![Meta-analysis of ENI incidence in patients with acute pancreatitis. Abbreviations: DL: DerSimonian and Laird; RR, rate ratio; CI, confidence interval [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46].](https://www.researchgate.net/publication/389619010/figure/fig2/AS:11431281315503238@1741839422329/Meta-analysis-of-ENI-incidence-in-patients-with-acute-pancreatitis-Abbreviations-DL_Q320.jpg)
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Received: 12 February 2025
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Published: 5 March 2025
Citation: Xiao, W.; Zeng, Y.; Ai, L.;
Wang,G.; Fu, Y. Clinical Predictors and
Prevalence of Enteral Nutrition
Intolerance in Acute Pancreatitis: An
Updated Systematic Review and
Meta-Analysis. Nutrients 2025,17, 910.
https://doi.org/10.3390/nu17050910
Copyright: © 2025 by the authors.
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Systematic Review
Clinical Predictors and Prevalence of Enteral Nutrition
Intolerance in Acute Pancreatitis: An Updated Systematic
Review and Meta-Analysis
Wei Xiao 1,2, Yue Zeng 2,3, Lianzhong Ai 1, Guangqiang Wang 1 ,* and Yang Fu 2,3,*
1School of Health Science and Engineering, Shanghai Engineering Research Center of Food Microbiology,
University of Shanghai for Science and Technology, Shanghai 200093, China;
xiao18678170427@163.com (W.X.); ailianzhong1@126.com (L.A.)
2Shanghai Key Laboratory of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine,
Shanghai 201620, China; carrie_1004@sjtu.edu.cn
3Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of
Medicine, Shanghai 201620, China
*Correspondence: yzfuyang3341@163.com (Y.F.); 1015wanggq@163.com (G.W.)
Abstract: Background: Acute pancreatitis (AP) leads to severe inflammation and nutri-
tional deficits, with 80% of severe cases experiencing critical protein loss. Timely enteral
nutrition is essential for recovery. This study systematically reviews and analyzes the inci-
dence and predictors of enteral nutrition intolerance (ENI) in AP patients. Methods: Web of
Science, Embase, Cochrane Library, and PubMed were searched up to May 2024. Studies re-
porting on ENI incidence and predictors in AP patients were included based on predefined
criteria. Bias was assessed using standardized tools, and meta-analyses provided summary
estimates with confidence intervals. Results: From the 2697 screened studies, 28 involving
4853 patients met the inclusion criteria. The pooled incidence of ENI was 26%. Signifi-
cant predictors included comorbid diabetes, pancreatic necrosis, elevated pre-refeeding
serum lipase levels, peri-pancreatic fluid collections, and systemic inflammatory response
syndrome at admission. Higher ENI rates were observed in Europe, among patients with
severe acute pancreatitis (SAP), those receiving nasoenteric feeding, and in prospective
study cohorts. Conclusions: ENI affects approximately one-quarter of AP patients and
is not significantly associated with age, sex, or the cause of AP. Its incidence varies by
region, disease severity, feeding method and study design. Identifying predictors, such as
comorbid diabetes and pancreatic necrosis, may help clinicians reduce the risk of ENI. The
limitations of this study include the heterogeneity of the included studies and inconsistent
ENI diagnostic criteria.
Keywords: acute pancreatitis; enteral nutrition intolerance; predictive factors; prevalence;
meta-analysis
1. Introduction
Acute pancreatitis (AP) is a prevalent and serious condition of the digestive system,
with a global incidence rate of approximately 34 cases per 100,000 individuals [
1
]. Severe
inflammation in AP leads to high catabolic metabolism and increased nutritional needs [
2
].
Approximately 80% of severe AP (SAP) patients experience substantial nutritional deficien-
cies, including nitrogen losses of 20 to 40 g/d [
3
]. Therefore, nutritional support is critical
for the management of patients with AP.
Nutrients 2025,17, 910 https://doi.org/10.3390/nu17050910
Nutrients 2025,17, 910 2 of 14
Guidelines from the American Gastroenterological Association and the European
Society for Clinical Nutrition and Metabolism recommend early initiation of enteral nutri-
tion following admission [
4
–
6
]. Research suggests that, compared to parenteral nutrition,
enteral nutrition not only provides effective nutritional support, but also enhances gastroin-
testinal function. It significantly reduces the incidence of infection-related complications,
organ failure, the need for surgical interventions, and improves glycemic control [7,8].
Variations in patient conditions, such as underlying gastrointestinal dysmotility or
elevated inflammatory responses, often induce enteral nutrition intolerance (ENI) in AP [
9
].
Clinically, this intolerance manifests as nausea, vomiting, abdominal distension, and pain,
which can lead to prolonged hospital stays, increased healthcare utilization, and decreased
quality of life [10].
The clinical significance of studying the incidence and potential risks of ENI in AP
lies in its ability to improve risk stratification and optimize nutritional interventions [
11
].
Early identification of feeding intolerance symptoms and risk factors may allow clinicians
to optimize individualized patient management, including nutritional therapy, to prevent
or reduce feeding intolerance, prevent disease progression and complications, shorten
hospital stays, and alleviate the economic burden associated with acute pancreatitis.
The objective of this meta-analysis was to systematically review the incidence of ENI
in AP, assess the impact of confounding factors, and identify the key predictors of ENI.
This study seeks to enhance the understanding of ENI, providing insights to guide clinical
practice and improve nutritional support management for patients with AP.
2. Method
2.1. Registration and Protocol
The protocol was registered in the International Prospective Register of Systematic
Reviews under registration number CRD42024539304, and was performed in accordance
with the PRISMA 2020 checklist [12].
2.2. Search Strategy
Comprehensive electronic searches were conducted in databases, including Web of
Science, Embase, Cochrane Library, and PubMed, covering all publications in English
through to 10 May 2024. Our search strategy involved the use of Medical Subject Headings
(MeSH), including “Enteral Nutrition”, “Intolerance”, and “Pancreatitis”, complemented
by corresponding keywords in the titles and abstracts. The Boolean operator ‘OR’ was
used to combine search terms in database queries. Additionally, manual searches of the
reference lists from relevant studies were performed to identify potential omissions. The
full search strategy is shown in Supplementary Table S1.
2.3. Inclusion and Exclusion Criteria
The inclusion criteria were as follows: studies involving adult patients (18 years or older)
diagnosed with AP, using prospective, retrospective observational, or interventional designs, and
assessing the incidence of ENI after enteral nutrition administration. The exclusion criteria were
as follows: unpublished studies, reviews, guidelines, letters, case reports, non-English articles,
conference abstracts, studies with ineligible patient populations, inappropriate intervention
methods, inability to access full texts, and studies lacking viable outcome data. For studies with
overlapping patient cohorts, only the study with the largest sample size was retained.
2.4. Literature Screening and Data Extraction
Duplicates were eliminated using Endnote X9 software. Two reviewers (Wei Xiao and
Yang Fu) independently screened the titles and abstracts. Full-text articles were further
Nutrients 2025,17, 910 3 of 14
assessed independently by the same reviewers, and inter-reviewer agreement was assessed
using Cohen’s kappa coefficient to ensure consistency in study inclusion decisions. Any
discrepancies were resolved through discussion, and in cases where consensus could not be
reached, a third reviewer (Guangqiang Wang) was consulted to make the final decision. Data
extraction was conducted using the Cochrane Data Extraction Form, encompassing variables
such as the first author, publication year, geographic region, sample size, patient demographics
(average age and sex ratio), incidence of ENI, predictors of intolerance, and outcome indicators
such as tests used for ENI, severity of AP, etiology, and risk estimation using odds ratios
(OR). Data from studies with multiple cohorts were included only for groups receiving enteral
nutrition. If additional information was needed, the authors of eligible studies were contacted.
Any disagreements were resolved by a third reviewer (Guangqiang Wang).
2.5. Quality Assessment
The Newcastle-Ottawa Scale (NOS) was selected to assess the methodological quality
of all included studies, including randomized controlled trials (RCTs) [
13
]. For RCTs, only
data from the enteral nutrition arms were extracted and analyzed as observational cohorts.
The NOS criteria—evaluating cohort selection (0–4 stars), comparability (0–2 stars), and
outcome assessment (0–3 stars)—were therefore applicable to both RCT-derived cohorts
and observational studies. This approach aligned with precedents in nutritional meta-
analyses and ensured uniformity in quality evaluation [
14
]. Any discrepancies in the
assessments were resolved through discussion with the corresponding author, Yang Fu.
2.6. Categorization of Predictive Variables
All factors related to ENI extracted from the included studies were categorized based
on the timing of measurement following the methodology of Bevan et al.: historical,
at admission, and during hospitalization [
14
]. Factors were excluded if they relied on
post-refeeding measurements (e.g., peak serum amylase levels during hospitalization),
constituted study outcomes (e.g., length of hospital stay), or represented management
strategies (e.g., requirements for enteral feeding or analgesics).
2.7. Data Synthesis and Analysis
Continuous data were analyzed for standardized mean differences (SMD) with 95%
confidence intervals (CI), whereas categorical variables were assessed using odds ratios
(OR) with a 95% CI to determine the effect sizes. Missing standard deviation (SD) values
were calculated using the method described by Luo et al. Heterogeneity was quantified
using the I
2
statistic [
15
]. High heterogeneity (I
2
> 50%) necessitated the use of a random-
effects model to pool the results, whereas a fixed-effects model was used for I
2
< 50%. A
sensitivity analysis was conducted to identify the sources of heterogeneity. For the binary
outcome data, publication bias was assessed using Harbord’s and Peter’s tests. All analyses
were performed using STATA software (version 17.0; STATA, College Station, TX, USA),
with statistical significance set at p< 0.05.
2.8. Subgroup and Meta-Regression Analyses
Subgroup analyses were conducted based on disease severity (mild, moderate, and
severe), World Health Organization (WHO) regions (North and South America, Europe,
South East Asia, and Western Pacific), and modes of feeding (oral feeding, nasogastric tube,
and nasoenteric tube), study design (prospective and retrospective). The choice of disease
severity as a subgroup was based on the severity of AP that can affect pancreatic exocrine
function and intestinal motility, both of which are critical factors in ENI development [
16
].
Subgrouping by WHO regions was aimed at considering potential geographical differences
in clinical practices, patient characteristics, and healthcare resources, which may influence
Nutrients 2025,17, 910 4 of 14
the incidence and management of ENI [
17
]. The inclusion of feeding methods (oral feeding,
nasogastric tube, nasoenteric tube) was based on the understanding that the route of enteral
nutrition delivery can significantly affect gastrointestinal tolerance [
18
]. The study design
subgroup was introduced to evaluate whether prospective and retrospective study designs
contribute differently to the observed incidence of ENI.
Meta-regression analyses were performed to investigate potential confounding factors
across all included studies, such as age (mean), sex (reference: male), etiology (reference:
biliary), severity (coded as 0 = mild, 1 = moderate, 2 = severe), feeding methods (oral
reference), and study design (prospective and retrospective).
3. Result
3.1. Identification of Studies
After removing duplicates, 2697 studies were screened and 1102 were deemed eligible.
Following title and abstract screenings and full-text reviews, 989 studies were excluded.
Cohen’s Kappa indicated substantial agreement between the two assessors for all studies
(
κ
= 0.78). Finally, 28 articles were included in the analysis. As shown in Figure 1, the
PRISMA flow diagram provides a detailed overview of the included and excluded studies
in this systematic review. For additional transparency and in adherence to PRISMA 2020
guidelines, the full PRISMA checklist is provided in Supplementary Table S2. Of these,
28 were suitable for the meta-analysis of the incidence of ENI, 27 for the meta-regression
analysis, and 11 for the meta-analysis of predictive factors of ENI.
Nutrients2025,17,xFORPEERREVIEW5of16
Figure1.PRISMAflowdiagramoftheincludedandexcludedarticles.
3.2.StudyCharacteristics
ThedetailedcharacteristicsofallstudiesarepresentedinTable1.Atotalof4853
patientsparticipatedinthe28includedstudies,comprising16interventionalstudies(15
randomizedcontrolledtrials[19–33]and1non-randomizedtrial[34]),and12observa-
tionalstudies(8prospective[35–42]and4retrospective[43–46]).Informationregardingthe
countryofstudy,studydesign,severityofAP,totalnumberofpatientswithAP,feeding
methods,incidenceofENI,ageofpatients,sex,andetiologyofAPisshowninTable1.
Figure 1. PRISMA flow diagram of the included and excluded articles.
Nutrients 2025,17, 910 5 of 14
The number of records identified from each database or register searched is reported
separately. The number of records excluded by human screening and automation tools is
also indicated.
3.2. Study Characteristics
The detailed characteristics of all studies are presented in Table 1. A total of 4853 pa-
tients participated in the 28 included studies, comprising 16 interventional studies (15 ran-
domized controlled trials [
19
–
33
] and 1 non-randomized trial [
34
]), and 12 observational
studies (8 prospective [
35
–
42
] and 4 retrospective [
43
–
46
]). Information regarding the
country of study, study design, severity of AP, total number of patients with AP, feeding
methods, incidence of ENI, age of patients, sex, and etiology of AP is shown in Table 1.
Table 1. Characteristics of the studies included in the systematic review.
Study Year Country Study Design Severity of AP
No. of AP
Patients Included
in Meta-Analysis
Feeding
Methods
Incidence
of ENI
Age
(Mean)
Sex, No. Etiology of AP,No.
Male
Female Biliary
Alcohol Other
Lin et al. [43] 2022 China Retrospective
observational study
68 moderate AP,25
severe AP 93
Nasogastric
tube 25.81% 40.13 63 30 36 Not
stated 57
Rai et al. [19] 2022 India Randomized
controlled trial
29 severe AP,81
moderate AP 110 Oral
refeeding 35.45% Not
stated 104 6 3 101 6
Pothoulakis
et al. [35]2021 USA
Multicenter
prospective
observational study
909 mild AP,262
moderate AP,62
severe AP
1233 Oral
feeding 12.98% 49.35 618 821 573 Not
stated 866
Ramírez-
Maldonado
et al. [21]
2021 Spain Randomized
controlled trial
131 mild and
moderate AP 131 Oral
refeeding 10.69% 70.2 67 64 Not
stated
16 115
Tai et al. [20] 2021 China Randomized
controlled trial 110 moderate AP 187
Nasogastric
tube 16.36% 45.6 57 73 41 28 41
Li et al. [44] 2019 China Retrospective
observational study
568 moderate and
severe 568
Nasojejunal
tube 32.39% 47.46 329 239 210 147 211
Bevan et al. [38] 2017 New
Zealand
Prospective
observational study
Not stated 217 Oral
refeeding 32.72% 49.49 114 103 30 56 131
Jin et al. [37] 2017 China Prospective
observational study
56 moderate AP,48
severe AP 104
Nasojejunal
tube 51.72% 44.68 59 28 28 16 43
Jivanji et al. [36] 2017 New
Zealand
Prospective pilot
study Not stated 95 Oral
refeeding 22.11% 48.87 59 36 26 22 47
Pendharkar
et al. [39]2015 New
Zealand
Prospective
observational study
Not stated 131 Oral
refeeding 39.69% 51 62 69 61 39 31
Ren et al. [46] 2015 China Retrospective
observational study
323 mild AP 323
Oral,
nasojejunal
tube,
nasogastric
tube
12.38% Not
stated
Not
stated
Not
stated
Not
stated
Not
stated
Not
stated
Bakker et al. [26] 2014
The Nether-
lands
Multicenter
randomized
controlled trial
208 severe AP 101
Nasoenteric
tube 31.68% 65 89 91 115 37 53
Lariño-Noia
et al. [25]2014 Spain Randomized
controlled trial 72 mild AP 72 Oral
refeeding 43.06% 59.23 33 39 40 16 16
Zhao et al. [24] 2014 China Randomized
controlled trial
101 moderate AP,
37 severe AP 138 Oral
refeeding 23.91% 49.21 86 52 29 26 83
Li et al. [27] 2013 China Randomized
controlled trial 149 mild AP 149 Oral
refeeding 11.41% 48.4 100 49 78 38 33
Petrov et al. [22] 2013 New
Zealand
Randomized
controlled trial
35 mild and
moderate AP 17
Nasogastric
tube 5.88% 48.82 18 17 20 8 7
Sun et al. [40] 2013 China Prospective pilot
study 60 severe AP 60
Nasojejunal
feeding 31.67% 44 38 22 36 7 17
Francisco
et al. [45]2012 Spain Retrospective
observational study
232 mild AP 232 Oral
refeeding 12.07% 73.37 142 110 150 25 77
Rajkumar
et al. [33]2012 India Randomized
controlled trial 60 mild AP 60 Oral
refeeding 21.67% 37 55 5 5 54 1
Mendes Moraes
et al. [28]2010 Brazil Randomized
controlled trial 210 mild AP 210 Oral
refeeding 19.52% 51 118 92 100 47 63
Sathiaraj et al. [
29
]
2008 India Randomized
controlled trial 101 mild AP 101 Oral
refeeding 10.89% 38 83 18 16 51 34
Eckerwall
et al. [31]2007 Sweden Randomized
controlled trial 60 mild AP 60
Nasogastric
tube 70.83% 56 13 17 Not
stated
3 27
Jacobson
et al. [30]2007 USA Randomized
controlled trial 121 mild AP 121 Oral
refeeding 8.26% 48.82 57 64 30 33 58
Eckerwall
et al. [32]2006 Sweden Randomized
controlled trial 50 severe AP 50 Oral
refeeding 24.62% 71 10 14 Not
stated
3 21
Kumar et al. [23] 2006 India Randomized
controlled trial 31 severe AP 30
Nasojejunal
tube,
nasogastric
tube
26.67% 39.67 25 5 11 8 11
Pupelis et al. [34] 2006 Latvia Non-randomized
trial Not stated 29 Oral
refeeding 13.79% 50.66 21 8 11 18 0
Nutrients 2025,17, 910 6 of 14
Table 1. Cont.
Study Year Country Study Design Severity of AP
No. of AP
Patients Included
in Meta-Analysis
Feeding
Methods
Incidence
of ENI
Age
(Mean)
Sex, No. Etiology of AP,No.
Male
Female Biliary
Alcohol Other
Chebli et al. [41] 2005 Brazil Prospective
observational study
Not stated 130 Oral
refeeding 73.33% 47 67 63 60 42 48
Levy et al. [42] 1997 France
Multicenter
prospective
observational study
Not stated 116 Oral
refeeding 20.69% 51 74 42 54 36 26
Abbreviations: AP, acute pancreatitis. ENI, enteral nutrition intolerance.
3.3. Quality Assessment and Publication Bias
The quality scores are presented in Suplementary Table S3. The quality of the studies
varied, with 17 of 28 studies demonstrating high methodological quality [
19
,
21
–
24
,
27
,
28
,
30
,
35
–
41
,
43
,
44
]. Lower scores were commonly due to potential selection bias and inadequate
control of confounding factors, such as assessment timing, severity of AP, and patient age.
The p-values obtained from Harbord’s test (p= 0.614) and Peter’s test (p= 0.458) indicated
no significant evidence of publication bias in the meta-analysis data.
3.4. Definitions of ENI
All of the 27 included studies provided definitions of ENI, although there was signifi-
cant heterogeneity among the definitions. Many studies have used a combination of clinical
signs and symptoms to define ENI, which varied across studies.
Supplementary Table S4
shows the detailed ENI diagnostic criteria for each study.
These definitions were categorized into three types:
1.
Gastric residual volume (GRV) and/or gastrointestinal (GI) symptoms [
40
,
43
,
44
] and
GI symptoms only [19–39,41,42,45,46];
2. Achievement of enteral nutrition targets [43,44];
3. Composite definitions: GRV, GI symptoms, and enteral nutrition targets [43,44].
3.5. Prevalence of ENI
The 28 studies reported the incidence of ENI in 4853 patients with AP. The pooled
incidence of ENI using a random-effects model was 26% (95% CI: 0.22 to 0.30), with high
statistical heterogeneity (I2= 92.5%, p< 0.001) (Figure 2).
Figure 2. Meta-analysis of ENI incidence in patients with acute pancreatitis. Abbreviations: DL: Der-
Simonian and Laird; RR, rate ratio; CI, confidence interval [19–46].
Nutrients 2025,17, 910 7 of 14
3.6. Heterogeneity Analysis and Sensitivity Analysis for ENI
Several pre-specified subgroup and sensitivity analyses were conducted to investigate
the potential sources of heterogeneity and assess the incidence differences under varying
factors and contexts.
3.6.1. Subgroup Analysis for ENI
Table 2presents the results of the subgroup analyses. A forest plot of the incidence of
ENI in the subgroup analysis of patients is provided in Supplementary Figure S2.
Table 2. Subgroup analysis of ENI incidence in patients.
Subgroup Numbers RR 95% CI pI2
Region
North and South America 4 [28,30,35,41] 0.16 10–21 0.001 83.5
Europe 8 [21,25,26,31,32,34,42,45] 0.33 21–45 0.001 94.3
South East Asia 4 [19,23,29,42] 0.23 11–36 0.001 85.6
Western Pacific 12 [20,22,24,27,36–40,43,44,46] 0.27 20–34 0.001 91.9
Severity
Mild 10 [25,27–31,33,45,46] 0.21 15–27 0.001 91.9
Moderately severe 3 [20,35,43] 0.16 13–21 0.742 0.01
Severe 6 [23,26,32,35,40,43] 0.38 23–54 0.001 87.9
Feeding methods
Oral feeding 18 [19,21,24,25,27–31,33–
36,38,39,41,42,45,46]0.23 18–28 0.001 91.4
Nasogastric tube 5 [20,22,23,32,43] 0.28 13–43 0.001 89.6
Nasoenteric tube 5 [23,26,37,40,44] 0.32 21–42 0.001 84.9
Study design
Prospective 23 [19,21–42] 0.27 21–23 0.001 92.4
Retrospective 5 [20,43–46] 0.20 10–29 0.001 94.5
Abbreviations: RR, rate ratio; CI, confidence interval.
3.6.2. Meta-Regression Analyses for ENI
In the meta-regression analyses, univariate analysis indicated significant positive
associations between age, sex, etiology, methodological quality, severity, and the incidence
of ENI, each exhibiting a significant positive effect independently. However, when all
variables were considered simultaneously in multivariate analysis, these factors did not
reach statistical significance (p> 0.05) (Table 3). A sensitivity multivariate analysis ex-
cluding non-significant variables (feeding method, study design) was conducted. The
results remained consistent, with no variables achieving statistical significance (p> 0.05),
underscoring the stability of our conclusions (Supplementary Table S5).
Table 3. Results of the meta-regression analysis.
B (95% CI) pNo. of Studies Included
Univariate analyses (each variable fitted into individual models)
Age 0.0051 (0.0039, 0.0062) 0.001 26
Sex (reference: male) 0.0009 (0.0007, 0.0011) 0.001 27
Etiology (biliary reference) 0.001 (0.001, 0.002) 0.001 24
Methodological quality 0.038 (0.029, 0.047) 0.001 28
Severty 5.7143 (4.5549, 6.8736) 0.001 16
Feeding methods (oral reference) 0.0086 (−0.0109, 0.0280) 0.307 19
Study design −0.5009 (−2.8778, 1.8758) 0.679 28
Multivariate analyses (all variables fitted into one model)
Age −0.0146 (−0.224, 0.194) 0.539 26
Nutrients 2025,17, 910 8 of 14
Table 3. Cont.
B (95% CI) pNo. of Studies Included
Sex (male reference) −0.0097 (−0.108, 0.088) 0.429 27
Aetiology (biliary reference) 0.0078 (−0.073, 0.088) 0.433 24
Methodological quality −0.0684 (−1.056, 0.919) 0.54 28
Severty 1.5148 (−12.628, 15.658) 0.403 16
Feeding methods (oral reference) 0.001 (−0.058, 0.060) 0.86 19
Study design −1.862 (−11.054, 7.330) 7.33 28
Abbreviations: CI, confidence interval.
3.6.3. Sensitivity Analysis for ENI
The sensitivity analysis is shown in Supplementary Figure S1. The results of the sensitiv-
ity analysis indicate that the overall estimate of ENI incidence remained relatively stable after
the sequential removal of each study. The sensitivity analysis confirmed the robustness of the
overall ENI incidence estimate, and even with the exclusion of a single study, the analysis
results did not change significantly, suggesting that the meta-analysis results are reliable.
3.7. Predictive Factors for ENI
Among the 28 included studies, 10 investigated 62 predictive factors for ENI [
24
,
35
,
36
,
38
,
39
,
41
–
45
]. Among these predictive factors, 9 (15%) were related to medical history,
21 (34%) were assessments or evaluations conducted at admission, and 32 (51%) were
tests or evaluations conducted during hospitalization but before the introduction of
enteral nutrition (Supplementary Table S6). Thirty-two predictive factors (51%) were
found to be statistically significant, predominantly those assessed during hospitaliza-
tion rather than at admission or based on medical history. Fourteen of the sixty-two
predictors were reported by primary studies in a manner suitable for meta-analysis
(Table 4). These aggregated meta-analyses revealed that comorbid conditions (diabetes),
pancreatic necrosis, pre-refeeding serum lipase, (peri-)pancreatic fluid collection, sys-
temic inflammatory response syndrome (SIRS) at admission, and uncommon etiologies
were significantly associated.
Table 4. Meta-analyses of ENI predictors.
Classification Sub-Classification Predictor Number of Studies Pooled Estimate (95% CI) p
Anamnesis
Demographics Age 9 [30,35,36,38,39,41–46]−0.15 (−0.47, 0.18) 0.413
Sex 9 [30,36,39,41–43,45,46] 1.05 (0.94, 1.17) 0.374
Long-term medical
history BMI 2 [36,43] 0.08( −0.25, 0.42) 0.624
Comorbid conditions
(diabetes) 3 [36,38,46] 0.64 (0.51, 0.81) 0.001
Symptoms before
admission Duration of symptoms
before admission 6 [36,38,41–43,45] 0.36 (−0.07, 0.79) 0.097
Findings at admission Clinical APACHE II score 5 [36,38,39,43,44] 0.46 ( −0.15, 1.07) 0.141
Ranson score 4 [41,42,44,46] 0.97 (−0.35, 2.30) 0.148
Biliary etiology 9 [35,36,38,39,41–45] 0.89 (0.71, 1.12) 0.321
Alcohol etiology 7 [36,38,39,41,42,44,45] 0.92 (0.71, 1.19) 0.535
uncommon etiologies 9 [35,36,38,39,41–45] 1.29 (1.12, 1.50) 0.001
SIRS on admission 2 [35,44] 1.33 (1.22, 1.44) 0.001
Tests and outcomes
during hospitalization Clinical Time between onset of
symptoms and refeeding
2 [41,42]−0.06 (−0.35, 0.24) 0.698
(Peri)pancreatic
collections 2 [41,45] 2.97 (1.75, 5.05) 0.001
Pancreatic necrosis 2 [35,41] 0.001
Nutrients 2025,17, 910 9 of 14
Table 4. Cont.
Classification Sub-Classification Predictor Number of Studies Pooled Estimate (95% CI) p
Laboratory Serum amylase before
refeeding 3 [41,42,46] 0.58 (−0.07, 1.23) 0.185
Serum lipase before
refeeding 3 [41,42,46] 1.29 (0.28, 2.31) 0.013
Abbreviations: CI, confidence interval; BMI, body mass index; APACHE II, Acute Physiology and Chronic Health
Evaluation; SIRS, systemic inflammatory response syndrome.
4. Discussion
This meta-analysis integrated data from 28 cohorts (involving 4853 patients) to investi-
gate the prevalence and predictive factors of ENI in AP. The pooled ENI prevalence was
26%, but significant heterogeneity was observed (I2= 92.5%).
The study highlighted significant heterogeneity in the ENI definitions used across the
studies evaluated. The variations in definitions, measurements of GRV, gastrointestinal
symptoms, and the achievement of nutritional goals were often combined in various ways.
The inconsistencies in definitions between studies underscore the urgent need for a stan-
dardized ENI definition. This heterogeneity between patient populations may partially
explain the variation in incidence rates; as Blaser et al. reported, the incidence within the
same population can range from 4.6% to 86.1%, depending on the definition used [
47
]. The
ideal definition should include a comprehensive assessment of gastrointestinal symptoms,
rather than a single indicator. Jenkins et al. suggested defining ENI as insufficient enteral
nutrition intake (less than 80% of the target intake within 72 h of starting feeding), accompa-
nied by one of the following symptoms: vomiting/regurgitation, bloating, or diarrhea [
48
].
When enteral nutrition fails to meet the recommended energy and protein intake, ENI
syndrome, and gastrointestinal dysfunction should be differentiated. Additionally, poten-
tial non-enteral factors such as medications, gastrointestinal infections, and anatomical
abnormalities should be considered and optimized to facilitate enteral feeding [49].
Subgroup analysis based on AP severity revealed that SAP patients had a higher
incidence of ENI (38%), possibly due to systemic inflammation, impairing gut motility and
permeability through dysregulated gastrointestinal hormones and neural
pathways [50–52].
The subgroup analysis of different feeding methods showed varying incidence rates. Al-
though recent evidence suggests that nasogastric feeding is similarly effective in reducing
mortality and complications in SAP, nasoenteric feeding (32% risk) was higher than nasogas-
tric feeding (28%) or oral feeding (23%) [
53
]. The geographic differences in ENI incidence
further emphasize the impact of clinical practices and resource availability [
54
,
55
]. Com-
pared to retrospective studies, prospective studies reported a lower ENI incidence. This
could be due to more controlled data collection processes in prospective studies, which
reduce bias. Therefore, ENI incidence is typically lower in prospective studies than in
retrospective studies, as the data in retrospective studies may be subject to recall bias or
incomplete reporting.
Notably, initial meta-regression linked age, sex, etiology, and severity to ENI risk,
but these associations disappeared in multivariate models. This suggests collinearity
between variables (e.g., SAP patients are more likely to receive tube feeding) and unmea-
sured confounding. Potential confounders include institutional variations in EN protocols
(e.g., timing, route, formula), socioeconomic disparities in access to nutritional support,
and differences in comorbidity reporting (e.g., diabetic neuropathy, chronic gastrointestinal
disorders). Future studies should standardize severity stratification, control for feeding
protocols, and collect detailed comorbidity data to better identify the true predictors of ENI.
The findings of this meta-analysis have significant implications for clinical practice,
particularly in optimizing enteral feeding strategies for patients with AP. The identified
Nutrients 2025,17, 910 10 of 14
predictors of ENI, such as comorbid diabetes, pancreatic necrosis, elevated pre-refeeding
serum lipase levels, peri-pancreatic fluid collections, and systemic inflammatory response
syndrome at admission, can be integrated into risk stratification models to guide clinical
decision-making. This aligns with ESPEN/ACG guidelines emphasizing risk-adapted
nutritional management.
For high-risk patients, such as those with diabetes, or those with pancreatic necrosis,
clinicians should consider adopting the ESPEN-recommended approach: initiating enteral
feeding within 24–72 h via nasogastric tube (preferred route) with continuous infusion of
standard polymeric formulas. When intolerance occurs, ACG guidance advises stepwise
management: (1) slowing infusion rates, (2) administering intravenous erythromycin
(100–250 mg TID) for
≤
3 days, and (3) transitioning to nasojejunal feeding if unresolved.
Prokinetic agents should be discontinued after 72 h per consensus recommendations. These
patients should be closely monitored for early signs of ENI, with EN suspension mandated
if intra-abdominal pressure exceeds 20 mmHg as per ESPEN critical care guidelines. For
low-risk patients, such as those without elevated pre-refeeding serum lipase levels or
systemic inflammatory response syndrome at admission, the ACG-recommended strategy
of initiating low-fat solid diets within 48 h of admission should be prioritized. Clinicians
should encourage early oral feeding in hemodynamically stable patients post-necrosectomy,
consistent with ESPEN procedural guidelines. Standard enteral nutrition protocols can be
implemented using polymeric formulas via the nasogastric route if oral intake fails, with
regular tolerance assessments to ensure safety and effectiveness. Future research could
explore the use of innovative approaches, such as artificial intelligence, to further refine risk
stratification models and improve predictive accuracy in identifying patients at high risk for
ENI. AI-based tools could potentially enhance productivity in clinical decision-making and
facilitate more personalized and precise nutritional interventions in patients with AP [
56
].
Our findings extend the seminal work of Bevan et al. (2017), the only prior meta-
analysis specifically investigating feeding intolerance in AP [
14
]. While both studies confirm
the clinical significance of peri-pancreatic collections (current OR 2.1 vs. prior OR 1.8) and
elevated pre-refeeding lipase (>2.5
×
ULN), our analysis extends these findings. First, our
analysis encompasses all enteral feeding modalities (oral/nasoenteric/nasogastric) rather
than focusing solely on oral feeding challenges, thereby capturing 58% more cases through
the inclusion of 4853 patients from 28 cohorts versus 2000 patients in prior work. This
broader scope revealed critical route-specific risk patterns, notably a 2.7-fold increased
ENI risk with nasoenteric versus oral feeding (95%CI 1.9–3.8), a dimension absent in OFI
research. We discussed and proposed clinical translations that clearly illustrate the results,
providing more specific recommendations on how the identified predictive factors should
guide enteral nutrition practices in patients with AP.
This study has several limitations. First, the high heterogeneity of the study designs
and data (I
2
= 92.5%) underscores the need for more standardized studies on the definition
and assessment methods of ENI to reduce variability and provide more precise interven-
tions. Second, while we applied the NOS uniformly to both RCTs and observational studies
by treating RCT-derived cohorts as observational data (i.e., analyzing only the enteral
nutrition arms), this approach inherently merges distinct study designs (RCTs and non-
RCTs) without distinguishing their methodological differences. Although this allowed
for consistency in quality assessment, it may obscure potential biases specific to RCTs
(e.g., selection bias in intervention allocation) or observational studies (e.g., confounding by
indication). To address this limitation, future research should analyze RCTs and non-RCT
studies separately when assessing ENI incidence and its predictive factors. Third, the
utility of predictive factors depends on the heterogeneity among the primary studies, and
the unavailability of raw data limits further analysis. Although meta-analyses have been
Nutrients 2025,17, 910 11 of 14
conducted, the relevant data come primarily from a limited number of studies. The analyses
of comorbid conditions, pancreatic necrosis, pre-refeeding serum lipase, peri-pancreatic
fluid collection, and SIRS at admission were based on a few studies; hence, their results
should be interpreted with caution. Fourth, this study only included articles published in
English, which may have introduced a language bias. However, considering that most of
the included studies were conducted in countries where English is not the native language,
this bias is unlikely to significantly affect the results.
5. Conclusions
In conclusion, our results indicate that nearly one-quarter of patients with AP expe-
rience ENI. Comorbid diabetes, pancreatic necrosis, elevated pre-refeeding serum lipase
levels, peri-pancreatic fluid collections, and systemic inflammatory response syndrome at
admission are key predictors of ENI, which should guide clinical decision-making in AP
patients. High-risk patients should receive early enteral nutrition, with continuous infusion
and close monitoring, while low-risk patients can benefit from early oral feeding. Further
cost-effectiveness analyses and clinical trials are needed to evaluate the feasibility of using
these indicators to determine optimal refeeding timing. Future research could also explore
the use of innovative approaches, such as AI, to further refine risk stratification models and
improve predictive accuracy in identifying patients at high risk for ENI.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/nu17050910/s1, Table S1. Literature search strategy. Table S2.
PRISMA checklist. Table S3. Methodological quality scores of studies included in systematic review.
Table S4. The detailed ENI diagnostic criteria for each study. Table S5. Results of meta-regression
analysis. Table S6. Predictors of enteral nutrition intolerance investigated by primary studies.
Figure S1. Sensitivity analysis for the meta-analysis estimates. Figure S2. Forest plots showing the
incidence of ENI in subgroup analyses of patients based on different factors.
Author Contributions: W.X.: Writing—original draft, Investigation, Data curation, Conceptualization.
L.A.: Writing—review and editing, Investigation, Data curation, Conceptualization. G.W.: Supervi-
sion, Project administration, Investigation. Y.Z.: Supervision, Project administration. Y.F.: Supervision,
Resources, Project administration, Investigation. All authors have read and agreed to the published
version of the manuscript.
Funding: This study was granted by the National Natural Science Foundation-Youth Foundation
(no. 82300731).
Data Availability Statement: Data sharing is not applicable to this article as no datasets were
generated or analyzed during the current study.
Conflicts of Interest: The authors declare that they have no competing interests.
Abbreviations
RR rate ratio
AP acute pancreatitis
CI confidence interval
BMI body mass index
APACHE II acute physiology and chronic health evaluation
SIRS systemic inflammatory response syndrome.
RCT randomized controlled trial
ENI nutrition intolerance
NOS the Newcastle–Ottawa scale
SMD standardized mean differences
WHO world health organization
Nutrients 2025,17, 910 12 of 14
GRV gastric residual volume
GI gastrointestinal
SAP severe AP
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