Parenter Enteral Nutr. (2001); 25(2 Suppl):S57–60. doi: 10.1177/014860710102500212, 11288925. Moya P, Soriano-Irigaray L, Ramirez JM, Garcea A, Blasco O, Blanco FJ, et al. Perioperative Standard Oral Nutrition Supplements Versus Immunonutrition in Patients Undergoing Colorectal Resection in an Enhanced Recovery (ERAS) Protocol: A Multicenter Randomized Clinical Trial (SONVI Study). Medicine (Baltimore). (2016); 95(21):e3704. doi: 10.1097/MD.0000000000003704, 27227930. Farber MS, Moses J, Korn M. Reducing costs and patient morbidity in the enterally fed intensive care unit patient. JPEN J Parenter Enteral Nutr. (2005); 29(1 Suppl):S62–9. doi: 10.1177/01486071050290S1S62, 15709547. Atkinson S, Sieffert E, Bihari D. A prospective, randomized, double-blind, controlled clinical trial of enteral immunonutrition in the critically ill. Guy's Hospital Intensive Care Group. Crit Care Med. (1998); 26(7):1164–72. doi: 10.1097/00003246-199807000-00013, 9671364. Marti I Lindez AA, Reith W. Arginine-dependent immune responses. Cell Mol Life Sci. (2021); 78(13):5303–24. Epub 20210526. doi: 10.1007/s00018-021-03828-4, 34037806. Patkova A, Joskova V, Havel E, Kovarik M, Kucharova M, Zadak Z, et al. Energy, Protein, Carbohydrate, and Lipid Intakes and Their Effects on Morbidity and Mortality in Critically Ill Adult Patients: A Systematic Review. Adv Nutr. (2017); 8(4):624–34. Epub 2017/07/16. doi: 10.3945/an.117.015172, 28710148. Weimann A, Braga M, Carli F, Higashiguchi T, Hubner M, Klek S, et al. ESPEN practical guideline: Clinical nutrition in surgery. Clin Nutr. (2021); 40(7):4745–61. Epub 20210419. doi: 10.1016/j.clnu.2021.03.031, 34242915. Wu G, Meininger CJ, McNeal CJ, Bazer FW, Rhoads JM. Role of L-Arginine in Nitric Oxide Synthesis and Health in Humans. Adv Exp Med Biol. (2021); 1332:167–87. doi: 10.1007/978-3-030-74180-8_10, 34251644. Makarenkova VP, Bansal V, Matta BM, Perez LA, Ochoa JB. CD11b+/Gr-1+ myeloid suppressor cells cause T cell dysfunction after traumatic stress. J Immunol. (2006); 176(4):2085–94. doi: 10.4049/jimmunol.176.4.2085, 16455964. Marik PE, Flemmer M. The immune response to surgery and trauma: Implications for treatment. J Trauma Acute Care Surg. (2012); 73(4):801–8. doi: 10.1097/TA.0b013e318265cf87, 22976420. De Luis DA, Izaola O, Terroba MC, Cuellar L, Ventosa M, Martin T. Effect of three different doses of arginine enhanced enteral nutrition on nutritional status and outcomes in well nourished postsurgical cancer patients: a randomized single blinded prospective trial. Eur Rev Med Pharmacol Sci. (2015); 19(6):950–5., 25855918. Calder PC. n-3 PUFA and inflammation: from membrane to nucleus and from bench to bedside. Proc Nutr Soc. (2020):1–13. Epub 20200622. doi: 10.1017/S0029665120007077, 32624016. Bansal V, Syres KM, Makarenkova V, Brannon R, Matta B, Harbrecht BG, et al. Interactions between fatty acids and arginine metabolism: implications for the design of immune-enhancing diets. JPEN J Parenter Enteral Nutr. (2005); 29(1 Suppl):S75–80. doi: 10.1177/01486071050290S1S75, 15709549. Hess JR, Greenberg NA. The role of nucleotides in the immune and gastrointestinal systems: potential clinical applications. Nutr Clin Pract. (2012); 27(2):281–94. Epub 20120305. doi: 10.1177/0884533611434933, 22392907. Society of Critical Care Medicine. Critical Care Statistics. 2024. Accessed 01 Feb 2024. https://www.sccm.org/Communications/Critical-Care-Statistics. American Hospital Directory®. Custom dataset purchased in 2019 and sourced from Federal Fiscal Year 2017 Medicare IPPS Claims Data, Inpatient Statistics. Updated May 07, 2023. https://www.ahd.com/. Maki DG CC, Safdar N. Nosocomial Infection in the Intensive Care Unit. In: Parrillo JE, Dellinger RP, editors. Critical Care Medicine 3rd ed. Noninvasive ventilation for patients near the end of life: what do we know and what do we need to know? chap 51: Mosby; (2008). p. 1003–69. doi: 10.1016/B978-032304841-5.50053-4 Garrouste-Orgeas M, Philippart F, Bruel C, Max A, Lau N, Misset B. Overview of medical errors and adverse events. Ann Intensive Care. (2012); 2(1):2. Epub 20120216. doi: 10.1186/2110-5820-2-2, 22339769

By Niels D. Martin; Laura L. Schott; Mary K. Miranowski; Amarsinh M. Desai; Cynthia C. Lowen; Zhun Cao and Krysmaru Araujo Torres

Reported by Author; Author; Author; Author; Author; Author; Author

Titel:
Exploring the impact of arginine-supplemented immunonutrition on length of stay in the intensive care unit: A retrospective cross-sectional analysis.
Autor/in / Beteiligte Person: Martin, ND ; Schott, LL ; Miranowski, MK ; Desai, AM ; Lowen, CC ; Cao, Z ; Araujo Torres, K
Link:
Zeitschrift: PloS one, Jg. 19 (2024-04-26), Heft 4, S. e0302074
Veröffentlichung: San Francisco, CA : Public Library of Science, 2024
Medientyp: academicJournal
ISSN: 1932-6203 (electronic)
DOI: 10.1371/journal.pone.0302074
Schlagwort:
  • Humans
  • Male
  • Female
  • Middle Aged
  • Retrospective Studies
  • Aged
  • Cross-Sectional Studies
  • Dietary Supplements
  • Critical Illness therapy
  • Hospital Mortality
  • Immunonutrition Diet
  • Arginine administration & dosage
  • Arginine therapeutic use
  • Length of Stay
  • Intensive Care Units
  • Enteral Nutrition methods
Sonstiges:
  • Nachgewiesen in: MEDLINE
  • Sprachen: English
  • Publication Type: Journal Article; Research Support, Non-U.S. Gov't
  • Language: English
  • [PLoS One] 2024 Apr 26; Vol. 19 (4), pp. e0302074. <i>Date of Electronic Publication: </i>2024 Apr 26 (<i>Print Publication: </i>2024).
  • MeSH Terms: Arginine* / administration & dosage ; Arginine* / therapeutic use ; Length of Stay* ; Intensive Care Units* ; Enteral Nutrition* / methods ; Humans ; Male ; Female ; Middle Aged ; Retrospective Studies ; Aged ; Cross-Sectional Studies ; Dietary Supplements ; Critical Illness / therapy ; Hospital Mortality ; Immunonutrition Diet
  • References: Medicine (Baltimore). 2016 May;95(21):e3704. (PMID: 27227930) ; Nutr Clin Pract. 2012 Apr;27(2):281-94. (PMID: 22392907) ; Med Care. 2009 Jun;47(6):626-33. (PMID: 19433995) ; J Immunol. 2006 Feb 15;176(4):2085-94. (PMID: 16455964) ; Nutrients. 2022 Apr 12;14(8):. (PMID: 35458163) ; J Am Coll Surg. 2011 Mar;212(3):385-99, 399.e1. (PMID: 21247782) ; Clin Nutr. 2022 Jun;41(6):1357-1424. (PMID: 35365361) ; Crit Care Med. 1998 Jul;26(7):1164-72. (PMID: 9671364) ; Ann Intensive Care. 2012 Feb 16;2(1):2. (PMID: 22339769) ; Nutr Clin Pract. 2018 Jun;33(3):348-358. (PMID: 29878555) ; Nutr Clin Pract. 2021 Oct;36(5):957-969. (PMID: 34486169) ; Nutr Clin Pract. 2023 Aug;38(4):924-931. (PMID: 36788760) ; Clin Nutr. 2019 Feb;38(1):48-79. (PMID: 30348463) ; Mo Med. 2012 Sep-Oct;109(5):388-92. (PMID: 23097945) ; Front Nutr. 2022 Jun 29;9:941975. (PMID: 35845793) ; Oral Oncol. 2016 Mar;54:42-6. (PMID: 26803343) ; Clin Nutr. 2021 Jul;40(7):4745-4761. (PMID: 34242915) ; JPEN J Parenter Enteral Nutr. 2001 Mar-Apr;25(2 Suppl):S57-60. (PMID: 11288925) ; Intensive Care Med. 2017 Mar;43(3):380-398. (PMID: 28168570) ; JPEN J Parenter Enteral Nutr. 2022 Jan;46(1):12-41. (PMID: 34784064) ; Lancet. 2007 Oct 20;370(9596):1453-7. (PMID: 18064739) ; Eur Rev Med Pharmacol Sci. 2015;19(6):950-5. (PMID: 25855918) ; JPEN J Parenter Enteral Nutr. 2020 Mar;44(3):395-406. (PMID: 31994761) ; J Trauma Acute Care Surg. 2012 Oct;73(4):801-8. (PMID: 22976420) ; J Health Econ Outcomes Res. 2022 Jul 1;9(2):1-10. (PMID: 35854856) ; Clin Nutr. 2016 Feb;35(1):144-152. (PMID: 25660316) ; Nutr Clin Pract. 2015 Feb;30(1):72-85. (PMID: 25516537) ; Crit Care. 2016 Mar 31;20:76. (PMID: 27037030) ; Crit Care Med. 2016 Feb;44(2):390-438. (PMID: 26771786) ; Adv Exp Med Biol. 2021;1332:167-187. (PMID: 34251644) ; Proc Nutr Soc. 2020 Jun 22;:1-13. (PMID: 32624016) ; J Nutr. 2016 Dec;146(12):2594S-2600S. (PMID: 27934650) ; JPEN J Parenter Enteral Nutr. 2005 Jan-Feb;29(1 Suppl):S75-80. (PMID: 15709549) ; JAMA Netw Open. 2019 Nov 1;2(11):e1915138. (PMID: 31747030) ; JPEN J Parenter Enteral Nutr. 2020 Jul;44(5):742-767. (PMID: 31709584) ; Nutrients. 2022 May 01;14(9):. (PMID: 35565870) ; Cell Mol Life Sci. 2021 Jul;78(13):5303-5324. (PMID: 34037806) ; PLoS One. 2022 Mar 28;17(3):e0266038. (PMID: 35344543) ; Clin Nutr. 2023 Sep;42(9):1671-1689. (PMID: 37517372) ; Med Care. 1998 Jan;36(1):8-27. (PMID: 9431328) ; BMJ Open Qual. 2020 Mar;9(1):. (PMID: 32213547) ; Nutrients. 2022 Feb 02;14(3):. (PMID: 35277003) ; JPEN J Parenter Enteral Nutr. 2020 Jul;44(5):880-888. (PMID: 31529520) ; Crit Care. 2022 Sep 8;26(1):270. (PMID: 36076215) ; JPEN J Parenter Enteral Nutr. 2017 Jul;41(5):744-758. (PMID: 26838530) ; Cochrane Database Syst Rev. 2018 Aug 30;8:CD010954. (PMID: 30160300) ; JPEN J Parenter Enteral Nutr. 2005 Jan-Feb;29(1 Suppl):S62-9. (PMID: 15709547) ; Adv Nutr. 2017 Jul 14;8(4):624-634. (PMID: 28710148)
  • Substance Nomenclature: 94ZLA3W45F (Arginine)
  • Entry Date(s): Date Created: 20240426 Date Completed: 20240426 Latest Revision: 20240429
  • Update Code: 20240430
  • PubMed Central ID: PMC11051586

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

oder
oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

oder
oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

xs 0 - 576
sm 576 - 768
md 768 - 992
lg 992 - 1200
xl 1200 - 1366
xxl 1366 -