Network Meta-Analysis of the Efficacy of Acupuncture, Alpha-blockers and Antibiotics on Chronic Prostatitis/Chronic Pelvic Pain Syndrome

Alpha-blockers and antibiotics are most commonly used to treat chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) in clinical practice. Currently, increasing evidence also suggests acupuncture as an effective strategy. This network meta-analysis intended to assess the comparative efficacy and safety of acupuncture, alpha-blockers and antibiotics for CP/CPPS. Twelve trials involving 1203 participants were included. Based on decreases in the National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) score, a network meta-analysis indicated that electro-acupuncture (standard mean difference [SMD]: 4.29; 95% credible interval [CrI], 1.96–6.65), acupuncture (SMD: 3.69; 95% CrI, 0.27–7.17), alpha-blockers (SMD: 1.85; 95% CrI, 1.07–2.64), antibiotics (SMD: 2.66; 95% CrI, 1.57–3.76), and dual therapy (SMD: 3.20; 95% CrI, 1.95–4.42) are superior to placebo in decreasing this score. Additionally, electro-acupuncture (SMD: 2.44; 95% CrI, 0.08–4.83) and dual therapy (SMD: 1.35; 95% CrI, 0.07–2.62) were more effective than alpha-blockers in decreasing the total NIH-CPSI total score. Other network meta-analyses did not show significant differences between interventions other placebo. The incidence of adverse events of acupuncture was relatively rare (5.4%) compared with placebo (17.1%), alpha-blockers (24.9%), antibiotics (31%) and dual therapy (48.6%). Overall, rank tests and safety analyses indicate that electro-acupuncture/acupuncture may be recommended for the treatment of CP/CPPS.

Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. Clearly describe eligible treatments included in the treatment network, and note whether any have been clustered or merged into the same node (with justification).
14 Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. 14-15 Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.

14-15
Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).

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Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.

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Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.

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Geometry of the network S1 Describe methods used to explore the geometry of the treatment network under study and potential biases related to it. This should include how the evidence base has been graphically summarized for presentation, and what characteristics were compiled and used to describe the evidence base to readers. Describe the methods of handling data and combining results of studies for each network meta-analysis. This should include, but not be limited to: • Handling of multi-arm trials; • Selection of variance structure;

• Selection of prior distributions in Bayesian analyses; and
• Assessment of model fit.

S2
Describe the statistical methods used to evaluate the agreement of direct and indirect evidence in the treatment network(s) studied. Describe efforts taken to address its presence when found.

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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).

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Additional analyses

16
Describe methods of additional analyses if done, indicating which were pre-specified. This may include, but not be limited to, the following: • Sensitivity or subgroup analyses; • Meta-regression analyses; • Alternative formulations of the treatment network; and • Use of alternative prior distributions for Bayesian analyses (if applicable).

Study selection 17
Give numbers of studies screened, assessed 14-15 , for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.

Figure 1
Presentation of network structure

S3
Provide a network graph of the included studies to enable visualization of the geometry of the treatment network.

Figure 2
Summary of network geometry

S4
Provide a brief overview of characteristics of the treatment network. This may include commentary on the abundance of trials and randomized patients for the different interventions and pairwise comparisons in the network, gaps of evidence in the treatment network, and potential biases reflected by the network structure.

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For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. Table 1 Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment. Table 2 Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: 1) simple summary data for each intervention group, and 2) effect estimates and confidence intervals. Modified approaches may be needed to deal with information from larger networks.
Supplementary Table 1 Synthesis of results

21
Present results of each meta-analysis done, including confidence/credible intervals. In larger networks, authors may focus on comparisons versus a particular comparator (e.g. placebo or standard care), with full findings presented in an appendix. League tables and forest plots may be considered to summarize pairwise comparisons. If additional summary measures were explored (such as treatment rankings), these should also be presented.

Exploration for inconsistency S5
Describe results from investigations of inconsistency. This may include such information as measures of model fit to compare consistency and inconsistency models, P values from statistical tests, or summary of inconsistency estimates from 9 different parts of the treatment network.

Risk of bias across studies 22
Present results of any assessment of risk of bias across studies for the evidence base being studied. Table 2 Results of additional analyses

23
Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, metaregression analyses, alternative network geometries studied, alternative choice of prior distributions for Bayesian analyses, and so forth).

Summary of evidence 24
Summarize the main findings, including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policymakers).

9-11
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review level (e.g., incomplete retrieval of identified research, reporting bias). Comment on the validity of the assumptions, such as transitivity and consistency. Comment on any concerns regarding network geometry (e.g., avoidance of certain comparisons).

Conclusions 26
Provide a general interpretation of the results in the context of other evidence, and implications for future research.

Funding 27
Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. This should also include information regarding whether funding has been received from manufacturers of treatments in the network and/or whether some of the authors are content experts with professional conflicts of interest that could affect use of treatments in the network.