Impact of a multidisciplinary collaborative nutritional treatment model in patients who are critically ill with neurological disorders: A randomized controlled trial

Abstract

BACKGROUND:

Malnutrition is a widespread problem in critically ill patients with neurological disorders.

OBJECTIVE:

The purpose of this study is to investigate the effect of a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support on the outcome metrics in patients with neurological disorders who are critically ill.

METHODS:

We enrolled 84 participants who were hospitalized in the intensive care unit (ICU) of Yancheng No. 1 People’s Hospital for neurological disorders between June 2018 and December 2021. The participants were randomly assigned to the control group and the test group. The control group received traditional nutritional support, while the test group was treated with a multidisciplinary collaborative nutritional treatment mode based on a standardized unit for nutritional support. We collected the general information, feeding tolerance (FT), nutritional risk score, and laboratory indicators before intervention, after intervention for one week, and after intervention for 2 weeks, and other data of the participants.

RESULTS:

After the intervention, the test group scored significantly lower than the control group in the incidence of gastroparesis and diarrhea, as well as the NUTRIC score, with statistically significant differences ( $P<$ 0.001). The prealbumin levels in the test group increased progressively prior to intervention, after intervention for one week, and after intervention for two weeks. Compared to the control group, the test group had higher prealbumin levels prior to intervention, after intervention for one week, and after intervention for two weeks, with statistically significant differences ( $P<$ 0.001).

CONCLUSION:

We developed a multidisciplinary collaborative nutritional treatment model based on a standard unit for nutritional support. This model can improve neural function, FT, and pertinent outcome indicators and is generally applicable.

Keywords

Nervous system diseases nutrition therapy patient care team

1. Introduction

Patients who are critically ill with neurological disorders are a significant cause of death and severe disabilities [1]. Malnutrition nowadays is a widespread problem in these patients. Research shows that as many as 68% of critically ill patients are malnourished [2]. This group of patients experiences a high-consumption state and leads to hyperglycemia, loss of lean body mass. These metabolisms changes can cause immunosuppression, increase the risk of infection, prolong hospital stays, and even increase mortality [3]. Enteral feeding intolerance is associated with worse clinical outcomes [4]. Feeding intolerance occurs frequently in the critically ill, and the incidence was approximately 50% [5]. Factors such as autonomic nerve loss, gastric emptying disorder, disease severity, and medications can all lead to feeding intolerance.

Jeong et al. showed timely and effective nutritional therapy is essential in the management of cerebral injury to improve patient outcomes [6]. The Consensus of Chinese Experts on Enteral Nutritional Assistance for Neurological Diseases claims that nutritional assessment and support should be provided to patients at an early stage in order to decrease mortality and minimize the length of hospital stay [7]. Previous studies have reported that more than 30% of people with brain injury do not receive adequate nutritional support, even in experienced and motivated intensive care units [8, 9]. In view of the effectiveness of nutritional therapy, many scholars have further discussed the clinical effects of different kinds of nutrients intake in nutritional therapy. For example, one study explored the effect of French sea pine bark extract on the outcome of patients with traumatic brain injury and found that it could reduce inflammation, improve clinical status and malnutrition scores, and thus reduce mortality [10]. In addition, propolis has also been found to reduce inflammation in patients [11]. In Pahlavani et al.’s [12] study, the combined application of propolis and melatonin decreased the levels of IL-6 and C-reactive protein, suggesting that propolis and melatonin have certain anti-inflammatory effects. A review pointed out that there is a certain correlation between vitamin C and zinc deficiency and decreased innate immune response, and adequate intake of vitamin C and zinc may be necessary to reduce the adverse physiological effects [13]. However, guidelines for the provision and assessment of nutrition support therapy in pediatric critically ill patient points out that the use of immune-enhancing nutrition agents is not recommended [14].

Trial findings and guideline recommendations continue to be conflicting, making the translation of evidence into practice challenging [15]. Due to the considerable heterogeneity of the critically ill, medical nutrition therapy should be carefully implemented to them [16]. Therefore, it is necessary to formulate a targeted nutritional treatment plan according to the actual situation of each patient.

The nutritional knowledge of healthcare personnel is closely related to the dietary practices and outcomes of patients who are critically ill. Professional training can enhance awareness of nutrition screening, nutrition assessment, and other procedures among healthcare workers, as well as increase the consistency of their implementation [17]. In addition, if nursing staff receive training in nutritional knowledge, standardized management of enteral nutrition support, and standardized tolerance evaluations, the proportion of patients who meet nutritional support standards will increase [18, 19]. According to research, a scientific-based nutrition support program can help nurses in utilizing systematic and evidence-based strategies to satisfy the dietary needs of patients [20]. The multidisciplinary collaborative nutritional treatment model has generated an integral system and has yielded significant benefits in terms of improving the nutritional status of patients, reducing complications, and lowering hospitalization expenses [21, 22, 23].

Research on the clinical nutritional support mode has been conducted extensively in China. As reported by Yang, the construction of standard units for nutritional support facilitates the implementation of interdisciplinary collaboration for nutritional support and enhances the quality of nutritional work in general hospital wards [24]. The standardized unit for nutritional support is a medical complex that targets patients, has nursing specifications and goals for nutritional diagnosis and treatment that covers a certain district of the hospital. It is not a specific therapy, but a comprehensive treatment that implements a scientific management system for patients, comprehensively practices the patient-centered concept of medical care, and closely integrates multidisciplinary strengths. However, for patients with neurological disorders who are critically ill, the establishment of standardized units for nutritional support through the multidisciplinary collaborative nutritional treatment mode is still being investigated, and its specific implementation and the collaboration mode of the team require ongoing development and improvement.

In this study, we aim to build a standardized nutritional management approach by establishing standard units for nutritional support and employing a collaborative multidisciplinary collaborative nutritional treatment model. To achieve this, we devised the clinical working mode of “ward rounds and nutritional team system,” studied the effect of this working mode on feeding tolerance (FT), nutritional outcomes, and recovery of neural function of patients, and investigated the clinical efficacy and social significance of this working mode on the patients who have neurological disorders and are critically ill. Through these efforts, we expect that our study can serve as a model and give references for standardized nutritional treatment in other inpatient departments in our district, contribute to the implementation of nutritional treatment, and benefit patients.

2. Methods

2.1 Study participants

There were 84 participants in this study who were hospitalized in the neurological ICU of a hospital between June 2018 and December 2021. They were randomly assigned to the test and control group. Inclusion criteria: (1) Patients aged $\geqslant$ 18 years; (2) Patients who stayed in the neurological ICU for 72 hours or longer; (3) Patients who remained dependent on tube feeding for 72 hours or longer. Exclusion criteria: (1) Patients with comorbidities that adversely affect their digestive systems; (2) Patients with additional disease that had malignant prognosis or patients considered to have a survival period of less than 12 months (For instance, cancer patients at the advanced stage); (3) Patients with psychiatric disorders. The criteria for case dropout determination: (1) Death; (2) Patients that were released from the hospital contrary to our study plan; (3) Patients who withdrew voluntarily. The study was approved by the ethics committee of our hospital and all participants (or their families) signed the consent form.

2.2 Methods

The current study is a single-blind RCT of traditional nutritional support versus multidisciplinary collaborative nutritional treatment mode. Participants were randomised via a table of random number.

The control group: The control group completed nutritional risk screening and swallowing function screening within 24 hours after they were admitted to the hospital. Based on the screening results, we developed nutritional assistance regimens for them. The formulated nutritional assistance regimens were modified in real time based on weekly reevaluations of the nutritional risks of the patients, swallowing function, and nutritional status. When appropriate, a nutritional consultation was held, and we adjusted the nutritional plan of the patients depending on opinions from the consultation.

The test group: A standardized unit for nutritional support that worked in a standardized multidisciplinary collaborative mode was established for the neurological ICU. The nutritional support team comprised of members from the departments of neurology, nutrition, rehabilitation, and pharmacy, and all members had completed the periodic systematic training and examination related to this intervention. The form of collaboration between these team members was “ward rounds and nutritional team system.” Their division of labor was clear and well-defined, resulting in three working lines: nurses, physicians for patients with neurological disorders who are critically ill, and nutritionists. We also specified the routine of ward rounds. The entire nutritional support staff would convene at the bedside of each patient every Monday and Thursday, to inquire about and assess their nutritional status. They collaboratively carried out the nutritional support work in accordance with the guidelines “Five stages and six techniques.” The five stages are listed below. The first stage: nurses on duty performed nutritional risk screening and swallowing function screening (including the Kubota Water Swallowing Test and the volume-viscosity swallow test [V-VST]) within 24 hours of hospital admission to identify patients with high nutritional risks (NUTRIC score of $\geqslant$ 5) who required nutritional interventions. The on-duty nurses would initially direct these patients in accordance with doctors’ orders. The second stage: On the early stages of hospitalization (48–72 hours after hospitalization) of patients with high nutritional risks, full-time nurses for nutritional nursing care continued to perfect assessments of the nutritional conditions of these patients, including body mass index (BMI), FT, and laboratory tests (e.g. liver and kidney function, albumin, prealbumin, hemoglobin). The full-time nurses for nutritional nursing care, nutritionists, and neurologists would together make ward rounds to analyze the nutritional state of the patients and discuss and formulate individualized nutritional support programs (In the acute and early stages of critical illness, total energy targets are 20 $\sim$ 25 kcal $\cdot$ kg ${}^{-1}\cdot$ d^{- 1}, During the recovery stage, the total energy target are 25 $\sim$ 30 kcal $\cdot$ kg ${}^{-1}\cdot$ d^{- 1}, In obese patients, ideal body weight was used to calculate caloric requirements [25]).

The on-duty nurses were responsible for administering the tailored nutritional support plans. The third stage: Nutrition-specific nurses routinely re-evaluated the nutritional conditions and swallowing function of patients during their hospitalization, monitored the implementation of doctors’ advice on nutrition (including the attainment of targeted nutritional quantity and complications resulting from nutrition feeding), dynamically adjusted nutritional support plans based on the swallowing function and conditions of the patients, and educated the patients on nutritional knowledge. The fourth stage: At the time of the discharge of the patient from the hospital (24 hours before discharge), full-time nutrition-specific nurses would reassess the nutritional conditions of the patients and provide them with dietary advice. The fifth stage: Following discharge of the patients, full-time nutrition-specific nurses would continue to provide nutritional support to patients at home through online nursing service platforms. The six techniques are as follows: nutritional screening (nutritional screening within 24 hours after admission to the hospital and re-screening each week), nutritional assessment (nutritional assessment within 48–72 hours after admission to the hospital and re-assessment each week), nutritional guidance (nutritional guidance within 24 hours after admission to the hospital, within 48–72 hours after admission to the hospital, and 24 hours prior to discharge from hospital), and oral nutritional supplements (individualized administration throughout the entire hospitalization), enteral and parenteral nutrition (individualized administration throughout the entire hospitalization), and guidance on home nutrition (Fig. 1).

Figure 1.

Intervention group pathway.

2.3 Monitoring indicators

2.3.1 General data

General data included the gender, age, diagnosis, Glasgow Coma Scale (GCS), and acute gastrointestinal injury (AGI) grade at admission to the hospital of the patients.

2.3.2 Laboratory indicators

We obtained the values of prealbumin, hemoglobin, and albumin within 24 hours, after one week, and after two weeks after hospitalization of the patients. The quality assessment criteria for evaluating for all examinations were based on the pertinent regulations of the clinical laboratory center.

2.3.3 Assessment of nutritional risks

We used the nutritional risk assessment scale for patients who were critically ill, particularly the NUTRIC score, to undertake nutritional risk assessments on patients within 24 hours after hospital admission and 24 hours prior to discharge from hospital. Heylend et al. created the NUTRIC scale in 2011, which is useful for nutritional risk assessment of patients who are critically ill, unconscious, and bedridden in the ICU, and can compensate for the lack of conventional instruments for assessing nutritional risks [26]. The NUTRIC scale can be used to evaluate the age, severity of the disease, organ function, complications, and length of hospitalization prior to the admission of the patient to the ICU. The overall points on the NUTRIC scale ranges from 0–9 points, with points of 0 to 4 suggesting minimal nutritional hazards and points 5 to 9 indicating high nutritional risks.

2.4 Quality control

All terms used in this study were defined precisely. All members of the standardized unit for nutritional support completed training and examinations to ensure the data collection was standardized and uniform and to ensure data reliability. We conducted a preliminary survey to comprehensively identify any issues and to resolve them before conducting the main survey. Two people were responsible for entering the collected data. In the event of a dispute, the data would be evaluated. Items missing more than 20% of their content were excluded.

2.5 Statistical analysis

SPSS version 23.0 was used for statistical analysis. Enumeration data are expressed as frequency (percentage) and measurement data are expressed as mean (standard deviation) or median (interquartile range) based on their properties. The $t$ -test and analysis of variance were used for inter-group comparisons. All tests were two-sided tests, and $P<$ 0.05 indicated a statistically significant difference.

3. Results

3.1 General information

In this study, there were 84 participants of which 50 (59.52%) were male and 34 (40.48%) were female. General information was comparable between the test and control groups, but there was no statistically significant difference between the two groups (Table 1).

Table 1
Comparison of baseline data between the two groups ( $n=$ 84)

Group	Case number	Gender (case)		Age	BMI	Diagnosis			AGI grading
		Male	Female			Cerebral infarction	Cerebral hemorrhage	Subarachnoid hemorrhage	Grade 1	Grade 2
Control group	41	28	13	65.56 $\pm$ 10.81	24.74 $\pm$ 3.15	21	16	4	31	10
Test group	43	22	21	67.19 $\pm$ 8.69	24.10 $\pm$ 3.11	18	16	9	30	13
Statistical measure		2.56		$-$ 0.761	0.939	2.107			0.36
$P$ value		0.110		0.449	0.351	0.349			0.55

Table 2

Comparisons of the NUTRIC, GCS, and tolerance scores

Group	NUTRIC scores				GCS scores				Tolerance scores (case/(%))
	Before intervention	After intervention	$t$	$p$	Admission to the hospital	Discharge from hospital	$t$	$p$	Gastroparesis	Diarrhea
Control group	4.61 $\pm$ 1.14	4.54 $\pm$ 1.07	1.78	0.08	10.05 $\pm$ 2.13	12.73 $\pm$ 1.90	$-$ 7.74	0.000	13 (31.71%)	11 (26.83)
Test group	4.58 $\pm$ 1.01	3.72 $\pm$ 0.67	7.95	0.00	9.90 $\pm$ 2.01	12.60 $\pm$ 2.82	$-$ 6.500	0.000	3 (6.98)	4 (9.30)
Statistical measure	0.12	4.16			0.31	0.24			8.33	4.40
$P$ value	0.90	0.000			0.75	0.81			0.004	0.036

3.2 Comparisons in the NUTRIC score, GCS score, and tolerance between the test and control groups

As shown in Table 2, after the intervention, the test group had a significantly lower NUTRIC score than the control group ( $t=$ 4.16, $P<$ 0.001), with a statistically significant difference. The test group had significantly lower NUTRIC scores after intervention, than before intervention ( $t=$ 7.95, $P<$ 0.001). The incidence of gastroparesis and diarrhea in the test group was significantly lower compared to the control group, with statistically significant differences.

3.3 Comparisons in prealbumin between the test and control groups before intervention, after intervention for one week, and after intervention for two weeks

All data had a normal distribution when tested by the Shapiro-Wilk test. The Mauchly’s $W=$ 0.88 in the sphericity test and significance $P=$ 0.006 did not comply with the sphericity test. Therefore, we used the results that were corrected using one-way ANOVA (analysis of variance) “Greenhouse-Geisser” as the final results. The results of variance analysis of repeated measurements are as follows: Main effect of the grouping was not significant, with $F=$ 2.79, $P=$ 0.10, and partial $\eta^{2}=$ 0.03; main effect of the measurement frequency was significant, with $F=$ 13.91, $P<$ 0.001, and partial $\eta^{2}=$ 0.15; the interaction effect between the measurement frequency and grouping was significant, with $F=$ 19, $P<$ 0.001, and partial $\eta^{2}=$ 0.19. The test results of the simple effect of the grouping are as below: Before intervention, the simple effect of the grouping was not significant, with $F=$ 2.65, $P=$ 0.11, and partial $\eta^{2}=$ 0.03; after intervention for one week, the simple effect of the grouping was significant, with $F=$ 4.64, $P=$ 0.03, and partial $\eta^{2}=$ 0.05; after intervention for two weeks, the simple effect of the grouping was significant, with $F=$ 11.22, $P=$ 0.001, and partial $\eta^{2}=$ 0.12. The results of the simple effect of measurement frequency are listed below: In the control group, the simple effect of measurement frequency was not significant, with $F=$ 0.911, $P=$ 0.41, and partial $\eta^{2}=$ 0.02; in the test group, the simple effect of measurement frequency was significant, with $F=$ 25.29, $P<$ 0.001, and partial $\eta^{2}=$ 0.38. The findings based on multiple comparisons are as follows: In the test group, the value of prealbumin progressively increased before intervention, after intervention for one week, and after intervention for two weeks, and the increases between each two adjacent stages of the three stages were significant ( $P<$ 0.001); in the control group, there was no significant difference between each two adjacent stages of the three stages, namely before intervention, after intervention for one week, and after intervention for two weeks (Table 3).

Table 3
Comparisons of prealbumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention

Group	Before intervention	One week	Two weeks	$F$ test for repeated measurements
	M $\pm$ SD	M $\pm$ SD	M $\pm$ SD	$F$	$P$	Partial $\eta^{2}$
Control group	198.74 $\pm$ 60.70	188.70 $\pm$ 77.33	195.34 $\pm$ 76.26
Test group	179.42 $\pm$ 47.64	221.30 $\pm$ 60.68	242.77 $\pm$ 51.78
Grouping effect				2.79	0.10	0.03
Main effect of test frequency				13.91	0.000	0.15
Group *Test frequency				19.00	0.000	0.19

3.4 Comparisons in hemoglobin between the test and control groups before intervention, after intervention for one week, and after intervention for two weeks

The Mauchly’s $W=$ 0.59 in the sphericity test and significance $P<$ 0.001, which did not conform to the sphericity test. We took the results that were corrected using one-way ANOVA “Greenhouse-Geisser” as the results. The results of variance analysis of repeated measurements are as follows: Main effect of the grouping was not significant, with $F=$ 0.15, $P=$ 0.70, and partial $\eta^{2}=$ 0.002; main effect of the measurement frequency was not significant, with $F=$ 1.07, $P=$ 0.33, and partial $\eta^{2}=$ 0.013; interaction effect between the measurement frequency and grouping was not significant, with $F=$ 2.43, $P=$ 0.11, and partial $\eta^{2}=$ 0.029 (Table 4).

Table 4
Comparison of hemoglobin levels between the two groups prior to intervention, one week after intervention, and two weeks after intervention

Group	Before intervention	One week	Two weeks	$F$ test for repeated measurements
	M $\pm$ SD	M $\pm$ SD	M $\pm$ SD	$F$	$P$	Partial $\eta^{2}$
Control group	131.24 $\pm$ 24.52	122.44 $\pm$ 17.91	124.39 $\pm$ 16.86
Test group	126.79 $\pm$ 17.33	131.58 $\pm$ 45.25	124.51 $\pm$ 16.51
Grouping effect				0.15	0.70	0.002
Main effect of test frequency				1.07	0.33	0.013
Group *Test frequency				2.43	0.11	0.029

3.5 Comparisons in hemoglobin between the test and control groups before intervention, after intervention for one week, and after intervention for two weeks

The test results of simple effect of the grouping are listed below: Before intervention, the simple effect of the grouping was not significant, with $F=$ 3.85, $P=$ 0.05, and partial $\eta^{2}=$ 0.045; after intervention for one week, the simple effect of the grouping was significant, with $F=$ 4.12, $P=$ 0.046, and partial $\eta^{2}=$ 0.048; after intervention for two weeks, the simple effect of the grouping was significant, with $F=$ 5.64, $P=$ 0.02, and partial $\eta^{2}=$ 0.064. The results of the simple effect of measurement frequency are as follows: In the control group, the simple effect of measurement frequency was significant, with $F=$ 188.37, $P<$ 0.001, and partial $\eta^{2}=$ 0.822; in the test group, the simple effect of measurement frequency was significant, with $F=$ 197.57, $P<$ 0.001, and partial $\eta^{2}=$ 0.83. Findings based on multiple comparisons are as follows: In the test group, the differences in the albumin were significant between the two adjacent stages of the three stages, namely before intervention, after intervention for one week, and after intervention for two weeks ( $P<$ 0.001), as was the case in the control group ( $P$ 0.001) (Table 5).

Table 5
Comparison of albumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention

Group	Before intervention	One week	Two weeks	$F$ test for repeated measurements
	M $\pm$ SD	M $\pm$ SD	M $\pm$ SD	$F$	$P$	Partial $\eta^{2}$
Control group	40.65 $\pm$ 2.42	31.85 $\pm$ 2.39	34.05 $\pm$ 1.90
Test group	39.43 $\pm$ 3.21	30.83 $\pm$ 2.23	35.18 $\pm$ 2.39
Grouping effect				0.77	0.38	0.009
Main effect of test frequency				478.63	$<$ 0.001	0.85
Group *Test frequency				10.49	$<$ 0.001	0.11

4. Discussion

Clinical nutritional support work for patients with neurological disorders who are critically ill should include, screening of nutritional status, laboratory tests, nutritional assessments, nutritional diagnosis, nutritional support, and curative efficacy [7]. Clinical nutritional support work involves dynamic and consecutive management and cannot be completed by a single person. Therefore, multidisciplinary collaboration is needed to provide precise and individualized nutritional support. In nutritional management, nurses implement the nutritional support plans, observe the efficacy of nutritional support, and provide recommendations for nutritional support plans. Therefore, nurses serve as the leader of the interdisciplinary collaborative nutrition treatment model constructed in this study. This interdisciplinary collaborative nutrition treatment model has demonstrated significant benefits in the management of neonates [27], elderly patients who visited hospital emergency departments [28], and patients with short bowel syndrome or coronary heart disease [29, 30]. The multidisciplinary collaborative model introduced in our study, led by nurses and involving the participation of doctors, nurses, nutritionists, rehabilitation therapists, and pharmacists, is a management model that is systematic, standardized, and personalized. Guidance and evidence from various departments are integrated through multidisciplinary structural ward rounds twice a week, structured communication for addressing diarrhea, malnutrition, and neurological function impairment in patients with neurological disorders who are critically ill, and comprehensive assessments of patient condition. The interdisciplinary collaboration model yielded accurate and efficient communication among team members and attained fruitful achievements in reducing diarrhea and gastroparesis, and in improving nutritional indicator values.

The requirement for nutritional management is the evaluation of the nutritional status and risks of the patient. As per the American Society for Parenteral and Enteral Nutrition (ASPEN), nutritional risk assessment (such as the NRS 2002 [nutrition risk screening] and the NUTRIC score) should be performed on all patients who are critically ill and are at risk of inadequate self-support nutrition intake [31]. Notably, only the NUTRIC was developed and validated among critically ill patients [32, 33]. Hsu et al. reported that most older ICU patients had high NUTRIC score (91.1%) [34].

Compared to NRS 2002, the NUTRIC scale can more accurately reflect the association between nutritional interventions and clinical outcomes and has a higher specificity for patients with neurological disorders who are critically ill. As described in the literature, NUTRIC is able to accurately predict the probability of death and can thus provide references for personalized assessment of patient conditions, treatment options, and improving prognosis [35]. According to a study by Liang et al., who evaluated the nutritional status of patients and formulated nutritional support plans, the test group that received nutritional support had a significantly lower NUTRIC score than the control group ( $P<$ 0.05, $P<$ 0.01), which is consistent with our findings [36]. In our study, after intervention, the NUTRIC score in the test group was significantly lower than in the control group ( $t=$ 4.16, $P<$ 0.001), indicating that the nutritional support mechanism established in our study significantly decreased the nutritional risks of the patients and positively improved their prognosis. Based on comparisons of the GCS score between the test and control groups, we found that the GCS score was significantly higher during discharge from hospital than during admission at hospital, with a statistically significant difference, and that the GCS score of prealbumin values at discharge was positively correlated with that after intervention for 2 weeks (0.289, $P<$ 0.05), indicating nutritional intervention is able to increase prealbumin levels in patients and promote recovery of their neurological functions. However, there was no statistically significant difference in the GCS score between the control and test groups at discharge, and there was no correlation between GCS scores and the selection of nutritional plans. Furthermore, our intervention strategy did not demonstrate significant benefits in enhancing neurological function of the patients before discharge. Therefore, a bigger sample size should be used to investigate how our intervention technique can improve the neurological function of patients.

There is currently no Class I evidence on the timing of nutritional therapy because ethical issues. However, some research suggests that early nutrition may lead to better outcomes. A Cochrane review suggests that early feeding may reduce the risk of infection and improve patient outcome [37]. Li et al. demonstrated that early enteral nutrition for patients in the ICU is correlated with less wound infection, lower mortality, and shorter hospital stay [38]. Chourdakis et al. found that early enteral feeding within 24 to 48 hours may improve the hormonal profile to attenuate the catabolic response [39]. However, Herbert et al. proposed that early enteral nutrition produced no statistical difference in the mortality rate of patients [40]. Dorken Gallastegi et al.’s study also showed no difference in 28-day mortality between early enteral nutrition and late enteral nutrition in critically ill patients receiving vasopressor support [41]. Nutritional treatment of severe patients is very complicated and affected by many factors. Most studies have reported the benefits of early nutritional therapy, but no adverse effects have been reported. Furthermore, the American Society for Parenteral and Enteral Nutrition-Society of Critical Care Medicine (ASPEN-SCCM) and The European Society for Clinical Nutrition and Metabolism (ESPEN) guidelines also recommend initiating early enteral nutrition (EN) within 24–48 hours in critically ill patients if oral intake is not possible [42, 43]. As a consequence, the timing of nutritional intervention in the experimental group was determined to be within 24 hours after admission. According to studies, 30% of patients who are critically ill in neurosurgery units experience varied degrees of gastrointestinal intolerance despite receiving standard enteral nutrition intervention [44]. Multiple studies have shown that increasing the head of the bed 30–45 degrees, continuous infusion of EN, use of concentrated enteral formulas ( $\geqslant$ 1.5 kcal/mL) and promotility agents may be considered to improve feeding tolerance [45, 46]. In our study, the incidence of feeding intolerance (FI) in the test group was 16.28%, which is much less than the 30.0% reported in the aforementioned literature. This pertains to the individualized start-up timing and speed-adjustment strategy for enteral nutrition in the test group.

Patients with neurological disorders who are critically ill experience severe catabolism after hospitalization, resulting in acute deterioration of their nutritional status. Patients may lose 10–25% of their total protein stores within 10 days of their hospitalization [47]. Prealbumin, albumin, and hemoglobin are often employed as clinical markers for monitoring the nutrition risk of patients. The degree to which these three markers diminish shows the severity of the condition. Without a timely correction of the three indicators, the prognosis of the patients will be deemed poor. Ding, found that the albumin and prealbumin levels in patients were both below the lower limit of the standard range [48]. We also discovered that the prealbumin and albumin levels of patients were lower one week after admission than they were upon admission. Therefore, it is crucial to establish effective and standardized nutritional support strategies at early stages. Prealbumin has a short half-life (about 1.9 days) and is unaffected by the administration of exogenous human albumin to the body. The level of albumin is also influenced by the underlying disease and the severity of the disease [49, 50]. In addition, changes in blood volume have a substantial effect on albumin levels; dehydration, blood volume expansion (namely, to use clinical medical approaches to increase blood volume to cope with shock), and other variables can all cause albumin levels to fluctuate [51]. The statistically significant difference in albumin levels between the control group before intervention, after intervention for one week, and after intervention for two weeks in our study may relate to the aforementioned fact that albumin levels are easily influenced by the aforementioned factors.

Cui found that after receiving enteral nutritional treatment for one or two weeks, the test group exhibited statistically significant differences ( $P<$ 0.05) in prealbumin and albumin levels compared to the control group [52], which is similar to the results of our study where we found that the multidisciplinary collaborative nutritional treatment approach was highly effective at increasing the levels of prealbumin and albumin. According to the research conducted by Xu on the nutritional status of patients who were critically ill in ICU, after 7 days of nutritional support, there is a statistically significant difference in prealbumin values, but no statistically significant difference in hemoglobin or albumin [53]. In contrast, in our study, there was no effect on hemoglobin by nutritional intervention, however there was a statistically significant difference in both the albumin and prealbumin values after nutritional intervention. Reasons for the differences in analysis include: 1) The subjects of study, the nutritional treatment strategies developed and the time points of measurement of nutritional indicators were different; 2) Serum albumin and prealbumin are not direct causal relationship between malnutrition. Evans et al. pointed out that the serum concentrations of albumin and prealbumin decline in the presence of inflammation, and the presence of inflammation can lead to malnutrition or risk of malnutrition. Serum albumin and prealbumin declines must be recognized as inflammatory markers associated with “nutrition risk” in the context of nutrition assessment rather than with malnutrition per se [54]. We also found that, even though there were considerable improvements in prealbumin and albumin values during the second week of nutritional support, both values were significantly lower than they were at admission. Han reported that patients with cerebrovascular disorders who were critically ill significantly differed from other patients who are critically ill [55]. Patients with cerebrovascular disorders who are critically ill have an approximately normal nutritional status at the onset of the disease in most cases, but their nutrition levels decline in the acute phase due to variables such as the stress response and infection caused by the disease. Despite active enteral nutritional support, nutritional intake cannot be effectively compensated for in these patients.

5. Conclusion

Multidisciplinary collaboration based on a standardized unit for nutritional support can minimize the incidence of diarrhea and gastroparesis, increase the levels of prealbumin and albumin, leading to a better prognosis, and is thus suitable for promotion in clinical practice. The drawbacks of our study are that we only included a small number of nutritional indicators and did not include clinical outcomes as observation indicators. Supplementary research on this aspect needs to be conducted in the future.

Funding

No external funding was received to conduct this study.

Availability of data and materials

All data generated or analysed during this study are included in this article. Further enquiries can be directed to the corresponding author.

Footnotes

Acknowledgments

The authors would like to acknowledge the hard and dedicated work of all staff that implemented the intervention and evaluation components of the study.

Conflict of interest

The authors declare that they have no competing interests.

References

Wang

Peng

Zhang

, et al. Brief report on stroke prevention and treatment in China, 2020. Chinese Journal of Cerebrovascular Diseases. 2022; 19(02): 136-144.

Robinson

Davis

Dinglas

, et al. A systematic review finds limited data on measurement properties of instruments measuring outcomes in adult intensive care unit survivors. J Clin Epidemiol. 2017; 82: 37-46.

Kurtz

Rocha

EEM

. Nutrition therapy, glucose control, and brain metabolism in traumatic brain injury: A multimodal monitoring approach. Front Neurosci. 2020; 14: 190.

Heyland

Ortiz

Stoppe

Patel

Yeh

Dukes

Chen

Almansa

Day

. Incidence, risk factors, and clinical consequence of enteral feeding intolerance in the mechanically ventilated critically ill: An analysis of a multicenter, multiyear database. Crit Care Med. 2021; 49(1): 49-59. doi: 10.1097/CCM.0000000000004712. PMID: 33148950.

Yamamoto

Allen

Jones

, et al. Meeting calorie and protein needs in the critical care unit: A prospective observational pilot study. Nutr Metab Insights. 2020; 13: 1178638820905992.

Jeong

Kim

Choo

, et al. Nutrition therapy for patients with traumatic brain injury: A narrative review. Korean Journal of Neurotrauma. 2023; 19(2): 177.

Pan

Pen

, et al. Chinese expert consensus on enteral nutrition support for neurological diseases (the second version). Chinese Journal of Clinical Nutrition. 2019; 27(4): 193-203.

Barr

Hecht

Flavin

Khorana

Gould

. Outcomes in critically ill patients before and after the implementation of an evidence-based nutritional management protocol. Chest. 2004; 125(4): 1446-57.

Lambell

Tatucu-Babet

Chapple

, et al. Nutrition therapy in critical illness: A review of the literature for clinicians. Critical Care. 2020; 24: 1-11.

10.

Malekahmadi

Shadnoush

Islam

SMS

, et al. The effect of French maritime pine bark extract supplementation on inflammation, nutritional and clinical status in critically ill patients with traumatic brain injury: A randomized controlled trial. Phytother Res. 2021; 35(9): 5178-5188. doi: 10.1002/ptr.7187.

11.

Nattagh-Eshtivani

Pahlavani

Ranjbar

Gholizadeh Navashenaq

Salehi-Sahlabadi

Mahmudiono

Nader Shalaby

Jokar

Nematy

Barghchi

Havakhah

Maddahi

Rashidmayvan

Khosravi

. Does propolis have any effect on rheumatoid arthritis? A review study. Food Sci Nutr. 2022 Mar 10; 10(4): 1003-1020. doi: 10.1002/fsn3.2684.

12.

Pahlavani

Sedaghat

Bagheri Moghaddam

, et al. Effects of propolis and melatonin on oxidative stress, inflammation, and clinical status in patients with primary sepsis: Study protocol and review on previous studies. Clin Nutr ESPEN. 2019; 33: 125-131. doi: 10.1016/j.clnesp.2019.06.007.

13.

Firouzi

Pahlavani

Navashenaq

Clayton

Beigmohammadi

Malekahmadi

. The effect of Vitamin C and Zn supplementation on the immune system and clinical outcomes in COVID-19 patients. Clin Nutr Open Sci. 2022 Aug; 44: 144-154. doi: 10.1016/j.nutos.2022.06.006.

14.

Mehta

Skillman

Irving

, et al. Guidelines for the provision and assessment of nutrition support therapy in the pediatric critically ill patient: Society of critical care medicine and american society for parenteral and enteral nutrition. JPEN J Parenter Enteral Nutr. 2017; 41(5): 706-742. doi: 10.1177/0148607117711387.

15.

Quintard

Ichai

. Nutritional and metabolic supplementation for the injured brain: An update. Curr Opin Crit Care. 2019; 25(2): 126-131. doi: 10.1097/MCC.0000000000000588.

16.

Movahed

Toussi

Pahlavani

, et al. Effects of medical nutrition therapy compared with general nutritional advice on nutritional status and nutrition-related complications in esophageal cancer patients receiving concurrent chemoradiation: A randomized controlled trial. Mediterranean Journal of Nutrition And Metabolism. 2020; 13(3): 265-276. doi: 10.3233/MNM-200424.

17.

Shi

Pang

. Effect analysis of standardized nutrition training in medical staff and inpatients. Chinese Journal of Clinical Nutrition. 2018; 26(3): 2. doi: 10.3760/cma.j.issn.1674-635X.2018.03.010.

18.

Ameri

Vafaee

Sadeghi

, et al. Effect of a comprehensive total parenteral nutrition training program on knowledge and practice of nurses in NICU. Glob J Health Sci. 2016 Oct 1; 8(10): 56478. doi: 10.5539/gjhs.v8n10p135. PMID: 27302458.

19.

Gong

Wang

. Reduce feeding interruption during nursing operation and improve early enteral nutrition standards. Parenteral & Enteral Nutrition. 2019; 26(01): 6-7. doi: CNKI:SUN:CWCN0.2019-01-002.

20.

Shim

Yon

, et al. Effectiveness of a multidisciplinary team for nutrition support in a trauma intensive care unit. Acute Crit Care. 2020; 35(3): 142-148.

21.

Lopes

MCBR

Ceniccola

Araújo

WMC

, et al. Nutrition support team activities can improve enteral nutrition administration in intensive care units. Nutrition (Burbank, Los Angeles County, Calif.). 2019; 57: 275-281.

22.

Reber

Strahm

Bally

, et al. Efficacy and efficiency of nutritional support teams. Journal of Clinical Medicine. 2019; 8(9): 1281.

23.

DeLegge

Kelly

. State of nutrition support teams. Nutrition in Clinical Practice: Official Publication of the American Society for Parenteral and Enteral Nutrition. 2013; 28(6): 691-697.

24.

Yang

Wang

Yang

et al. Study on clinical nutrition quality service units via SWOT analysis. Chinese Journal of General Practice. 2021; 19(08): 1395-1397.

25.

Singer

Blaser

Berger

, et al. ESPEN guideline on clinical nutrition in the intensive care unit. Clin Nutr. 2019; 38(1): 48-79. doi: 10.1016/j.clnu.2018.08.037. Epub 2018 Sep 29. PMID: 30348463.

26.

Heyland

Dhaliwal

Jiang

, et al. Identifying critically ill patients who benefit the most from nutrition therapy: The development and initial validation of a novel risk assessment tool. Crit Care. 2011; 15(6): 268-270.

27.

Jeong

Jung

Shin

, et al. The successful accomplishment of nutritional and clinical outcomes via the implementation of a multidisciplinary nutrition support team in the neonatal intensive care unit. Bmc Pediatrics. 2016; 16(1): 113.

28.

Suzuki

Furuya

Nakagawa

, et al. Changes in nutrition-intake method and oral health through a multidisciplinary team approach in malnourished older patients admitted to an acute care hospital. Int J Environ Res Public Health. 2022; 19(16): 9784.

29.

Yeh

Vasileiou

Mulder

, et al. Severe Short Bowel Syndrome: Prognosis for Nutritional Independence Through Management by a Multidisciplinary Nutrition Service and Surgery. Am Surg. 2022 Apr 23: 31348221087901. doi: 10.1177/00031348221087901. Epub ahead of print. PMID: 35465680.

30.

Jiang

Zhang

Yan

, et al. Effectiveness of a nurse-led multidisciplinary self-management program for patients with coronary heart disease in communities: A randomized controlled trial. Patient Educ Couns. 2020; 103(4): 854-863.

31.

Mcclave

Martindale

Vanek

, 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 (AS.P.E.N.). Journal of Parenteral and Enteral Nutrition. 2009; 33(3): 277-316.

32.

Heyland

Dhaliwal

Jiang

, et al. Identifying critically ill patients who benefit the most from nutrition therapy: The development and initial validation of a novel risk assessment tool. Crit care. 2011; 15: R268.

33.

Rahman

Hasan

Agarwala

, et al. Identifying critically-ill patients who will benefit most from nutritional therapy: Further validation of the “modified NUTRIC” nutritional risk assessment tool. Clin Nutr. 2016; 35: 158-62.

34.

Hsu

Lee

Kuo

, et al. Higher energy and protein intake from enteral nutrition may reduce hospital mortality in mechanically ventilated critically ill elderly patients. Int J Gerontol. 2018; 12: 285-9.

35.

Lin

Yen

Lam

, et al. Use of modified-NUTRIC score to assess nutritional risk in surgical intensive care unit. Journal of the Chinese Medical Association. 2021; 84(9): 860-864.

36.

Liang

Zhang

Jiang

. Application of systematic nutritional assessment to guide nutritional support care in ICU patients with severe illness. Journal of Qilu Nursing. 2021; 27(19): 39-42.

37.

Perel

Yanagawa

Bunn

Roberts

Wentz

Pierro

. Nutritional support for head-injured patients. Cochrane Database Syst Rev. 2006; 2006(4): CD001530. Published 2006 Oct 18. doi: 10.1002/14651858.CD001530.pub2.

38.

Wang

Fang

, et al. Effect of early enteral nutrition on outcomes of trauma patients requiring intensive care. Chinese Journal of Traumatology. 2020; 23(03): 163-167.

39.

Chourdakis

Kraus

Tzellos

Sardeli

Peftoulidou

Vassilakos

Kouvelas

. Effect of early compared with delayed enteral nutrition on endocrine function in patients with traumatic brain injury: An open-labeled randomized trial. JPEN J Parenter Enteral Nutr. 2012; 36(1): 108-16.

40.

Herbert

Perry

Andersen

, et al. Early enteral nutrition within 48 hours of lower gastrointestinal surgery versus later commencement for length of hospital stay and postoperative complications. Cochrane Database Syst Rev. 2019; 22: CD004080.

41.

Dorken Gallastegi

Gebran

Gaitanidis

Naar

Hwabejire

Parks

Lee

Kaafarani

HMA

Velmahos

Mendoza

. Early versus late enteral nutrition in critically ill patients receiving vasopressor support. JPEN J Parenter Enteral Nutr. 2022; 46(1): 130-140.

42.

McClave

Taylor

Martindale

, 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 (AS.P.E.N.). JPEN J Parenter Enteral Nutr. 2016 Feb; 40(2): 159-211.

43.

Singer

Blaser

Berger

, et al. ESPEN guideline on clinical nutrition in the intensive care unit. Clin Nutr. 2019; 38(1): 48-79.

44.

Gungabissoon

Hacquoil

Bains

, et al. Prevalence, risk factors, clinical consequences, and treatment of enteral feed intolerance during critical illness. Jpen Journal of Parenteral & Enteral Nutrition. 2015; 39(4): 441-488.

45.

Kattelmann

Hise

Russell

Charney

Stokes

Compher

. Preliminary evidence for a medical nutrition therapy protocol: Enteral feedings for critically ill patients. J Am Diet Assoc. 2006; 106(8): 1226-41.

46.

Mazaherpur

Khatony

Abdi

Pasdar

Najafi

. The effect of continuous enteral nutrition on nutrition indices, compared to the intermittent and combination enteral nutrition in traumatic brain injury patients. J Clin Diagn Res. 2016; 10(10): JC01-JC05.

47.

Koekkoek

. Nutrition in the critically ill patient. Current Opinion in Anaesthesiology. 2017; 30(2): 178-185.

48.

Ding

. Effects of probiotics on gastrointestinal function, nutrition and inflammatory status in critically ill patients receiving enteral nutrition support. Anhui Medical University. 2019. doi: 10.26921/d.cnki.ganyu.2019.000149.

49.

Puthucheary

Rawal

McPhail

, et al. Acute skeletal muscle wasting in critical illness. JAMA. 2013; 310(15): 1591-1600.

50.

Roberfroid

. Dietary fiber, inulin, and oligofructose: A review comparing their physiological effects. Crit Rev Food Sci Nutr. 1993; 33(2): 103-148.

51.

Arnaud-Battandier

Malvy

Jeandel

, et al. Use of oral supplements in malnourished elderly patients living in the community: A pharmaco-economic study. Clin Nutr. 2004; 23(5): 1096-103. doi: 10.1016/j.clnu.2004.02.007. PMID: 15380901.

52.

Cui

. Clinical analysis of nutritional support therapy with enteral nutrient solution in critically ill patients. Jilin Medical Journal. 2022; 43(07): 1937-1939.

53.

. Investigation on nutritional support and nursing status of critically ill patients in ICU. Huzhou University. 2020. doi: 10.27946/d.cnki.ghzsf.2020.000032.

54.

Evans

Corkins

Malone

, et al. The use of visceral proteins as nutrition markers: An ASPEN position paper. Nutrition in Clinical Practice. 2021; 36(1): 22-28.

55.

Han

. Role of soluble dietary fiber in nutritional support of patients with severe cerebrovascular disease. Jilin University. 2020. doi: 10.27162/d.cnki.gjlin.2020.004348.

Impact of a multidisciplinary collaborative nutritional treatment model in patients who are critically ill with neurological disorders: A randomized controlled trial

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Study participants

2.2 Methods

2.3.1 General data

2.3.2 Laboratory indicators

2.3.3 Assessment of nutritional risks

2.4 Quality control

2.5 Statistical analysis

3. Results

3.1 General information

Table 1 Comparison of baseline data between the two groups ( n = 84)

3.3 Comparisons in prealbumin between the test and control groups before intervention, after intervention for one week, and after intervention for two weeks

Table 3 Comparisons of prealbumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention

Table 4 Comparison of hemoglobin levels between the two groups prior to intervention, one week after intervention, and two weeks after intervention

Table 5 Comparison of albumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention

5. Conclusion

Funding

Availability of data and materials

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Comparison of baseline data between the two groups ( $n=$ 84)

Table 3
Comparisons of prealbumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention

Table 4
Comparison of hemoglobin levels between the two groups prior to intervention, one week after intervention, and two weeks after intervention

Table 5
Comparison of albumin levels between the two groups before intervention, one week after intervention, and two weeks after intervention