Description of the conditions

Emergence agitation (EA) is a significant complication in children undergoing general anesthesia, with a reported frequency ranging from 10% to 80% [1]. Sikich and Lerman [2] defined emergence delirium, which is a significant cause of EA, as follows: “a disturbance in a child’s awareness of and attention to his or her environment with disorientation and perceptual alterations including hypersensitivity to stimuli and hyperactive motor behavior in the immediate post-anesthesia period.” Restless recovery from anesthesia may increase the risk of self-injury and result in the additional adjunctive administration of sedatives and/or analgesic medications, which may delay the patient’s discharge from the hospital. Moreover, EA often requires constant nursing supervision, which strains nursing staff resources and induces stress in caregivers and families [1,3].

Description of the intervention

Pharmacological agents, including propofol, fentanyl, dexmedetomidine, and clonidine are the conventional anesthetics used for preventing EA [4–8]. However, these pharmacological agents may often increase the adverse events such as delayed recovery, vomiting, and respiratory depression [8,9]. Therefore, alternative methods for preventing EA are desired.

Acupuncture therapy is considered an effective solution without pharmacological therapy and is reported to prevent EA without causing adverse events [10,11]; however, its efficacy remains controversial [12]. Acupuncture therapy is aimed at reducing symptoms and curing diseases by inserting fine needles into the skin to a certain depth at acupoints and manipulating them. It is among the most preferred complementary therapies in many countries [13]. Acupuncture therapy was initially developed as part of Chinese medicine with the aim of restoring the patient to the state of equilibrium that supposedly existed before the illness.

Mechanism of action of the intervention

Several studies have reported that acupuncture therapy effectively prevents EA in children; however, its mechanism of action remains unclear [10,11]. Measures that prevent EA focus on the management of pain, opioid withdrawal, and its inhibitory effect on the central nervous system. In adults, several studies have suggested that acupuncture therapy activates the release of opioid peptides in the central nervous system; this has been associated with the relief of an array of pain conditions [14,15]. Acupuncture therapy increases the secretion of β-endorphin and reduces epinephrine, cortisol, and prostaglandin E2 levels, which are responsible for regulating visceral pain [16]. Several studies have reported effective stimulation points. The LI-4 (he gu) acupuncture point has been shown to improve gastrointestinal symptoms and reduce pain and autonomic distress in children [17,18]. The HT7 (shen men) acupuncture point reportedly reduces psychological stress and agitation in adults [19,20]; it also inhibits the central release of gamma-aminobutyric acid B, which regulates dopamine, resulting in a significant reduction in behavioral hyperactivity associated with morphine tolerance in animal models [21].

Significance of this review

In children, acupuncture therapy has been a safe complementary or alternative medicine modality, with some degree of efficacy and low risk [22]. However, no meta-analyses have been conducted to understand the effect of acupuncture therapy in preventing EA in children [12]. Previous studies have varied in the method of acupuncture therapy, selection of stimulation points, and type of surgery.

The primary purpose of this meta-analysis is to assess the effect of acupuncture therapy on the incidence of EA in children undergoing general anesthesia. The effect was evaluated using a specific assessment tool compared with no treatment, placebo/sham, or standard care in children. The secondary purpose was to assess adverse events and conduct subgroup analyses when the heterogeneity of the included studies is high.

Eligibility criteria

Information sources and search strategy

We will conduct a search in the following databases: MEDLINE, CENTRAL, Embase, and Web of Science. The reference lists of the retrieved articles will also be evaluated. In addition, we will conduct a search of clinicaltrials.gov, the European Union Clinical Trials Register, the WHO International Clinical Trials Registry Platform, and the UMIN Clinical Trials Registry. The search strategy combining free text and MeSH terms for PubMed is shown in Table 1.

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Table 1. Search strategy for PubMed.

https://doi.org/10.1371/journal.pone.0264197.t001

Two authors will independently scan the titles and abstracts of the reports identified by the variety of search strategies described above. We will use Mendeley to remove duplicate titles and abstracts from our database search. We will export the remaining titles and abstracts to the application Rayyan QCRI14 [25] and screen the titles and abstracts. If eligibility cannot be determined from the title or abstract, the full paper will be reviewed. Potentially relevant studies selected by at least one author will be retrieved and evaluated in full-text versions. The articles meeting the inclusion criteria will be assessed separately by two authors, and any discrepancies will be resolved through a discussion.

Study records

Outcomes and prioritization

Assessment of the risk of bias in individual studies

We will assess the risk of bias using Cochrane’s Risk of Bias tool (RoB2.0) [26] for RCTs. The RoB 2.0 assessment for individually randomized trials has five domains and one overall risk of bias domain, as follows:

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 the measurement of outcomes.
5. Bias for selection of the reported result.
6. Overall risk of bias.

The risk of bias will be assessed as “low,” “some concern,” or “high” in each domain.

Data synthesis and statistical analyses

Statistical analyses will be performed using the R statistical software package, version 4.0.0 (R Foundation for Statistical Computing, Vienna, Austria). We will compare dichotomous outcomes between the groups using the risk ratio and continuous data using the mean difference (MD) and the corresponding standard deviation (SD). We will summarize the risk ratio and mean difference with a 95% confidence interval (CI); if the 95% CI includes a value of 0 or 1 for continuous or dichotomous data, respectively, we will consider the difference to be statistically insignificant. We will use a random-effect model (DerSimonian and Laird method [27]) to combine the results and the Hartung-Knapp-Sidik-Jonkman adjustment method [28] for the random-effect model in cases where the number of studies is small (i.e., less than 10). Heterogeneity will be quantified using the I² statistic; significant heterogeneity will be considered when the I² statistic exceeds 50%. We will conduct subgroup analyses to explore the possible causes in cases with high heterogeneity. The forest plot will be used to represent the effect of treatment graphically. A small study effect will be assessed using a funnel plot and Egger’s regression asymmetry test [29] and will be considered positive if p < 0.10 in the regression asymmetry test.

Subgroup and sensitivity analyses

We plan to conduct subgroup analyses according to the following predefined factors when the I² statistic exceeds 50%: (1) method of acupuncture therapy, (2) selection of points (unilateral or bilateral), and (3) type of surgery. Sensitivity analysis excluding studies with high risk of bias will be performed for primary outcome.

Trial sequential analysis

For the primary outcome, TSA will be performed to correct for random error and repetitive testing of accumulating and sparse data [30,31]. TSA monitoring boundaries (i.e., monitoring boundaries for meta-analysis) and required information size (RIS) will be quantified, and adjusted CIs will be calculated. The risk of type 1 error will be maintained at 5% with a power of 90%. A reduction in the risk ratio by 25% will be considered as clinically meaningful. If the cumulative Z-curve does not cross the TSA monitoring boundaries, we will downgrade the quality of evidence due to inaccuracies in the results.

Summary of evidence

We will grade the quality of evidence of the primary outcomes using the Grading of Recommendations Assessment, Development, and Evaluation approach [32,33] with GRADEpro software. Judgments of the quality of evidence will be based on the presence or absence of the following variables: limitations in study design, inconsistency, indirectness, inaccuracies in the results, and publication bias. The quality of evidence for the primary outcomes will be graded as very low, low, moderate, or high.