The content followed preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P)
2.1. Eligibility criteria
2.1.1. Types of studies. This review will include all randomized controlled trials (RCTs) in English or Chinese and without the restriction of publication type. Non-RCTs, quasi-RCTs, uncontrolled trials, case series, case reports, crossover studies, letters, and laboratory studies will be excluded.
2.1.2. Types of participants. Patients with ONP were diagnosed according to the book named Neurology[16]. Information relating to their age, sex, race, education, nationality, or economic status is not taken into account.
2.1.3. Types of interventions
2.1.3.1 Experimental interventions. The forms of acupuncture therapy include body acupuncture, electro-acupuncture, auricular acupuncture, intradermal needle, scalp acupuncture, ocular acupuncture, fire needling, elongated needling, and plum blossom needle. Acupoint injection, laser acupuncture, moxibustion, cupping, and acupressure will be excluded.
2.1.3.2. Control interventions. Types of control interventions include placebo control, sham acupuncture (use acupuncture needle to insert into skin without penetrating exact acupoints), western medicine compare with acupuncture, drug therapy, other therapies, no therapy and acupuncture plus 1 treatment compares with the same treatment.
2.1.4. Types of outcome measures
2.1.4.1. Primary outcomes. The primary outcome included:
(1) Scores of diplopia: visual analog scale (VAS)[11,17]
(2) Palpebral fissure size: from midpoint of upper eyelid to midpoint of lower eyelid.[13,15,18]
(3) Therapeutic efficiency: refer to the State Administration of Traditional Chinese Medicine issued the Standard for the Diagnosis and Curative Effect of Disease and Syndromes of Traditional Chinese Medicine[18-19].
(4) The range of eye movement: the distance from the lateral canthus to the center of the pupil was taken as the datum and the distance between the two maximal activity points was recorded[18].
(5) Measure pupil diameter: measure pupil size under light[17].
2.1.4.2. Secondary outcomes. The secondary outcome included:
(1) Measure the patients’ quality of life: quality of life assessment scale SF-36(short form 36 questionnaires)[5]
(2) Adverse effects
2.2. Search strategy
To evaluate the efficacy and safety of acupuncture for ONP, the following databases will be searched: PubMed, Cochrane Library, Medline, Chinese Biomedical Literature Database, China National Knowledge Infrastructure (CNKI), Wanfang data, Chinese Scientific Journal Database. In addition, relevant references and RCTs cited in the reports will be researched as supplementary sources. We will also study the gray literature.
The search strategy for PubMed is detailed in Table 1.It will also be used for any other electronic databases.
Table 1. Search strategy used in PubMed databases
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)
2.3. Data collection and analysis
2.3.1. Selection of studies. All reviewers will get trained to understand the aim and process of the system review and will select the trials and studies separately. Two reviewers will screen the titles, abstracts and keywords based on the inclusion criteria and the full text will be read for further assessment if necessary. Details of the research choices are shown and exclusive studies will be listed and explained. The process of study selection will be presented in PRISMA flow diagram (Fig.1)
2.3.2 .Data extraction and management. Three viewers (XHZ, SMZ and RSD) will independently extract the data from the studies and fill in the data collection form. The discrepancies and uncertainties in the process will be discussed and solved with a senior reviewer HC. We will contact the corresponding authors for more information if the details of trials were not completed. All data will be transferred into review manager software. We will extract information such as author, time of publication, participants information (age, sex), sample size, interventions, outcome indicators, and other details.
2.3.3. Risk of bias assessment. We will use Cochrane risk of bias assessment tools to evaluate 6 domains of bias(sequence generation, allocation concealment, blinding, incomplete outcome data, selective outcome reporting ,and other sources of bias ).The included studies will be divided into 3 parts: unclear , low risk, and high risk. The discrepancies will be solved with a senior reviewer called HC.
2.3.4. Assessment of heterogeneity. I square (I2) statistic will be used to assess heterogeneity in included studies, which describes the percentage of variation between studies due to heterogeneity. If I2<50%, we will choose the fixed-effect model to calculate risk ratio (RR) and mean difference(MD), otherwise use a random-effect model.
2.3.5. Measures of treatment effect. We used review manager 5.3 for meta-analysis to analyze data. We will use MD or standardized mean difference with 95% confidence interval(CI) to synthesize continuous variables. For dichotomous data, we will use the RR with 95% CI.
2.3.6. Dealing with missing data. We will contact the original authors to obtain relevant information when collected data is insufficient. If the missing data are unobtainable, we will analyze the available data and describe it in the discussion. We will discuss the impact of missing data if necessary.
2.3.7. Subgroup analysis and investigation of heterogeneity. If data are available, we will perform subgroup analysis for different intervention forms. We will analyze different acupuncture therapy types, the age of patients, and other control interventions.
2.3.8. Ethics and dissemination. This review is aim to evaluate the safety and efficacy of acupuncture to treat ONP.
Formal ethical approval is not required, as the data are not individualized. The results will be disseminated through peer-reviewed publication.
2.3.9. Assessment of reporting biases. We will use a funnel plot to test reporting biases if more than 10 trials are included in the meta-analysis.