Pediatrics

Advanced airway management for pediatric out-of-hospital cardiac arrest: A systematic review and network meta-analysis

a b s t r a c t

Objectives: Although airway management is important in pediatric resuscitation, the effectiveness of Bag-mask ventilation (BMV) and advanced airway management (AAM), such as Endotracheal intubation and Supraglottic airway devices, for prehospital resuscitation of Pediatric out-of-hospital cardiac arrest (OHCA) remains unclear. We aimed to determine the efficacy of AAM during prehospital resuscitation of pediat- ric OHCA cases.

Methods: We searched four databases from their inception to November 2022 and included randomized con- trolled trials and observational studies with appropriate adjustments for confounders that evaluated prehospital AAM for OHCA in children aged <18 years in quantitative synthesis. We compared three interventions (BMV, ETI, and SGA) via Network meta-analysis using the GRADE Working Group approach. The outcome measures were survival and favorable neurological outcomes at hospital discharge or 1 month after cardiac arrest.

Results: Five studies (including one clinical trial and four cohort studies with rigorous confounding adjustment) involving 4852 patients were analyzed in our quantitative synthesis. Compared with ETI, BMV was associated with survival (relative risk [RR] 0.44 [95% confidence intervals (CI) 0.25-0.77]) (very low certainty). There were no significant association with survival in the other comparisons (SGA vs. BMV: RR 0.62 [95% CI 0.33-1.15] [low certainty], ETI vs. SGA: RR 0.71 [95% CI 0.39-1.32] [very low certainty]). There was no significant association with Favorable neurological outcomes in any comparison (ETI vs. BMV: RR 0.33 [95% CI 0.11-1.02]; SGA vs. BMV: RR 0.50 [95% CI 0.14-1.80]; ETI vs. SGA: RR 0.66 [95% CI 0.18-2.46]) (all very low certainty). In

the ranking analysis, the hierarches for efficacy for survival and favorable neurological outcome were BMV > SGA > ETI.

Conclusion: Although the available evidence is from observational studies and its certainty is low to very low, pre- hospital AAM for pediatric OHCA did not improve outcomes.

(C) 2023

  1. Introduction

Although Pediatric cardiac arrest is rare, it significantly impacts pub- lic health because of the years of life that could be lost and the length of

Abbreviations: OHCA, out-of-hospital cardiac arrest; BMV, bag-mask ventilation; AAM, advanced airway management; ETI, endotracheal intubation; SGA, supraglottic airway; RCT, Randomized controlled trial; RR, relative risk; CI, confidence interval; OR, odds ratio; HR, hazard ratio.

* Corresponding author at: Division of Emergency and transport services, National Center for Child Health and Development, 2-10-1, Okura, Setagaya-ku, Tokyo 157-8535, Japan.

E-mail address: [email protected] (S. Amagasa).

time that a child may live with the aftereffects [1,2]. Survival rates of children who experience out-of-hospital cardiac arrest (OHCA) range from 2 to 22% [2-8]. Therefore, improvement of survival rates and neu- rological outcomes in pediatric OHCA cases is essential.

Airway management is important in pediatric resuscitation and in- cludes bag-mask ventilation (BMV) and Advanced airway management , such as Endotracheal intubation and Supraglottic airway devices [9]. AAM may provide more Effective airway manage- ment than BMV; however, airway placement may interrupt chest com- pressions, and failure to secure the airway may occur due to a difficult intubation [10,11]. A 2019 systematic review/meta-analysis comparing AAM and BMV reported that AAM is not superior to BMV during resus- citation for cardiac arrest in children [12]. However, the meta-analysis

https://doi.org/10.1016/j.ajem.2023.03.049

0735-6757/(C) 2023

Bias assessment within indi”>included cases of in-hospital and OHCA and did not adequately examine AAM types in detail. The 2020 American Heart Association Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care recommended that BMV is reasonable compared with advanced airway interventions in the management of OHCA in children [13]. Meanwhile, the European Resuscitation Council Guidelines 2021 rec- ommended that BMV to be the first-line ventilation method, ETI to be performed while considering the risks, and SGA to be an alternative to ETI [14]. Although several high-quality observational studies examining AAM in the prehospital setting have been published after the 2019 meta-analysis, there are no meta-analyses examining ETI, SGA, and BMV for prehospital resuscitation of pediatric OHCA, and the effective- ness of each is unknown.

In order to compare the efficacy of AAM for prehospital resuscitation of pediatric OHCA, we conducted a systematic review and network meta-analysis to evaluate the relative efficacy of ETI, SGA, and BMV.

  1. Materials and methods
    1. Protocol and registration

This systematic review was designed based on the Preferred Reporting Items for Systematic Review and Meta-Analyses extension statements for reporting systematic reviews that incorporate network meta-analysis [15]. The review protocol was registered in PROSPERO (CRD42022321109). At the time of the PROSPERO registry, the review included studies on AAM during cardiopulmonary resuscitation for both in-hospital cardiac arrest and OHCA; however, when conducting the review, we considered that AAM studies of prehospital cardiopul- monary resuscitation for OHCA would provide less heterogeneity and higher quality network meta-analysis. Therefore, this review examined AAM during prehospital resuscitation for OHCA.

    1. Studies, participants, interventions/comparators, and outcomes

Randomized controlled trials , prospective and retrospective non-randomized studies with a control group, interrupted time series, controlled before-and-after studies, and cohort studies in all languages were included in this study. Additionally, RCTs and observational stud- ies that were appropriately adjusted for confounders were included in the quantitative analysis by network meta-analysis, while the other studies were included in the qualitative assessments. We excluded crossover trials, case reports or case series, review articles, editorials, and commentaries. Pediatric patients aged <18 years who experienced OHCA were included in this meta-analysis. Studies on neonates with perinatal asphyxia were excluded. We included studies that compared at least two of the three airway interventions (ETI, SGA, and BMV) dur- ing prehospital resuscitation for OHCA, and network meta-analysis was utilized to compare the three groups.

The primary outcome was the survival rate at discharge or at the lon- gest point described in the studies. The secondary outcome was the rate of favorable neurological outcomes (defined as a Pediatric Cerebral Per- formance Category 1-2, Cerebral Performance Category 1-2, Glasgow Outcome Scale 4-5, VABS-II score >= 70, no disability, no change from baseline, mild disability) at discharge or at the longest point described in the studies [16-18].

    1. Data sources and search details

The Cochrane Central Register of Controlled Trials and MEDLINE via Ovid were searched for eligible published trials. The World Health Orga- nization International Clinical Trials Platform Search Portal and http:// ClinicalTrials.gov trial registries were also searched for ongoing trials. If data were missing, we attempted to contact the authors of the study. The search was carried out on November 1, 2022. Details of the search strategy and the performed searches are presented in Appendix.

    1. Study selection, data collection process, and data items

Using EndNote software (Thomson Reuters, Toronto, Ontario, Canada), citations were recorded and duplicates were removed. Rayyan software was used for the systematic review [19]. Two authors (SU and TM) individually examined the titles and/or summaries of studies re- trieved using the search function as well as summaries of studies from additional sources to determine whether they fulfilled the selection criteria outlined above. The two authors (SU and TM) separately col- lected the full texts of potentially eligible studies and evaluated their el- igibility. Any differences regarding research eligibility were discussed with a third reviewer (SA) and settled. A standardized pre-pilot form was used to extract data from the included studies and to assess the quality of the research and synthesis of the evidence. The baseline char- acteristics of the study population and participants, specifics of the in- tervention and control conditions, study methods, results and timepoints of measurement, and data to assess the risk of bias were ex- tracted for analysis. Data extraction was carried out independently by two review authors (SU and TM), and any differences were discussed with a third author (SA), as necessary.

    1. Risk of bias assessment within individual studies

We evaluated the risk of bias in the RCTs using the Cochrane Risk of Bias tool 2.0, including bias arising from the randomization process, bias due to deviations from the intended interventions, bias due to missing outcome data, bias in the measurement of the outcomes, and bias in the selection of the reported results [20]. For each of these categories, we then classified each study as having low risk, some concerns, or high risk of bias. Disagreements between the two authors were resolved through discussions involving the third author.

We evaluated the risk of bias of non-RCTs using the ROBINS-I tools for non-RCT risk of bias assessment (observational studies) in various domains; namely, bias due to confounding factors, bias in the selection of the study participants, bias in the classification of interventions, bias due to deviations from the intended interventions, bias due to missing data, bias in the measurement of outcomes, and bias in the selection of the reported results [21]. Depending on whether there was informa- tion available about bias, we categorized each study as having a low, moderate, serious, or critical risk of bias. Disagreements between the two authors were resolved through discussions involving the third author.

    1. Statistical analysis

Network meta-analysis is a method of meta-analysis that simulta- neously addresses the comparative effectiveness and safety of multiple interventions by linking >=3 interventions to form a network and inte- grating their direct and indirect comparisons. In the present study, net- work meta-analysis was used to create a network from multiple comparisons of two of the three interventions (ETI, SGA, and BMV), combine indirect evidence with direct evidence, increase the accuracy of comparisons, and compare and rank the three interventions simultaneously.

A network plot was constructed to identify the number of studies and patients included in the meta-analysis. The “netmeta 2.5-0” R pack- age (version 4.1.2) was used to conduct the network meta-analysis. The frequency-based technique with multivariate random effects meta- analysis was used, and the effect sizes were represented as Relative risk with 95% confidence intervals (CI). If the outcome was pre- sented as odds ratios (OR) or hazard ratios (HR) rather than RR, OR and HR were converted to RR using the approximation recommended by VanderWeele [22]. The certainty of evidence of the network effect es- timate was evaluated using the Grading of Recommendations, Assess- ment, Development, and Evaluation working group approach of the network meta-analysis [23]. Data from clinical trials and cohort studies

with high-quality comparisons that were appropriately adjusted for confounders were synthesized. Data from the studies that have not been appropriately adjusted for confounders, such as simple observa- tional studies or studies with exploratory multivariate analysis, were not synthesized; only qualitative assessments were performed on these studies.

The underlying transitivity assumptions of the network meta- analysis were evaluated by comparing the distributions of clinical and methodological variables that may act as effect modifiers across treat- ment comparisons. The risk of bias between studies was evaluated after the considerations of pairwise meta-analyses. The conditions asso- ciated with “suspicious” and “undetected” bias across studies were de- termined by the presence of publication bias as indicated by direct comparisons. Each study in the network was evaluated for its indirect- ness in relation to the research topic while considering the study popu- lation, intervention, outcome, and study setting; indirectness was then classified as low, medium, or high. A contribution matrix was then cre- ated using judgments made at the study level. The method for address- ing imprecision involved contrasting the range of equivalency with the range of treatment effects covered by the 95% CI. Heterogeneity of the treatment effect for clinically important risk ratios (<0.8 or >1.25) in the CI was assessed. We contrasted the posterior distribution of the es- timated heterogeneity variance with its predicted distribution to deter- mine the degree of heterogeneity [24]. The importance of heterogeneity with and without capturing heterogeneity was evaluated using CI- based assessment and agreement between the prediction intervals. When the clinically significant risk ratio was between 0.8 and 1.25 in the prediction interval, heterogeneity of the treatment effect was evalu- ated. A design-by-design interaction test was used to statistically evalu- ate consistency, and inconsistencies in the network model were estimated from the inconsistency factors and their uncertainty [25]. There were no discrepancies across sources of evidence for comparisons informed simply by direct evidence; as a result, “no risk” about contra- diction. When only circumstantial evidence was used, there were “some

worries.” When the p-value of the interaction test for each treatment was <0.01, “major concern” was considered.

The P-score was derived using the network’s point estimate and standard error as a ranking analysis. The average level of certainty that a treatment is superior to all others can be calculated from its P-score.

    1. Sensitivity analysis

We performed analysis considering the type of study. Analysis lim- ited to RCTs excluded high risk of bias, and analysis observational stud- ies the excluded critical and serious risk of bias was also performed.

  1. Results
    1. Study selection

We identified 6665 eligible articles. After removing duplicates, we screened 6303 articles (Fig. 1); 145 articles underwent full-text review. We tried contacting the researchers of an unpublished trial that was registered on http://ClinicalTrials.gov but there was no response. Mean- while, 5 and 14 studies were included in the quantitative (network meta-analysis) and qualitative analysis, respectively.

    1. Study characteristics

The characteristics of the included studies are shown in Tables 1 and 2 and Supplementary Table 1. Of the five studies included in the net- work meta-analysis, one was a clinical trial [26], two were propensity score-matched cohort studies [27,28], and two were cohort studies using inverse probability of treatment weighting method [29,30]. Meanwhile, 14 observational studies that did not meet the criteria for quantitative analysis were subjected to qualitative analysis [31-44]; the reasons for not including these studies in the quantitative analysis are given in Table 2.

Image of Fig. 1

Fig. 1. Flowchart of included studies.

Overall, 4852 patients were included in the network meta-analysis. The studies were published between 2000 and 2022, and patient re- cruitment was conducted from 1994 to 2018. One study compared ETI with BMV, two studies compared ETI with SGA, and two studies com- pared ETI with BMV and SGA with BMV. The number of patients who re- ceived either ETI, SGA, or only BMV was 2555 (52.7%), 889 (18.3%), or

1408 (29.0%), respectively.

    1. Risk of bias within individual studies

The risk of bias within the included studies is shown in Supplemen- tary Tables 2-5. The risk of bias of one clinical trial was high due to the randomization process, whereas that of the four observational studies was determined to be moderate.

    1. Survival at discharge or the longest point described in the studies

Survival was reported in five studies representing 4852 patients. The network plot for survival is shown in Fig. 2, while pairwise comparisons are shown in Supplementary Fig. 1. Network estimates are shown in Table 3 and Fig. 3. Compared with ETI, BMV was more strongly associ- ated with survival (RR 0.44 [95% CI 0.25-0.77]). Meanwhile, we did not find any significant association in the other comparisons. A sum- mary of the confidence in the network estimates and detailed assess- ment of estimates from the network meta-analysis are shown in Supplementary Tables 6 and 7. The certainty of evidence in the network estimates was low for SGA vs. BMV and very low for ETI vs. BMV and ETI vs. SGA. The P-scores for survival are shown in Table 5. The hierarchy for efficacy in survival was BMV (P-score = 0.99) > SGA (P-score = 0.49) > ETI (P-score = 0.007). Outcomes and effect sizes for qualitative analysis are shown in Table 2.

    1. Favorable neurological outcomes at discharge or the longest point described in the studies

Favorable neurological outcomes at discharge or the longest point were reported in five studies representing 4852 patients. The network plot for favorable neurological outcomes is displayed in Fig. 2, while pairwise comparisons are shown in Supplementary Fig. 2. Network esti- mates are shown in Table 4 and Fig. 4. We did not find any significant association between any of the methods and favorable neurological out- comes. A summary of the confidence in the network estimates and de- tailed assessment of estimates from the network meta-analysis are shown in Supplementary Table 6 and 8. The certainty of evidence in the network estimates was very low for all three comparisons. The P-scores for favorable neurological outcomes are shown in Table 5. The hierarchy for efficacy of survival was BMV (P-score = 0.99) > SGA (P-score = 0.49) > ETI (P-score = 0.01). Outcomes and effect sizes for qualitative analysis are shown in Table 2.

    1. Sensitivity analysis

As an analysis by type of study, network meta-analysis of the four high-quality observational studies was performed because there was only one clinical trial. The analysis excluding studies with high risk of bias was not performed because one clinical trial was excluded, and the analysis was based on four observational studies.

Pairwise comparisons are shown in Supplementary Fig. 3 and 4, while the network plot for survival and favorable neurological out- comes is shown in Supplementary Fig. 5. Network estimates of survival and favorable neurological outcomes at discharge or the longest point described in the studies are shown in Supplementary Tables 9 and 10 and Supplementary Fig. 6 and 7. BMV was associated with survival

Table 1

Study characteristics I.

Study

Years of recruitment

Country of recruitment

Study type

Intervention

Comparator

Number of patients

Intervention

Comparator

Studies included in quantitative synthesis

Gausche 2000

1994-1997

US

RCT

ETI

BMV

591

301

290

dy dy dy dy

ETI/SGA

BMV

1723

727/215

781

ETI

SGA

226?

113

113

ETI

SGA

1579

1355

224

ETI/SGA

BMV

733+

59/337

337

Hansen 2017

2013-2015

US Observational stu

PS-IPTW

Fukuda 2020

2011-2017

Japan Observational stu PS-matching

Le Bastard 2021

2011-2018

French Observational stu

PS-IPTW

Tham 2022

2009-2018

Asian countries?? Observational stu

Studies not being included in quantitative synthesis but included in qualitative analysis

PS-matching

Losek 1987

1983-1985

US

Observational study

ETI

BMV

112

75

37

Aijan 1989

1984-1987

US

Observational study

ETI

BMV

42

28?

14

Pitetti 2002

1995-1999

US

Observational study

ALS?

BLS

189

150

39

Abe 2012??

2005-2008

Japan

Observational study

AAM

BMV

3189

455

2734

Deasy 2012

1999-2007

Australia

Observational study

ETI

BMV

193

167

26

Tijssen 2015

2005-2012

US and Canada

Observational study

AAM

BMV

2244

1558

686

Zwingmann 2015

1993-2013

German

Observational study

ETI

BMV

142

120

22

Fink 2016

2007-2012

US and Canada

Observational study?

ETI/SGA

BMV

3228

900/82

1346

Oishi-Fukuda 2017

2011-2012

Japan

Observational study??

AAM

BMV

692*

346

346

Nehme 2018

2000-2016

Australia

Observational study

ETI

BMV

948

655

293

Okubo 2019

2014-2016

Japan

Observational study??

ETI/SGA

BMV

852*

60/396

396

Lee 2019

2005-2016

Taiwan

Observational study

AAM

BMV

152

62

90

Hansen 2020

2016-2018

US

Observational study

ETI/SGA

BMV

155

73/19

63

Cheng 2021

2015-2019

Taiwan

Observational study?

AAM

BMV

124

53

71

RCT, Randomized controlled trial; BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway; AAM, advanced airway management; PS, propensity score; ALS, advanced life support.

* Propensity score matched cohort.

?? Japan, Korea, Malaysia, Singapore, Taiwan, Thailand, UAE, India, China, Philippines, and Vietnam.

+ Before propensity score matching. propensity matched cohort ETI/BMV 59/59, SGA/BMV 337/337.

? include attempted ETI.

? 92% of patients with ALS were successfully intubated.

?? age < 20.

? Exploratory Multivariate Analysis.

?? propensity score matching.

Table 2

Study characteristics II: outcomes and reasons for exclusion from quantitative synthesis.

Study Outcome Intervention

outcome

Comparator outcome

Effect size Reasons for exclusion from quantitative synthesis

Randomized controlled trial

Gausche 2000

survival at hospital discharge

ETI 24/301

BMV 24/290

OR 0.96 (0.53-1.73)

NA

Favorable neurological outcome at hospital discharge

ETI 15/301

BMV 10/290

OR 1.47 (0.65-3.32)

NA

Cohort studies included in quantitative synthesis

Hansen 2017

Survival at hospital discharge

ETI 51/727

BMV 110/781

aOR 0.39 (0.26-0.59)

NA

Favorable neurological outcome at hospital discharge

ETI 34/727

BMV 89/781

aOR 0.30 (0.18-0.50)

NA

Survival at hospital discharge

SGA 22/215

BMV 110/781

aOR 0.32 (0.12-0.84)

NA

Favorable neurological outcome at hospital discharge

SGA 13/215

BMV 89/781

aOR 0.14 (0.05-0.39)

NA

Fukuda 2020

Survival at 1 month

ETI 13/113

SGA 12/113

RR 1.08 (0.52-2.27)

NA

Favorable neurological outcome at 1 month

ETI 2/113

SGA 1/113

RR 2.00 (0.18-21.75)

NA

Le Bastard 2021

Survival at 1 month

ETI 104/1355

SGA 32/224

aOR 0.39 (0.25-0.62)

NA

Favorable neurological outcome at 1 month

ETI 63/1355

SGA 25/224

aOR 0.32 (0.19-0.54)

NA

Tham 2022

Survival at 1 month

ETI 4/59

BMV 8/59

aOR 0.46 (0.13-1.63)

NA

Favorable neurological outcome at 1 month

ETI 3/59

BMV 6/59

aOR 0.47 (0.11-1.99)

NA

Survival at 1 month

SGA 24/337

BMV 52/337

aOR 0.41 (0.25-0.69)

NA

Favorable neurological outcome at 1 month

SGA 5/337

BMV 31/337

aOR 0.14 (0.05-0.37)

NA

Cohort studies with appropriate confounding adjustment not being included in quantitative synthesis but included in qualitative analysis

Oishi-Fukuda 2017 Survival at 1 month AAM 51/346 BMV 37/346 aOR 0.74 (0.35-1.59) Intervention is AAM only.

Favorable neurological outcome at 1 month AAM 12/346 BMV 16/346 aOR 1.44 (0.92-2.27)

Okubo 2019 Survival at 1 month ETI 6/60 BMV 8/60 RR 0.55 (0.19-1.63) Patients in this study are

Favorable neurological outcome at 1 month ETI 0/60 BMV 4/60 NA

Survival at 1 month SGA 46/396 BMV 36/396 RR 1.32 (0.86-2.04) Favorable neurological outcome at 1 month SGA 9/396 BMV 6/396 RR 1.38 (0.45-4.26)

Cohort study with exploratory multivariate analysis

included in Fukuda 2020. However, this study approaches resuscitation time bias through time-dependent propensity score matching.

Deasy 2012

Survival at hospital discharge

ETI 13/167

BMV 2/26

aOR 1.01 (0.21-4.77)

Adjustment for confounding

factors were inadequate.

Tijssen 2015

Survival at hospital discharge

AAM NA

BMV NA

aOR 0.69 (0.43-1.10)

Intervention is AAM only.

Adjustment for confounding

factors were inadequate.

Zwingmann 2015

Survival at hospital discharge

ETI 78/120

BMV 21/22

aOR 0.037 (0.003-0.53)

Adjustment for confounding

factors were inadequate.

Fink 2016

Survival at hospital discharge

AAM NA

BMV NA

aOR 0.64 (0.37-1.13)

Intervention is AAM only.

Adjustment for confounding

factors were inadequate.

Nehme 2018

Survival at hospital discharge

ETI NA

BMV NA

aOR 2.74 (1.18-6.33)

Adjustment for confounding

factors were inadequate.

Cheng 2021

Survival at hospital discharge

AAM 15/53

BMV 11/71

aOR 8.952 (1.414-66.08)

Intervention is AAM only.

Adjustment for confounding

factors were inadequate.

Cohort studies without adjustment for confounding factors

Losek 1987

Survival at hospital discharge

ETI 8/75

BMV 1/37

RR 3.94 (0.69-24.18)

Confounding factors were

unadjusted.

Aijan 1989

Survival at hospital discharge

ETI 1/28

BMV 1/14

RR 0.50 (0.05-4.72)

Confounding factors were

unadjusted.

Pitetti 2002

Survival at hospital discharge

ALS 5/150

BLS 0/39

NA

Confounding factors were

unadjusted.

Abe 2012

Survival at 1 month

AAM 21/45

BMV 243/2734

RR 5.25 (3.65-7.00)

Intervention is AAM only.

Confounding factors were

unadjusted.

Lee 2019

Survival at hospital discharge

AAM 8/62

BMV 13/90

RR 0.89 (0.40-1.98)

Intervention is AAM only.

Confounding factors were

unadjusted.

Hansen 2020

Survival at hospital discharge

ETI 6/73

BMV 10/63

RR 0.52 (0.20-1.30)

Confounding factors were

unadjusted.

Survival at hospital discharge

SGA 3/19

BMV 10/63

RR 0.96 (0.31-2.92)

BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway; AAM, advanced airway management; aOR, adjusted odds ratio; RR, risk ratio; ALS, advanced life support.

compared with ETI (RR 0.36 [95% CI 0.21-0.60]) and SGA (RR 0.55 [95%

CI 0.33-0.92]). When compared with ETI, BMV was associated with fa- vorable neurological outcomes (RR 0.23 [95% CI 0.08-0.67]). A summary of the confidence in the network estimates and detailed assessment of estimates from the network meta-analysis are shown in Supplementary Tables 11-13. The certainty of evidence in the network estimates in both survival and favorable neurological outcomes was low for ETI vs. BMV and SGA vs. BMV, and very low for ETI vs. SGA. The P-scores for

survival and favorable neurological outcomes are shown in Supplemen- tary Table 14. The hierarchy for efficacy in both survival and favorable neurological outcome was BMV > SGA > ETI.

  1. Discussion

We compared the effectiveness of three prehospital airway manage- ment methods, namely, ETI, SGA, and BMV, during resuscitation for

Image of Fig. 2

Fig. 2. Network plot for Survival and neurological outcome.

The node size corresponds to the number of patients who received the intervention. The thickness of the line corresponds to the number of studies that compared the two linked interventions. BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway.

pediatric OHCA using network meta-analysis. Our results suggest that compared to BMV, ETI and SGA do not lead to improved patient survival and favorable neurological outcomes at hospital discharge or 1 month after cardiac arrest. However, the certainty of the evidence is low to very low, and there is no conclusive evidence regarding the effective- ness of AAM during prehospital pediatric cardiopulmonary resuscita- tion; therefore, high-quality clinical trials are required.

Because OHCA in children is often due to respiratory failure [45], ef- ficient airway management with AAM is important. However, the net- work meta-analysis revealed that there is no conclusive evidence that ETI or SGA improves important outcomes, such as survival and neuro- logical outcomes, more than BMV. A systematic review/meta-analysis comparing AAM to BMV for both in-hospital cardiac arrest and OHCA in 2019 showed no advantage for AAM [12], and a systematic review

evaluating AAM for prehospital pediatric patients in 2022 suggested ETI had similar or worse outcomes compared to other airway manage- ment methods [46]. The current network meta-analysis assessed the impact of AAM in prehospital cardiopulmonary resuscitation in pediat- ric OHCA, which is less heterogeneous than previous systematic reviews due to the limited number of patients and situations, suggesting that ETIs may not improve outcome.

Most of the available evidence was provided by observational stud- ies and only one clinical trial. The clinical trial, which was conducted

>20 years ago, allocated interventions according to even or odd days, and interventions were performed by emergency medicine service per- sonnel [26]. Analyses were performed with an intention to treat. Obser- vational studies included in the quantitative analysis were adjusted for several measurable confounders using propensity score [27-30]. In the cohort study conducted in France, physicians performed advanced air- way procedures in a mobile prehospital intensive care unit, and analysis was performed with an intention to treat [30]. Other observational stud- ies included in the quantitative analysis were performed by emergency medicine service personnel [27-29]. However, these studies did not clarify whether BMV was performed without attempting AAM or after failed AAM. Thirteen studies did not meet the criteria for quantitative synthesis because they either did not adjust or sufficiently adjust for confounders, only compared AAM and BMV [31-40,42-44]. These stud- ies were evaluated qualitatively; although their results varied, many studies did not find significant differences between AAM and BMV. Meanwhile, one observational study rigorously adjusted for con- founders and addressed resuscitation time bias [41], which occurs when the timing of intervention is not considered [47]; however, it was not included in the network meta-analysis due to patient duplica- tion [27]. In that study, no significant differences were found between ETI and BMV or SGA and BMV, partly due to the small number of patients.

Regarding neurological outcomes, when ETI and BMV were com- pared, there were inconsistencies between the results of the clinical trial conducted >20 years ago [26] and those of recent observational studies that rigorously adjusted for confounders using propensity score [27-30]. The clinical trial had point estimates of ETI superiority, al- though this was not statistically significant. Meanwhile, in a sensitivity analysis of a network meta-analysis of confounding-adjusted cohort studies, ETI had significantly worse outcomes than BMV in both survival and neurological outcomes. The finding that the evidence is uncertain, but certainly there is no proof that ETI or SGA is superior to BMV may

Image of Fig. 3

Fig. 3. Forest plots of the direct comparison, indirect comparison, and network meta-analysis for survival. BMV was associated with survival, compared with ETI. We did not find any sig- nificant association in the other comparisons. BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway, CI, confidence interval.

Image of Fig. 4

Fig. 4. Forest plots of the direct comparison, indirect comparison, and network meta-analysis for favorable neurological outcome. We did not find any significant association. BMV, bag- mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway, CI, confidence interval.

Table 3

Direct estimates, indirect estimates, and network estimates for survival.

Comparison

Direct estimate RR (95% Cl)

Indirect estimate RR (95% Cl)

Network estimate RR (95% Cl)

Certainty of evidence for network meta-analysis

ETI vs. BMV

0.59 (0.31-1.13)

0.26 (0.09-0.78)

0.44 (0.25-0.77)

Very low

SGA vs. BMV

0.41 (0.18-0.92)

0.93 (0.34-2.54)

0.62 (0.33-1.15)

Low

ETI vs. SGA

0.63 (0.30-1.35)

1.44 (0.51-4.04)

0.71 (0.39-1.32)

Very low

BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway; RR, risk ratio.

Table 4

Direct estimates, indirect estimates, and network estimates for favorable neurological outcomes.

Comparison

Direct estimate RR (95% Cl)

Indirect estimate RR (95% Cl)

Network estimate RR (95% Cl)

Certainty of evidence for network meta-analysis

ETI vs. BMV

0.62 (0.17-2.20)

0.09 (0.01-0.94)

0.33 (0.11-1.02)

Very low

SGA vs. BMV

0.15 (0.03-0.75)

1.04 (0.12-8.90)

0.50 (0.14-1.80)

Very low

ETI vs. SGA

0.59 (0.11-3.32)

4.01 (0.53-30.63)

0.66 (0.18-2.46)

Very low

BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway; RR, risk ratio.

be correct because of the effect size of the network meta-analysis in re- cent high-quality observational studies, differences in medical systems including emergency systems, and Resuscitation procedures in the 20- year-old clinical trial. However, observational studies of prehospital air- way management may be influenced by confounding by indication, which cannot be excluded by adjusting for confounders using known data. Additionally, most observational studies of interventions during cardiopulmonary resuscitation have issues with resuscitation time bias [47]. Therefore, rigorous clinical trials are needed to obtain conclu- sive evidence of prehospital airway management during resuscitation for pediatric OHCA.

The available evidence for prehospital AAM in adults indicates that ETI does not improve outcomes. A systematic review/meta-analysis of observational studies on adult OHCA reported worse outcomes for ETI and AAM compared to BMV [48]. Recent network meta-analysis also re- vealed that ETI did not demonstrate a significant advantage over alter- native techniques in terms of patient survival until discharge [49].

Table 5

P-scores of treatments.

Treatment P-score for survival P-score for favorable neurological outcomes BMV 0.99 0.99

SGA 0.49 0.49

ETI 0.007 0.01

BMV, bag-mask ventilation; ETI, endotracheal intubation; SGA, supraglottic airway.

A large RCT in 2018 reported that there were no differences in the 28- day survival or favorable neurological function between groups treated with ETI and BMV [50]. In this study, physicians on the prehospital med- ical team performed ETI and had a higher success rate, which may have favored ETI over other studies wherein emergency medicine service personnel performed the procedure. In RCTs comparing SGA and ETI in adult OHCA, ETI had equivalent or worse outcomes [51,52]. The ben- efits of advanced airway clearance include effective ventilation, preven- tion of aspiration, and uninterrupted chest compressions, whereas the risks include hyperventilation and interruption of chest compressions during the procedure; however, failure of intubation can result in poor Ventilation and oxygenation, aspiration, and interruption of chest com- pressions [53,54]. Children are anatomically more difficult to intubate than adults. The success rate of ETI is lower in children than in adults, and that of SGA is not different according to age [55,56]. Difficulty and failure of the procedure in children may be associated with poor out- comes following ETI.

  1. Strengths and limitation

To the best of our knowledge, no studies have determined the effec- tiveness of AAM during prehospital cardiopulmonary resuscitation for pediatric OHCA using quantitative integration with network meta- analysis. Previous systematic review/meta-analyses of AAM in children comprised studies investigating AAM during cardiopulmonary resusci- tation, including in-hospital cardiac arrest and OHCA, or AAM for

prehospital critically ill children; however, the present study focused on children during prehospital cardiopulmonary resuscitation. Therefore, the clinical heterogeneity of this is lower. A broad search without lan- guage restrictions was conducted for observational studies, and RCTs. Clinical trials and cohort studies with appropriate adjustment for con- founders underwent quantitative analysis, whereas observational stud- ies that were not appropriate for quantitative integration underwent qualitative evaluation.

However, the limitations of our study need to be acknowledged. First, this network meta-analysis synthesized studies of different types. Since there was only one clinical trial, we quantitatively synthe- sized the clinical trial and cohort studies that were appropriately ad- justed for confounders. We also performed a sensitivity analysis that synthesized only observational studies that were adjusted for con- founders. Second, most of the available evidence was from observa- tional studies, which have issues such as confounding by adaptation, resuscitation time bias, and were unclear as to whether advanced air- way intervention failed and resulted in BMV only or whether advanced airway intervention was not attempted. Although the network meta- analysis integrated observational studies that adjusted for confounders, confounding by adaptation may not be fully adjusted for based on known information. Regarding resuscitation time bias, only one obser- vational study, which could not be integrated due to duplication of cases, addressed this bias [41]. The inability to correctly classify patients in whom an advanced airway could not be secured is a significant prob- lem; however, this problem may be biased in favor of ETI and will not work against the present results. Third, various factors, such as the ex- perience level of the medical personnel performing airway manage- ment, high heterogeneity of patients with cardiac arrest, and differences in Emergency medical care systems, may affect the results; however, such clinical heterogeneity was not addressed.

  1. Conclusion

This network meta-analysis suggests that prehospital AAM for pedi- atric OHCA does not improve outcomes. However, many of the available evidence is from observational studies, and issues, such as confounding by indication, resuscitation time bias, inability to correctly classify pa- tients in whom an advanced airway cannot be secured, remain unre- solved. Further rigorous clinical trials are needed to draw definitive conclusions regarding the effectiveness of AAM during prehospital pedi- atric cardiopulmonary resuscitation.

Funding

This research did not receive any specific grant from funding agen- cies in the public, commercial, or not-for-profit sectors.

CRediT authorship contribution statement

Shunsuke Amagasa: Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Shu Utsumi: Writing – review & editing, Data curation. Taro Moriwaki: Writing – review & editing, Data curation. Hideto Yasuda: Writing – review & editing, Methodology, Formal analysis. Masahiro Kashiura: Writing – review & editing, Conceptualization. Satoko Uematsu: Writing – review & editing. Mitsuru Kubota: Writing – review & editing.

Declaration of Competing Interest

None.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi. org/10.1016/j.ajem.2023.03.049.

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