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. 2026 Apr 14;26:328. doi: 10.1186/s12871-026-03828-6

Comparison of the effects of different types of nasointestinal tube placement techniques for ICU patients: a Bayesian network meta-analysis

Peiyi Li 1, Jia Peng 1, Mengyu He 1, Liqin Wang 1, Xu Lu 1, Xi Liu 1, Shuang Wu 1,✉
PMCID: PMC13192050  PMID: 41981495

Abstract

Objective

To systematically compare the effectiveness and safety of different nasointestinal tube (NET) placement techniques in Intensive Care Unit (ICU) patients through a Bayesian network meta-analysis (NMA).

Methods

In accordance with PRISMA-NMA guidelines, we conducted a systematic review of randomized controlled trials (RCTs) from major English and Chinese databases, including PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, CBM, CNKI, Wanfang, and VIP. Data were analyzed within a Bayesian framework utilizing the ‘BUGSnet’ package in R, deriving relative treatment effects from posterior distributions and estimating rank probabilities. Efficacy hierarchies were established according to the surface under the cumulative ranking curve (SUCRA) values, which serve strictly as summary measures of probabilistic ordering. The assessed outcomes were placement success rate, procedure time, complication incidence rate, and direct healthcare costs.

Results

This NMA evaluated 19 RCTs involving 1,554 ICU patients. All instrument-assisted methods demonstrated higher placement success rates than Blind placement. Of the five assessed techniques, Fluoroscopic guidance showed the highest probability of being ranked favorably for placement success and complication reduction. However, wide and overlapping credible intervals suggest comparable clinical efficacy among the instrumented modalities. Furthermore, Electromagnetic and Endoscopic placements exhibited the highest probabilities of being the most time-efficient solutions, markedly decreasing procedural duration relative to the Blind method. Importantly, sparse data yielded no statistically significant differences in direct healthcare costs, preventing definitive conclusions regarding economic differences.

Conclusions

This NMA demonstrates that instrument-guided methods are significantly superior to Blind placement for NET placement in ICU patients. While Fluoroscopic guidance holds the highest probabilistic ranking for success and safety, other instrument-guided methods offer comparable clinical benefits and high probability in minimizing procedural duration. Clinical medical professionals should prioritize instrument-assisted methods to improve patient safety and procedural efficacy, selecting the specific modality based on resource availability, local expertise, and particular clinical requirements.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-026-03828-6.

Keywords: Network meta-analysis, Nasointestinal tube placement, Intensive care units, Magnetic navigation, Endoscope, Fluoroscopy

Background

Critically ill patients in the Intensive Care Unit (ICU) require nutritional support to maintain metabolic stability and improve clinical outcomes [1]. While enteral nutrition (EN) is generally preferred over parenteral nutrition due to its lower infection risks and cost-effectiveness [2], many ICU patients cannot tolerate gastric feeding due to gastroparesis or high aspiration risk [3]. Consequently, post-pyloric feeding via a nasointestinal tube (NET) is frequently advocated to prevent gastric reflux and reduce pneumonia risk, particularly in mechanically ventilated patients [4, 5]. However, achieving reliable post-pyloric access is challenging due to patient-related factors such as altered consciousness, as well as procedural risks like accidental respiratory tract placement [4].

Traditionally, Blind bedside insertion has been the standard of care. However, this method is limited by variable success rates and the potential for serious complications [6, 7]. To improve accuracy, various guided techniques have been introduced, including Fluoroscopic, Electromagnetic, Ultrasound, and Endoscopic guidance. While fluoroscopy was once considered the gold standard, it entails ionizing radiation exposure and logistical challenges [8, 9]. Newer bedside technologies, such as electromagnetic and ultrasound systems, offer real-time visualization but vary significantly in terms of cost, equipment availability, and required operator expertise [10–12]. Despite their high reliability, these technologies remain underutilized because they require specialized equipment, trained personnel, and substantial resources.

Despite this array of options, the optimal strategy remains unclear. The existing evidence base is constrained by a predominance of single-center randomized controlled trials (RCTs) with small sample sizes. Furthermore, most studies have relied on direct pairwise comparisons [3, 10, 13], making it difficult to establish a comprehensive hierarchy of efficacy across all available modalities. Consequently, current clinical guidelines lack definitive consensus on the most effective technique.

To address this gap, we conducted a systematic review and Bayesian network meta-analysis (NMA). Unlike traditional meta-analyses, this approach synthesizes both direct and indirect evidence to estimate the probabilistic ranking of competing interventions [14, 15]. The aim of this study is to provide a comparative effectiveness hierarchy regarding placement success, complications, insertion time, insertion-related complications incidence rate and direct healthcare costs to guide clinical decision-making in the ICU.

Methods

This study was conducted in accordance with the PRISMA extension statement for reporting systematic reviews incorporating network meta-analyses (PRISMA-NMA) [16, 17]. The protocol for the study has been submitted to the PROSPERO for registration (CRD420251239614).

PICOS criteria

  • Participants were ICU patients who required NET placement for enteral nutrition.

  • Intervention encompassed the use of specific NET insertion techniques, including fluoroscopic guidance, electromagnetic guidance, ultrasound guidance, and endoscopic assistance.

  • Control was another NET insertion technique or the traditional blind insertion method.

  • Outcome measures were classified into primary and secondary outcomes. Primary outcome is the placement success. Secondary outcomes included insertion time, insertion-related complications incidence rate and direct healthcare costs.

  • Study design was restricted to RCTs published in any language.

Data sources and searches

We conducted a search on Pubmed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), Wanfang database, and Chinese Scientific Journal Database (VIP database) to identify appropriate articles for eligible studies published until October 20, 2025. The search included keywords such as “nasointestinal tube” “Intensive Care Units” and “insertion”. Search terms used a combination of medical subject headings (MeSH) and free-text terms. Key MeSH terms were as follows: ‘Intensive Care Units’, ‘Intubation, Gastrointestinal’. The search terms and search algorithm for PubMed are available in the supplement (Supplementary Part A).

Inclusion and exclusion criteria

We identified relevant studies with complete texts based on the specified PICOS selection criteria.

Population

We recruited patients who required short-term enteral treatment as assessed by the attending ICU physicians. We excluded patients with preexisting nasopharyngeal anomalies that precluded nasal intubation, those with ileus or mechanical bowel blockage, and instances where informed consent could not be obtained from the patient or their authorized legal representative.

Interventions and comparisons

In this NMA, interventions were categorized into five distinct nodes based on the primary guidance modality used during insertion. To ensure conceptual homogeneity and verify the transitivity assumption of the network, we focused strictly on device-dependent methods. Consequently, studies investigating solely pharmacological interventions (e.g., prokinetics such as erythromycin or metoclopramide) or manual facilitation maneuvers in the absence of specific guidance devices were excluded. The final categorization comprised the following nodes: (1) Blind placement, (2) Fluoroscopic guidance, (3) Electromagnetic guidance, (4) Ultrasound guidance, and (5) Endoscopic guidance.

Outcomes

The primary outcome was placement success rate, defined as the proportion of NETs successfully positioned distal to the pylorus, verified through abdominal radiography (interpreted by radiologists or ICU clinicians), real-time ultrasonography, or direct visualization of the intestinal villi via endoscopic or visual stylet techniques. Secondary outcomes included insertion time, insertion-related complications incidence rate and direct healthcare costs. Importantly, to standardize the data extraction and address potential outcome reporting bias, the outcome coding for “complications” was explicitly predefined as acute insertion-related adverse events. Based on the clinical manifestations reported across the included trials, this composite outcome specifically encompassed inadvertent airway intubation (tracheobronchial placement), epistaxis (nasopharyngeal bleeding), upper gastrointestinal bleeding, pneumothorax, aspiration, abdominal pain and distension, and dyspnea.

Research design

Only RCTs were selected for inclusion. Review, protocols, editorials, meta-analysis, conference abstracts, other secondary sources, letters to the editor, and animal experiment research were excluded.

Data selection and extraction

Selection of studies

All studies were imported into Noteexpress 4.0 software for removal of duplication. Two researchers (LPY and PJ) independently evaluated all titles and abstracts obtained from the searches and examined the full texts of the remaining articles for eligibility. Discrepancies between the two researchers were resolved through consultation and arbitration by other members of the review team (WS and HMY).

Data extraction and management

Using a pre-prepared data structure extraction sheet, the same two researchers (LPY and PJ) independently read and extracted the data from each trial. The extracted data encompassed study characteristics, baseline participant characteristics, placement method types, and outcomes. The two researchers addressed any discrepancies in data extraction through the participation of a third researcher (WS).

Risk of bias assessment

Two independent researchers (LPY and PJ) evaluated the methodological quality of the included studies using Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2) [18]. The tool evaluates the risk of bias by examining five distinct domains: (1) bias arising from the randomization process; (2) bias due to deviations from intended interventions; (3) bias due to missing outcome data; (4) bias in measurement of the outcome; and (5) bias in selection of the reported result. Following the guidelines laid out in the Cochrane Handbook, the overall risk of bias for each study was rated as either low risk of bias, some concerns, or high risk of bias. Disagreements were resolved by a third reviewer (WS).

Quality assessment

We assessed the certainty of evidence for each network estimate using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework adapted for network meta-analysis, via the Confidence in Network Meta-Analysis (CINeMA) web application. The evaluation encompassed six domains: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. The certainty of the evidence was graded as high, moderate, low, and very low. Disagreements were discussed with a third investigator (WS). Additionally, we summarized the certainty of evidence in a Summary of Findings (SoF) table for the primary comparisons, following Cochrane recommendations.

Statistical analysis

All data were double-entered into the database to ensure accuracy. Relative treatment effects were estimated using Bayesian contrast-based multilevel NMA models [19] and Markov Chain Monte Carlo (MCMC) simulation in R utilizing the ‘BUGSnet’ package. We employed non-informative (vague) priors for all model parameters to allow the posterior distributions to be driven primarily by the observed data [19, 20]. Network diagrams were generated to visualize the geometric structure of the evidence network, with node sizes reflecting the sample size and edge thicknesses proportional to the number of studies comparing the interventions.

To address clinical and methodological heterogeneity among the included studies, we utilized a random-effects model, which provides more conservative estimates for pooled effects. Specifically, the MCMC simulation was performed with four independent chains. The models were run with an initial adaptation phase of 10,000 iterations and a burn-in period of 20,000 iterations to ensure the sampler reached a stable stationary distribution, followed by 100,000 iterations for posterior inference. To minimize within-chain autocorrelation, a thinning interval of 20 was applied. Model convergence was rigorously evaluated using the Potential Scale Reduction Factor (PSRF < 1.05) and visual inspection of trace plots and density plots [21].

The transitivity assumption was evaluated by comparing the distribution of potential clinical effect modifiers across treatment comparisons, including patient age, ICU type, and baseline disease severity. Consistency between direct and indirect evidence was evaluated using the Deviance Information Criterion (DIC) to compare consistency versus inconsistency models (Supplementary Part B) [22]. Furthermore, to comply with PRISMA-NMA guidelines, local inconsistency was assessed using the node-splitting method via the Gemtc package in R for outcomes containing closed loops in their network geometry (Supplementary Part C). This approach separates evidence into direct and indirect estimates to mathematically evaluate their agreement, with a p-value > 0.05 indicating local consistency.

For binary outcomes, results were expressed as odds ratios (ORs), while mean differences (MDs) were used for continuous variables, both reported with 95% credible intervals (CrIs). To establish a probabilistic hierarchy of the five techniques, we calculated the Surface Under the Cumulative Ranking (SUCRA) values and rank probabilities. Importantly, SUCRA values were interpreted as a summary statistic of the cumulative ranking for each intervention (reflecting its overall probability of being among the top ranks) rather than an absolute measure of clinical effect size.

To ensure the robustness of our results, sensitivity analyses were performed by excluding studies with a high risk of bias (as identified by RoB 2) and excluding pediatric populations.

Results

Literature search results

An aggregate of 4,915 records was found through a search of databases. After the removal of 1,657 duplicates, the remaining 3,258 records were screened by title and abstract. We subsequently retrieved 132 full-text research articles for detailed eligibility assessment. Finally, 19 RCTs (comprising 1,554 patients) involving five NET placement techniques were included. The process of study selection is depicted in Fig. 1.

Fig. 1.

Fig. 1

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Flow diagram of systematic review

Included study characteristics

A total of 19 RCTs published between 2000 and 2024, involving 1,554 participants, were included in this NMA. The sample sizes of the individual trials ranged from 17 to 82. All included trials were two-arm studies. The geometry of the entire evidence network comprised the following treatment arms: Blind (11 studies), Endoscopic (10 studies), Electromagnetic (8 studies), Ultrasound (5 studies), and Fluoroscopic (4 studies).

Importantly, to evaluate the transitivity assumption, we assessed the distribution of potential clinical effect modifiers (e.g., age, gender, and baseline conditions) across the different treatment nodes. No systematic or clinically meaningful differences in these baseline demographic factors were observed across the available direct comparisons, thereby justifying the validity of the indirect comparisons within the network. Comprehensive details and baseline characteristics of the included studies are summarized in Table 1.

Table 1.

Characteristics of included studies

Study Study population Sample size Types of placement method Age(years) Male (%) Severity of Illness Outcomes

Li.

2024 [23]

 traumatic brain injury patients

I:48

C:48

I: Endoscopic

C: Ultrasound

I:66.25 ± 7.80

C:67.56 ± 8.43

I:62.5

C:54.2

GCS:

I: 5.79 ± 0.58

C:5.53 ± 0.79

 ①②

Ma.

2024 [24]

 neurocritical care patients

I:43

C:42

I: Electromagnetic

C: Ultrasound

I:51.81 ± 13.56

C:57.17 ± 12.23

I:48.8

C:47.6

APACHE II:

I:28.24 ± 6.87

C:29.05 ± 7.00

 ①②③

Zhang.

2023 [25]

 critically ill patients

I:54

C:54

I: Endoscopic

C: Blind

I:65.35 ± 17.33

C:64.94 ± 18.80

I:70.4

C:75.9

APACHE II:

I:25.80 ± 6.34

C:25.11 ± 6.91

 ①②③

Shang.

2017 [26]

 traumatic brain injury patients

I:33

C:32

I: Ultrasound

C: Blind

I:37.4 ± 15.5

C:36.2 ± 14.6

I:60.6

C:56.3

GCS: I:6.5 ± 0.75

C:6.3 ± 1.0

 ①③

Shen.

2012 [27]

 critically ill patients

I:22

C:21

I: Fluoroscopic

C: Endoscopic

I:50.1 ± 10.3

C:49.1 ± 12.2

I:54.5

C:52.4

APACHE II:

I:18.2 ± 3.4

C:18.7 ± 3.2

 ①③

Wang.

2024 [28]

 neurocritical care patients

I:34

C:34

I: Endoscopic

C: Blind

I:54.62 ± 17.65

C:54.59 ± 17.33

I:70.9

C:64.7

APACHE II:

I:12.76 ± 3.29

C:12.82 ± 4.06

 ①②③④

Cao.

2024 [29]

 critically ill patients

I:35

C:35

I: Ultrasound

C: Blind

I:63. 3 ± 8. 1

C:63. 3 ± 8. 1

I:53.1

C:51.3

APACHE II: I:21. 09 ± 2. 73

C:20. 09 ± 2. 49

 ①③

Wei.

2024 [30]

 critically ill patients

I:40

C:40

I: Electromagnetic

C: Blind

I:57.50 ± 13.59

C:60.83 ± 13.43

I:67.5

C:57.5

I: Not Reported

C: Not Reported

 ①②③

Hou.

2024 [31]

 critically ill neurosurgical patients

I:30

C:30

I: Electromagnetic

C: Blind

I:50.07 ± 15.61

C:53.53 ± 11.76

I:83.3

C:73.3

GCS:

I:6.37 ± 2.25

C: 5.93 ± 1.65

 ①②③

Gao.

2018 [32]

 critically Ill patients

I:81

C:80

I: Electromagnetic

C: Endoscopic

I:51.5 ± 18.3

C:52.3 ± 18.2

I:48.6

C:51.4

SAPS II: I:41.7 ± 3.8

C:42.1 ± 3.9

 ①②③④

Wang.

2024 [11]

 critically Ill patients

I:50

C:50

I: Ultrasound

C: Blind

I:67.78 ± 15.50

C:65.32 ± 16.50

I:66

C:64

APACHE II:

I:19.89 ± 4.00

C:19.4 ± 7.00

 ①②

Holzinger.

2011 [33]

 critically ill patients

I:44

C:22

I: Electromagnetic

C: Endoscopic

I:55 ± 18

C:55 ± 15

I:81.8

C:63.6

SAPS II:

I:50 ± 19

C:50 ± 15

 ①②③

Jha.

2020 [34]

 critically ill children

I:28

C:24

I: Electromagnetic

C: Blind

I:1.45 ± 3.35

C:9.55 ± 3.76

I:60.7

C:54.2

PRISM:

I:8.00 ± 7.81

C:6.33 ± 9.46

 ①②③

Foote.

2004 [35]

 critically ill patients

I:26

C:17

I: Endoscopic

C: Fluoroscopic

I:59.0 ± 4.1

C:58.1 ± 5.6

I:50

C:44.4

I: Not Reported

C: Not Reported

 ①②③

Chen.

2022 [36]

 critically ill patients

I:61

C:61

I: Endoscopic

C: Blind

I:66.98 ± 15.73

C:66.18 ± 13.06

I:72.1

C:75.4

APACHE II:

I:19.37 ± 2.20

C:18.75 ± 2.12

 ①②③④

Kline.

2011 [37]

 critically ill children

I:22

C:26

I: Electromagnetic

C: Blind

I:2.1 ± 4.2

C:1.9 ± 3.0

I:32

C:63

I: Not Reported

C: Not Reported

 ①

Fang.

2005 [38]

 critically ill patients

I:50

C:50

I: Endoscopic

C: Fluoroscopic

I:52 ± 20.75

C:55 ± 19.25

I:66

C:64

Severe Neurological Impairment:

I: 44%

C: 46%

 ①②

Huerta.

2000 [39]

 critically ill patients

I:17

C:15

I: Fluoroscopic

C: Blind

I:45 ± 6

C:56 ± 5

I:76.5

C:60.0

Severe Neurological Impairment:

I:52.9%

C: 60.0%

 ①③④

Kappelle.

2018 [40]

 critically ill patients

I:82

C:73

I: Electromagnetic

C: Endoscopic

I:57.9 ± 16.8

C:56.6 ± 14.3

I:60.3

C:53.7

Mechanical Ventilation:

I: 34.2%

C: 32.9%

 ①②③④
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I Intervention group, C Control group, GCS Glasgow Coma Scale, APACHE II Acute Physiology and Chronic Health Evaluation II, SAPS II Simplified Acute Physiology Score II, PRISM Pediatric Risk of Mortality. Outcomes: ① Placement success rate; ② Insertion time; ③ Incidence rate of insertion-related complications; ④ Direct healthcare costs. Severity of Illness Surrogate Markers: For studies lacking standardized severity scores, surrogate clinical markers indicating severe patient acuity and procedural difficulty (e.g., the proportion of patients with Severe Neurological Impairment or requiring Mechanical Ventilation) were extracted

Risk of bias and certainty of evidence

Overall, the included RCTs demonstrated a relatively low to moderate risk of bias. However, the practical nature of the intubation interventions often precluded the blinding of operators and personnel, which contributed to the elevated risk of bias in specific domains, particularly in deviations from intended interventions and measurement of the outcome. Among the 19 evaluated trials, 12 trials (63.2%) exhibited a “low risk of bias”, 5 trials (26.3%) showed “some concerns”, and 2 trials (10.5%) presented a “high risk of bias” (Fig. 2).

Fig. 2.

Fig. 2

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Distribution of the methodological quality of included studies

RoB 2 findings were integrated into the CINeMA evaluation (Supplementary Part D). Certainty of evidence was high for success rate, low-to-high for complications (downgraded for imprecision and bias), and moderate-to-low for intubation time (due to heterogeneity and incoherence). A Summary of Findings (SoF) table for key comparisons is presented in Table 2.

Table 2.

Summary of Findings (CINeMA) for prmary comparisons

Outcome Intervention Relative effect (95% CrI) Certainty (CINeMA) Interpretation
Placement success rate Fluoroscopic OR 14.84 (4.27–39.75) ⊕⊕⊕⊕ High Large improvement vs. Blind
Endoscopic OR 10.02 (4.93–19.19) ⊕⊕⊕⊕ High Substantial improvement vs. Blind
Electromagnetic OR 8.04 (3.54–16.13) ⊕⊕⊕⊕ High Substantial improvement vs. Blind
Ultrasound OR 5.91 (2.68–11.54) ⊕⊕⊕⊕ High Moderate improvement vs. Blind
Procedure time Electromagnetic MD − 19.86 (− 30.55 to − 9.14) ⊕⊕⊕◯ Moderate Likely shorter procedure time
Endoscopic MD − 19.70 (− 30.37 to − 9.06) ⊕⊕⊕◯ Moderate Likely shorter procedure time
Fluoroscopic MD − 16.03 (− 35.85 to 3.91) ⊕⊕◯◯ Low May result in little to no difference
Ultrasound MD 3.74 (− 11.20 to 18.71) ⊕⊕⊕◯ Moderate Probably little to no difference vs. Blind
Complication rate Fluoroscopic OR 0.09 (0.01–0.33) ⊕⊕⊕◯ Moderate Likely fewer complications
Electromagnetic OR 0.23 (0.09–0.48) ⊕⊕⊕⊕ High Results in fewer complications
Endoscopic OR 0.42 (0.18–0.87) ⊕⊕⊕⊕ High Results in fewer complications
Ultrasound OR 0.57 (0.16–1.47) ⊕⊕◯◯ Low May result in little to no difference
Direct medical costs All vs. Blind No statistical difference ⊕⊕◯◯ Low Evidence is limited and uncertain
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OR Odds ratio, MD Mean difference, CrI Credible interval, CINeMA Confidence In Network Meta-Analysis

Analyses of outcomes

The comprehensive NMA graphs representing different NET placement methods are presented in Fig. 3, based on how the NET insertion techniques affected the placement success, insertion time, insertion-related complications incidence rate and direct healthcare costs. Importantly, the findings presented below remained robust even after excluding studies with a high risk of bias, as detailed in our sensitivity analysis (Sect.  3.5), ensuring that methodological flaws in individual trials did not skew our overall interpretation.

Fig. 3.

Fig. 3

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A NMA graph for placement success rate. B NMA graph for procedure time. C NMA graph for complication incidence rate. D NMA graph for direct healthcare costs

Primary outcome: placement success rate

The network graph illustrates all available comparisons of placement success rate from the included trials (Fig. 3). The NMA model analyzed 5 placement methods: Blind, Fluoroscopic, Electromagnetic, Ultrasound, Endoscopic. All four techniques showed higher placement success rates compared to Blind, with ORs ranging from 14.84 (95% CrI = 4.27 to 39.75) for Fluoroscopic to 5.91 (95% CrI = 2.68 to 11.54) for Ultrasound. However, there was no significant difference among the other 4 insertion techniques in placement success rate. The comparative effectiveness of various NET placement techniques regarding placement success rate is presented via SUCRA plot and league heat table (Fig. 4). The SUCRA research revealed that Fluoroscopic guidance showed the highest probability of being ranked among the most effective techniques (85.90%), followed by Endoscopic (73.01%), Electromagnetic (54.21%), Ultrasound (36.86%) and Blind (0%). However, these SUCRA rankings should be interpreted cautiously. Given that the 95% CrIs among the instrument-assisted methods widely overlapped, the probabilistic rankings do not strictly equate to definitive absolute clinical superiority.

Fig. 4.

Fig. 4

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A League heat table for placement success rate network. B SUCRA values for placement success rate network. The symbol ** in the figure indicates significant differences between the placement methods (P < 0.05)

Procedure time

In terms of procedure time 14 studies reported outcomes involving 5 different methods. Overall, the results indicated that only Electromagnetic and Endoscopic placement methods effectively reduced the procedure time compare with Blind placement. Specifically, Electromagnetic placement (MD = -19.86 min, 95% CrI = -30.55 to -9.14) and Endoscopic placement (MD = -19.70 min, 95% CrI = -30.37 to -9.06) significantly shortened the duration. In the clinical context of an intensive care unit, a procedure time reduction of approximately 20 min is clinically meaningful. It minimizes prolonged patient discomfort, decreases the duration patients must remain in a supine position or under sedation, and significantly optimizes the workflow and bedside workload for critical care personnel. Meanwhile, Fluoroscopic and Ultrasound demonstrated a negligible effect. The comparative effectiveness of various techniques in reducing procedure time was illustrated using an SUCRA plot and league heat table (Fig. 5). The SUCRA analysis indicated that Electromagnetic had the highest likelihood of being the most time-efficient technique (79.51%), closely followed by Endoscopic (79.15%).

Fig. 5.

Fig. 5

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A League heat table for procedure time network. B SUCRA values for procedure time network. The symbol ** in the figure indicates significant differences between the methods (P < 0.05)

Complication incidence rate

The network graph depicting complication incidence rate displayed comparisons from the included trials (Fig. 3). Fourteen studies examined the impact of different NET placement techniques on complications. The Bayesian model (Fig. 6) suggested lower complication probabilities with Fluoroscopic guidance compared with other techniques, with odds ratios ranging from 4.46 (95% CrI = 0.61 to 16.20) for Electromagnetic to 20.93 (95% CrI = 3.05 to 73.82) for Blind. According to the SUCRA analysis, Fluoroscopic guidance had the highest probability of being the safest insertion type (97.28%), followed by Electromagnetic (73.69%), Endoscopic (43.03%), Ultrasound (33.49%), and Blind (2.51%). Again, due to wide and partially overlapping CrIs, these safety rankings represent probabilistic hierarchies rather than definitive absolute differences among the active modalities.

Fig. 6.

Fig. 6

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A League heat table for complication incidence rate network. B SUCRA values for complication incidence rate network. The symbol ** in the figure indicates significant differences between the insertion techniques (P < 0.05)

Direct healthcare costs

Five studies evaluating direct healthcare costs included four insertion methods: Blind, Fluoroscopic, Electromagnetic, Endoscopic. The league table heatmap (Fig. 7) reveals no statistically significant differences between any type of NET placement techniques regarding direct healthcare costs. Although the SUCRA mathematical analysis ranked Fluoroscopic as having the highest probability of being economically favorable (76.92%), this finding must not be over-interpreted. Given that only five studies contributed to this economic node and all credible intervals crossed the null effect line, the current evidence is too sparse and statistically insignificant to definitively declare any single method as the most cost-effective.

Fig. 7.

Fig. 7

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A League heat table for direct healthcare costs network. B SUCRA values for direct healthcare costs network. The symbol ** in the figure indicates significant differences between the insertion techniques (P < 0.05)

Sensitivity analyses

To evaluate the robustness of our findings, we conducted two separate sensitivity analyses: (1) excluding studies involving pediatric populations (n = 2), and (2) excluding studies assessed as having a high risk of bias (n = 2). The results of both sensitivity analyses were highly consistent with the primary NMA, the SUCRA rankings for the placement techniques remained unchanged. Detailed results and comparison plots are provided in Supplementary Part F.

Discussion

This NMA thoroughly analyzed the efficacy of several different NET placement techniques for ICU patients, using the special features of NMA to conduct both direct and indirect comparisons among various insertion methods. Utilizing the four established results of interest (placement success rate, procedure time, complication incidence rate, and direct healthcare costs), we developed separate network frameworks and performed Bayesian NMA, subsequently ranking the efficacy of all included methods based on the results of the analysis.

Principal finding

This systematic review and NMA examined 19 RCTs encompassing 1,554 ICU patients. The analysis indicates that all instrument-assisted insertion methods are superior to Blind placement in terms of success rates. Fluoroscopic guidance exhibited the highest probabilistic SUCRA rankings for maximizing placement success and minimizing complications, although overlapping credible intervals suggest that other instrumented techniques may offer comparable clinical benefits. Furthermore, while mathematical rankings hinted at a potential cost-benefit for Fluoroscopic guidance, the lack of statistically significant differences and sparse data prevent any definitive economic conclusions. These results point to a substantial shift in clinical practice toward instrument-assisted techniques.

Comparison with existing literature

Previous conventional meta-analyses have underscored the advantages of instrument-assisted insertion techniques for patients, aligning with our results [3, 9, 10, 13, 41]. Nevertheless, these studies were confined to direct pairwise comparisons, lacking the establishment of a comprehensive hierarchy among all available modalities. This study builds on previous findings by using an NMA approach, which combines direct and indirect evidence to rank safety and efficacy.

For the past few decades, Fluoroscopic technology has been the “gold standard” and “final arbiter” for testing the accuracy of new placement technologies like Ultrasound and Electromagnetic [40, 42]. This NMA suggests that Fluoroscopic placement is a highly reliable method, exhibiting the highest probabilistic SUCRA rankings for both success and complication prevention. However, it should not be strictly viewed as the singular ‘optimal’ choice without considering clinical context, as its credible intervals overlap with those of other guided methods. Fluoroscopy allows continuous visualization of the guidewire trajectory during insertion in real time and with movement, which facilitates navigation through the pylorus and the ligament of Treitz [3, 9]. Additionally, patient safety is a primary consideration to think about when placing a NET. Our results indicate that Fluoroscopic guidance may help reduce the risk of insertion-related complications. Fluoroscopy, on the other hand, allows visualization of the guidewire’s path before you move it forward. This lets you find and fix any mistakes in real time. Fluoroscopy necessitates merely hydrophilic guidewire, in contrast to the endoscopic technique, which requires a cumbersome instrument, resulting in a markedly reduced occurrence of dyspnea and abdominal discomfort [3]. Nonetheless, the included studies did not evaluate the safety implications related to radiation exposure. One important ethical and safety concern associated with X-ray–based guidance is the potential risk of radiation exposure [43]. Repeated fluoroscopic procedures may expose critically ill patients to cumulative radiation doses, which should be considered when selecting placement techniques. In ICU patients requiring long-term enteral nutrition who may undergo multiple tube replacements or positional adjustments, cumulative radiation exposure may represent an additional clinical concern.

Fluoroscopic guidance demonstrated the highest probability of favorable rankings for effectiveness, but Electromagnetic and Endoscopic techniques represent a critical advancement in procedural efficiency (SUCRA 79.51% and 79.15%, respectively). Electromagnetic navigation uses weak electromagnetic sensing technology to measure the head position of the built-in magnetic guidewire catheter in real time, and plots its movement path into a trajectory line, which is presented in two and three dimensional forms on the screen [44]. Electromagnetic guidance reduces the duration by getting rid of the logistical delays that come with moving patients to the radiology suite. This makes it a suitable bedside option for ICU patients who are not stable. The field of endoscopic technology is also always changing, and recent improvements have made NET placement much more efficient. Research demonstrates that ultrathin transnasal endoscopy considerably reduces procedure duration compared to conventional endoscopy, thus enabling successful bedside insertion for critically ill patients [36]. In addition to placement, this technology enables simultaneous diagnostic upper gastrointestinal endoscopy to detect suspicious lesions, a diagnostic capability exclusive to the endoscopic method [45].

Nevertheless, our results show poor performance of Ultrasound-guided placement, particularly regarding procedure time, where it ranked even lower than the Blind technique. In recent years, critical care ultrasound technology has attracted clinical attention. It has the benefits of being easy to use, efficient, and radiation-free, and it can be done at the bedside [13, 46, 47]. However, it is hampered by significant physical limitations. Ultrasonography is more effective at penetrating solid structures, like organs, or liquids, but it has limitations when it comes to interacting with gas, which is often found in the digestive tract [48]. The visualization of the pylorus and duodenum is frequently obscured by bowel gas artifacts and depth attenuation, particularly in obese patients [49]. Additionally, Ultrasound requires the identification of a specific acoustic window, which can be time-consuming and technically challenging. These findings suggest that Ultrasound is a promising non-invasive method, which makes it less effective than other methods.

In terms of economic outcomes, the available evidence is currently insufficient to determine the most cost-effective method. Although SUCRA mathematically ranked Fluoroscopic placement highest, this must not be over-interpreted. Only five studies reported direct healthcare costs, and no statistically significant differences were observed among the techniques. Although fluoroscopy incurs initial radiology costs, its high success rate may reduce the total cost of care by minimizing repeated insertion attempts and treating of expensive complications. Conversely, Endoscopic placement was the least cost-effective method, probably because endoscopic equipment, sterilization, and specialized staff are all extremely costly [40, 50]. However, most studies focused on the procedure costs associated with short-term hospital stays, overlooking long-term comparisons, including readmission due to catheter dysfunction or costs related to malnutrition [51]. Furthermore, only five studies in our meta-analysis reported data on direct healthcare costs, these economic findings must be interpreted with caution. Consequently, the existing evidence is inadequate to ascertain a conclusive determination regarding the economic superiority of any singular method. Additional high-quality studies concentrating on cost-utility analysis are critically required to corroborate these findings.

Strengths and limitations

To our knowledge, it is the first NMA to comprehensively rank these five NET placement techniques, moving beyond simple pairwise comparisons to provide a global hierarchy of efficacy and safety. Only RCTs were included in our NMA, ensuring rigorous design and thus providing eligible quality evidence.

However, there are a number of limitations that need to be recognized. First, the sample sizes in some of the studies that were included were relatively small. These small-sample trials may not have enough statistical power, which could affect the accuracy of our network estimates. Second, only five studies had information about direct healthcare costs. This limited availability of economic data reduces the reliability and generalizability of our cost-effectiveness conclusions. Third, the use of a composite outcome for complications may obscure clinically important differences in safety profiles. In the included studies, complication rates often combined minor events (e.g., abdominal discomfort) with major adverse events (e.g., pneumothorax), which may dilute the interpretation of safety outcomes. Finally, operator expertise may have influenced the observed results. Guided placement techniques often require specific training and may involve a learning curve, and variability in operator experience across studies could have affected both success rates and procedure times. However, insufficient reporting on operator proficiency in the included studies limited further exploration of this potential confounder.

Conclusions

This NMA provides comprehensive evidence on the effectiveness of various NET insertion techniques for ICU patients, highlighting that all instrument-assisted placement types are significantly superior to Blind placement. Fluoroscopic guidance demonstrated the highest probability of favorable rankings for placement success and safety. Additionally, Electromagnetic guidance has shown the best efficacy in shortening procedure time. Clinicians should prioritize instrumental guidance to enhance patient safety and operational efficiency, tailoring the choice of method to available resources and specific patient needs.

Supplementary Information

Supplementary Material 1. (1.2MB, docx)

Acknowledgements

Not applicable.

Clinical trial number

Not applicable.

Authors’ contributions

LPY, WS designed research; LPY, PJ, HMY, WLQ, LX, WS conducted research; LPY, PJ, HMY, WS analyzed data; LPY wrote the first draft of manuscript; LPY, WS had primary responsibility for final content. Every author read and reached consensus on the final version of the manuscript. Every author made a contribution to the study’s conception or design, as well as to the data collection, analysis, and interpretation. The work was either drafted or critically revised by all authors, and the final version that was submitted for publication received their final approval. Each author pledges to take responsibility for every facet of the work, guaranteed accuracy and integrity.

Funding

Project supported by the Natural Science Foundation of Hunan, China (2026JJ81403) and Key Scientific Research Project of Changsha Central Hospital, China (YNKT202515).

Data availability

All pertinent data have been included in the manuscript. The dataset underpinning the conclusions of this paper is obtainable from the authors upon request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Wang WN, Wang CY, Hsu CY, Fu PK. Comparison of feeding efficiency and hospital mortality between small bowel and nasogastric tube feeding in critically ill patients at high nutritional risk. Nutrients. 2000;12(7):2009. [DOI] [PMC free article] [PubMed]
  • 2.Poropat G, Giljaca V, Hauser G, Štimac D. Enteral nutrition formulations for acute pancreatitis. Cochrane database Syst reviews. 2015;(3):CD010605. [DOI] [PMC free article] [PubMed]
  • 3.Lu G, Xiang Q, Wang S, Pan M, Xiang X, Yang Y, Shi X. Endoscopic-versus x-ray-guidance for placement of nasojejunal tubes in critically ill patients: a systematic review and meta-analysis of randomized controlled trials. Am J Translational Res. 2022;14(4):2134. [PMC free article] [PubMed] [Google Scholar]
  • 4.Smith AL, Santa Ana CA, Fordtran JS, Guileyardo JM. Deaths associated with insertion of nasogastric tubes for enteral nutrition in the medical intensive care unit: Clinical and autopsy findings. Proc (Bayl Univ Med Cent). 2018;31(3):310–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Turner AD, Hamilton SM, Callif C, Ariagno KA, Arena AE, Mehta NM, Martinez EE. Bedside Postpyloric Tube Placement and Enteral Nutrition Delivery in the Pediatric Intensive Care Unit. Nutr Clin Pract. 2020;35(2):299–305. [DOI] [PubMed] [Google Scholar]
  • 6.Kolbeinsson HM, Veldkamp J, Paauw JD. Fluoroscopic placement of nasojejunal feeding tubes in COVID-19 patients in the prone position. JPEN J Parenter Enter Nutr. 2022;46(3):556–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Zheng Z, Wang J, Shao Z, Cai H, Lu L, Tang S, Xu S, Gong F, Ye S, Yang X, Liu J. Multivariate analysis of factors associated with the successful prediction of initial blind placement of a nasointestinal tube in the stomach based on X-ray imaging: a retrospective, single-center study. BMC Gastroenterol. 2024;24(1):284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hu HT, Ma FH, Wu ZM, Qi XH, Zhong YX, Xie YB, Tian YT. Treatment of afferent loop syndrome using fluoroscopic-guided nasointestinal tube placement: Two case reports. World J Clin Cases. 2020;8(21):5353–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Zhu Y, Yin H, Zhang R, Ye X, Wei J. Endoscopy versus fluoroscopy for the placement of postpyloric nasoenteric tubes in critically ill patients: A meta-analysis of randomized controlled trials. J Crit Care. 2016;33:207–12. [DOI] [PubMed] [Google Scholar]
  • 10.Watanabe J, Kakehi E, Okamoto M, Ishikawa S, Kataoka Y. Electromagnetic-guided versus endoscopic-guided postpyloric placement of nasoenteral feeding tubes. Cochrane Database Syst Rev. 2022;10(10):CD013865. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Wang Q, Lu Z, Cai J, Sun L, Zhao Q, Zhu Y, Zhu C. Bedside Ultrasound-guided Nasointestinal Tube Placement in Critically Ill Patients in Intensive Care Unit. Altern Ther Health Med. 2023;29(8):178–82. [PubMed] [Google Scholar]
  • 12.Chen Y, Wu G, Qu C, Ye Z, Kang Y, Tian X. A multifaceted comparative analysis of image and video technologies in gastrointestinal endoscope and their clinical applications. Front Med (Lausanne). 2023;10:1226748. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Jin Z, Wei Y, Shen G, Zhang X. Electromagnetic-guided versus endoscopic placement of nasoenteral feeding tubes: protocol for a systematic review and meta-analysis. BMJ Open. 2021;11(3):e044637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Bucher HC, Guyatt GH, Griffith LE, Walter SD. The results of direct and indirect treatment comparisons in meta-analysis of randomized controlled trials. J Clin Epidemiol. 1997;50(6):683–91. [DOI] [PubMed] [Google Scholar]
  • 15.Zhao T, Tang C, Yan H, Lu Q, Guo M, Wang H. Comparative efficacy and acceptability of non-pharmacological interventions for depression in people living with HIV: A systematic review and network meta-analysis. Int J Nurs Stud. 2023;140:104452. [DOI] [PubMed] [Google Scholar]
  • 16.Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, Ioannidis JP, Straus S, Thorlund K, Jansen JP, Mulrow C, Catalá-López F, Gøtzsche PC, Dickersin K, Boutron I, Altman DG, Moher D. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777–84. [DOI] [PubMed] [Google Scholar]
  • 17.Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, Cates CJ, Cheng HY, Corbett MS, Eldridge SM, Emberson JR, Hernán MA, Hopewell S, Hróbjartsson A, Junqueira DR, Jüni P, Kirkham JJ, Lasserson T, Li T, McAleenan A, Reeves BC, Shepperd S, Shrier I, Stewart LA, Tilling K, White IR, Whiting PF, Higgins JPT. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. [DOI] [PubMed] [Google Scholar]
  • 19.Karahalios A, McKenzie JE, White IR. Contrast-Based and Arm-Based Models for Network Meta-Analysis. Methods Mol Biol. 2022;2345:203–21. [DOI] [PubMed] [Google Scholar]
  • 20.Shim SR, Kim SJ, Lee J, Rücker G. Network meta-analysis: application and practice using R software. Epidemiol Health. 2019;41:e2019013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Zhang J, Liu M, Yue J, Yang J, Xiao Y, Yang J, Cai E. Effects of virtual reality with different modalities on upper limb recovery: a systematic review and network meta-analysis on optimizing stroke rehabilitation. Front Neurol. 2025;16:1544135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Béliveau A, Boyne DJ, Slater J, Brenner D, Arora P. BUGSnet: an R package to facilitate the conduct and reporting of Bayesian network Meta-analyses. BMC Med Res Methodol. 2019;19(1):196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Li LQ, Dong Zb, Li HB, Guan YX, Dai XX, Zhang H, Lu DY, Wang ML, Zhang YQ. Application effects of visualization of nasoenteric tube placement innutritional support for patients with severe traumatic brain injury. J Zhengzhou Univ (Medical Sciences). 2024;59(06):851–4. [Google Scholar]
  • 24.Ma LX, Nie BB, Jin G, Deng WJ, Sun DL, Qu HM, Ren HL. Comparison of the application effects of electromagnetic guidance and bedside ultrasound in the placement of nasointestinal feeding tubes in neurocritical care patients. Chin J Integr Traditional Western Med Intensive Crit Care. 2024;31(5):566–71. [Google Scholar]
  • 25.Zhang M, Lu SY, Han XY, Wu ZS, Yang J, Chen JF. Zhen F.A study on the application of integrated real-time imaging in nasoenteric tube placement in adult critically ill patients. Parenter Enter Nutr. 2023;30(6):357–62. [Google Scholar]
  • 26.Shang XY. Application of enteral nutrition in patients with traumatic brain injury in ICU through nasointestinal tube water injecting in stomach by ultrasound. J North Sichuan Med Coll. 2017;32(1):60–3. [Google Scholar]
  • 27.Shen GG, Jiang XG, Lu WH, Wu JY, Wang J, Jin XJ. Clinical Effects of the Placement of Nose-jejunum Nutrition Tube Guided by X-ray and Endoscopy on Critically Ill Patients. Chin Gen Pract. 2012;15(29):396–3398. [Google Scholar]
  • 28.Wang SY, Li R, Si AB, Ma D, Wang XM. Study on application effect of visual nasointestinal tube catheterization in patients with severe neurological diseases. J Med Forum. 2024;45(24):2635–9. [Google Scholar]
  • 29.Cao H, Jia WS, Zhang Q, Liu CC, Zhang MM. Application analysis of ultrasound-guided nasointestinal tube placement combined with gastric motility visualization in ICU patients. J Med Imaging. 2024;34(10):88–91. [Google Scholar]
  • 30.Wei WG, Wang Cy, Tang XN, Lei QL, Lv H. The application effect of bedside nasojejunal tube insertion technique based on electromagnetic navigation in critically ill patients in the ICU. Guangxi Med J. 2024;46(12):1955–8. [Google Scholar]
  • 31.Hou x, Sun DY, Li Q, Ji Q. Application of electromagnetic navigation nasointestinal tube insertion technology to critically ill neurosurgical patients. Chin J Neurosurgical Disease Res. 2024;18(4):56–60. [Google Scholar]
  • 32.Gao X, Zhang L, Zhao J, Tian F, Sun H, Wang P, Wang J, Wang Z, Wang X. Bedside electromagnetic-guided placement of nasoenteral feeding tubes among critically Ill patients: A single-centre randomized controlled trial. J Crit Care. 2018;48:216–21. [DOI] [PubMed] [Google Scholar]
  • 33.Holzinger U, Brunner R, Miehsler W, Herkner H, Kitzberger R, Fuhrmann V, Metnitz PG, Kamolz LP, Madl C. Jejunal tube placement in critically ill patients: A prospective, randomized trial comparing the endoscopic technique with the electromagnetically visualized method. Crit Care Med. 2011;39(1):73–7. [DOI] [PubMed] [Google Scholar]
  • 34.Jha P, Rupp L, Bonilla L, Gelfond J, Shah JN, Meyer AD. Electromagnetic Versus Blind Guidance of a Postpyloric Feeding Tube in Critically Ill Children. Pediatrics. 2020;146(4):e20193773. [DOI] [PubMed] [Google Scholar]
  • 35.Foote JA, Kemmeter PR, Prichard PA, Baker RS, Paauw JD, Gawel JC, Davis AT. A randomized trial of endoscopic and fluoroscopic placement of postpyloric feeding tubes in critically ill patients. JPEN J Parenter Enter Nutr. 2004;28(3):154–7. [DOI] [PubMed] [Google Scholar]
  • 36.Chen Y, Tian X, Liu C, Zhang L, Xv Y, Xv S. Application of visual placement of a nasojejunal indwelling feeding tube in intensive care unit patients receiving mechanical ventilation. Front Med (Lausanne). 2022;9:1022815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Kline AM, Sorce L, Sullivan C, Weishaar J, Steinhorn DM. Use of a noninvasive electromagnetic device to place transpyloric feeding tubes in critically ill children. Am J Crit Care. 2011;20(6):453–9. quiz 460. [DOI] [PubMed] [Google Scholar]
  • 38.Fang JC, Hilden K, Holubkov R, DiSario JA. Transnasal endoscopy vs. fluoroscopy for the placement of nasoenteric feeding tubes in critically ill patients. Gastrointest Endosc. 2005;62(5):661–6. [DOI] [PubMed] [Google Scholar]
  • 39.Huerta G, Puri VK. Nasoenteric feeding tubes in critically ill patients (fluoroscopy versus blind). Nutrition. 2000;16(4):264–7. [DOI] [PubMed] [Google Scholar]
  • 40.Kappelle WFW, Walter D, Stadhouders PH, Jebbink HJA, Vleggaar FP, van der Schaar PJ, Kappelle JW, van der Tweel I, Van den Broek MFM, Wessels FJ, Siersema PD, Monkelbaan JF. Electromagnetic-guided placement of nasoduodenal feeding tubes versus endoscopic placement: a randomized, multicenter trial. Gastrointest Endosc. 2018;87(1):110–8. [DOI] [PubMed] [Google Scholar]
  • 41.Mancini FC, de Moura DTH, Funari MP, Ribeiro IB, Neto FLP, Mendieta PJO, McCarty TR, Bernardo WM, Nahas SC, de Moura EGH. Use of an electromagnetic-guided device to assist with post-pyloric placement of a nasoenteral feeding tube: A systematic review and meta-analysis. Endosc Int Open. 2022;10(8):E1118–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Re R, Lassola S, De Rosa S. Placement and Verification of the Nasogastric Tube: An In-depth Analysis of Ultrasonographic Technique. Dimens Crit Care Nurs. 2025;44(5):239–45. [DOI] [PubMed] [Google Scholar]
  • 43.Hiles P, Gilligan P, Damilakis J, Briers E, Candela-Juan C, Faj D, Foley S, Frija G, Granata C, de Gala LH, Pauwels H, Sans Merce R, Simantirakis M, Vano G. European consensus on patient contact shielding. Insights Imaging. 2021;12(1):194. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Powers J, Luebbehusen M, Aguirre L, Cluff J, David MA, Holly V, Linford L, Park N, Brunelle R. Improved Safety and Efficacy of Small-Bore Feeding Tube Confirmation Using an Electromagnetic Placement Device. Nutr Clin Pract. 2018;33(2):268–73. [DOI] [PubMed] [Google Scholar]
  • 45.Long C, Yu Y, Cui B, Jagessar SAR, Zhang J, Ji G, Huang G, Zhang F. A novel quick transendoscopic enteral tubing in mid-gut: technique and training with video. BMC Gastroenterol. 2018;18(1):37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Liu Z, Guo J, Ren W, Tang S, Huang Y, Huang L, Sun S, Lin L. Evaluation of ultrasound-guided Freka-Trelumina enteral nutrition tube placement in the treatment of acute pancreatitis. BMC Gastroenterol. 2020;20(1):21. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Wu N, Shen H. Safety and effectiveness of dual guidance with video-laryngoscopy and ultrasound in jejunal tube placement in patients undergoing invasive mechanical ventilation. Ann Palliat Med. 2021;10(3):3128–34. [DOI] [PubMed] [Google Scholar]
  • 48.Ferraboli SF, Beghetto MG. Bedside ultrasonography for the confirmation of nasogastric tube placement: agreement between nurse and physician. Rev Gaucha Enferm. 2022;43(spe):e20220211. [DOI] [PubMed] [Google Scholar]
  • 49.Li G, Pan Y, Zhou J, Tong Z, Ke L, Li W. Enteral nutrition tube placement assisted by ultrasonography in patients with severe acute pancreatitis: A novel method for quality improvement. Med (Baltim). 2017;96(45):e8482. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Galaski A, Peng WW, Ellis M, Darling P, Common A, Tucker E. Gastrostomy tube placement by radiological versus endoscopic methods in an acute care setting: a retrospective review of frequency, indications, complications and outcomes. Can J Gastroenterol. 2009;23(2):109–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Ribeiro FA, Sodré da Costa LS, Pedroso AC, de Paula Nogueira PB, Brandi S, Toledo DO, Laselva CR, Malheiro DT, Silva JM Jr. Evaluating multifaceted strategies to prevent nasoenteral tube complications and achieve significant cost savings in critically ill patients: the ENHANCE-CRIT trial. BMJ Open Qual. 2025;14(1):e003177. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (1.2MB, docx)

Data Availability Statement

All pertinent data have been included in the manuscript. The dataset underpinning the conclusions of this paper is obtainable from the authors upon request.

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