Hypertension is a systemic disease characterized by elevated systemic arterial pressure, usually caused by the interaction of multiple genetics, environment, and various risk factors.[1–3] It is one of the most common chronic diseases and an increasingly public-health challenge worldwide. Hypertension is also the most important risk factor for cardiovascular and cerebrovascular diseases, often accompanied by major complications, such as stroke, myocardial infarction, heart failure, and chronic kidney disease, which not only cause disability, high mortality but also severely consume medical and social resources, placing a heavy burden on families and countries.[3,5–7]
The underlying goal of hypertension treatment is to reduce the overall risk of development and death of heart, brain, kidney, and vascular complications. Commonly used antihypertensive drugs include calcium channel blockers (CCB), angiotensin-converting enzyme inhibitors (ACEI), angiotensin receptor blockers (ARB), diuretics, and beta blockers. ARB can reduce the incidence of cardiovascular complications in patients with a history of cardiovascular disease and the risk of cardiovascular events in hypertension patients, reducing proteinuria and microalbuminuria in patients with diabetes or kidney disease. ARB is especially suitable for patients with left ventricular hypertrophy, heart failure, diabetic nephropathy, coronary heart disease, metabolic syndrome, microalbuminuria or proteinuria, and patients who cannot tolerate ACEI.[9–11] ARB drugs mainly include losartan, valsartan, irbesartan, telmisartan, candesartan, olmesartan, alisartan, and azilsartan.[12,13] Compared with other ARBs, azilsartan binds tightly to AT1 and slowly separates. Furthermore, azilsartan induces insurmountable antagonism of angiotensin II-induced vascular contractions and inverse agonism against AT1.[14–16] The high affinity and tight binding properties of azilsartan are expected to induce potent and long-lasting antihypertensive effects in preclinical and clinical settings.[16–18]
Recently, many meta-analyses evaluated the efficacy and safety of azilsartan medoxomil compared to placebo or other antihypertensive drugs in patients with hypertension.[16,19,20] However, these studies are traditional meta-analyses that make it difficult to assess the effects of 2 or more interventions, although well-conducted systematic reviews of randomized controlled trials (RCTs) are often considered the best way to obtain evidence of healthcare decisions.[21–23] Therefore, it is still unclear which dose of azilsartan is optimal. Network meta-analysis (NMA) allows for visualization of a larger amount of evidence, estimation of the relative effectiveness among all interventions, and rank ordering of the interventions even if some head to head comparisons are lacking.[24,25] Thus, this study will perform a NMA to assess the efficacy and safety of different doses of azilsartan medoxomil in patients with hypertension.
The NMA will be conducted and reported in accordance with PRISMA extension version (PRISMA-NMA), and this protocol will be reported according to preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P). The present protocol has been registered on the international prospective register of systematic review (PROSPERO) (CRD42019136882).
2.1 Search strategy
Experienced medical information experts worked with the review team to develop a comprehensive search strategy. A combination of subject terms and keywords was used and make appropriate adjustments of vocabulary and grammar between different databases. PubMed, EMBASE.com, the Cochrane library, SCOPUS, and Web of Science were searched from inception to May 2019. At the same time, the reference lists of published reviews and retrieved articles were checked for additional trials. The PubMed search strategy as follows:
- #1 azilsartan kamedoxomil[Title/Abstract]
- #2 azilsartan medoxomil[Title/Abstract]
- #3 edarbi[Title/Abstract]
- #4 ipreziv[Title/Abstract]
- #5 tak 491[Title/Abstract]
- #6 tak491[Title/Abstract]
- #7 OR/1-6
2.2 Eligibility criteria
2.2.1 Types of study
RCTs reporting efficacy and safety of different doses of azilsartan medoxomil on hypertension will be included if they compared 1 dose of azilsartan medoxomil with another dose of azilsartan medoxomil or with a placebo regardless of sample size. RCTs should report at least 1 measure outcome of efficacy and safety and provide enough detail to calculate effect sizes.
Patients diagnosed with hypertension, regardless of age, gender, and duration of illness.
The intervention is the azilsartan medoxomil. The dose and duration of treatment are not limited.
The control can be another dose of azilsartan medoxomil or a placebo.
The primary outcomes will include 24-hour mean diastolic blood pressure (DBP), 24-hour mean systolic blood pressure (SBP), nighttime DBP, nighttime SBP, daytime DBP, daytime SBP, clinic DBP, clinic SBP, and response rate. The second outcomes are any adverse events, serious adverse events, and adverse events lead to treatment interruption.
2.2.6 Other criteria
The exclusion criteria are
- the intervention is azilsartan medoxomil combined with other antihypertensive drugs;
- comparison of 2 different types of antihypertensive drugs;
- nonrandomized trials, such as cohort study and observational study.
2.3 Selection of studies
The literature management software will be used to classify and organize the initial inspection documents, and to exclude duplicate documents. Then, 2 independent examiners will read the title and abstract of each record, and exclude studies that do not meet the inclusion criteria. For any potentially related study, we will review the full-text and determine the compliance studies based on the inclusion and exclusion criteria. The reasons for the excluded studies will be recorded. If we identify multiple similar articles published by the same author or institution, the one with long follow-up, the larger number of samples or more detailed data will be included, the remaining articles will be used as a supplement to the data. The intraclass correlation coefficient (ICC) will be used to assess the consistency of study selection between two reviewers. Disagreements will be discussed or by a third reviewer if no consensus is reached.
2.4 Data extraction
We will use predefined extraction forms with detailed written instructions to collect relevant information and data. Two independent reviewers will extract data from the included trials. The data extraction includes basic information such as first author, publication year, country, study time; information on hypertension; information on interventions (dose, time of treatment, follow-up time, etc); outcomes of interest. For studies did not report the data, we will contact the original author to get the relevant information. If there is a discrepancy between the two reviewers, a third researcher will be consulted.
2.5 Risk of bias assessment.
Two reviewers will independently use the Cochrane Handbook V.5.1.0 for systematic reviews of intervention to assess the quality of included RCTs. We will resolve any disagreement by discussion or by involving a third review author. The Handbook consists of random sequence generation, allocation concealment, blinding of all participants, including patients, personnel and outcome assessors, incomplete outcome data, selective reporting, and other sources of bias. We will rate the methodological quality as low, high or unclear risk of bias.
2.6 Statistical analysis
2.6.1 Pairwise meta-analyses
For categorical variables, we will calculate the pooled odds ratio (OR) with the 95% confidence interval (95% CI). For continuous variables, we will calculate the standardized mean differences (SMDs) or mean differences (MDs) with 95% CIs. Study-specific estimates will be combined in the random-effects model.
We will assess statistical heterogeneity within each pairwise comparison using the I2 statistic. The values of 25%, 50%, and 75% for the I2 as indicative of low, moderate, and high statistical heterogeneity, respectively. We will explore sources of heterogeneity by subgroup analysis or meta-regression.
2.6.2 Network meta-analysis
To assess the impact of azilsartan dosage on the pooled estimate, the effects of azilsartan therapy on blood pressure will be explored in the comparison of different doses of azilsartan with control therapy using a NMA. The NMA will be performed in a Bayesian hierarchical framework to incorporate both direct evidence and indirect evidence using Markov Chain Monte Carlo method in WinBUGS 14 (MRC Biostatistics Unit, Cambridge University, UK). The Brooks-Gelman-Rubin method will be used to assess convergence. We will compute a potential scale reduction factor (PSRF) by comparing within-chain and between-chain variance, and a PSRF very close to 1 is considered to indicate an approximate convergence. We will use the surface under the cumulative ranking curve (SUCRA) to rank the treatments according to each outcome accounting for the uncertainty in the treatment effects. The absolute ranks of the treatments per outcome are presented using “Rankograms” that visually show the distribution of ranking probabilities. The node splitting method will be adopted to examine the inconsistency between direct and indirect comparisons if a loop connecting 3 or more arms exist. A network plot will be plotted to describe and present the geometry of the treatment network of comparisons across trials to ensure if a NMA is feasible. The trials that are not linked by interventions will be excluded from the NMA, and we will just describe the findings of the study. All the result figures will be generated using STATA (13.0; Stata Corporation, TX,) software.
2.6.3 Subgroup analysis and sensitivity analysis
If the necessary data are available, subgroup analyses will be done for different types of participants by gender and country. Sensitivity analyses will be performed to assess the contribution of each study to the pooled estimate by excluding individual trials 1 at a time and recalculating the pooled estimates for the remaining studies.
2.7 Assessment of publication bias.
Egger test and funnel plot will be conducted to detect the asymmetry due to publication bias when applicable.[34,35]
2.8 Quality of evidence
The Grading of Recommendations Assessment, Development and Evaluation (GRADE) tool will be used to assess the quality of evidence for each primary outcome. The GRADE approach assesses the quality of the body of evidence for each outcome considering 5 factors: study limitations, consistency of effect, indirectness, imprecision, and publication bias. The overall quality of evidence can be rated into 4 levels: high level, moderate level, low level, and very low level.
3 Preliminary results
3.1 Study selection
According to the search strategy, related resources were retrieved, and 710 related records were obtained. After removing duplicates, 458 articles were obtained. Read the title and abstract to exclude 311 studies on non-hypertensive studies and concomitant medications, 15 news, errata, and drug information. Further screening of the remaining 132 full texts, excluding 3 pharmacokinetic studies, 17 studies comparing other antihypertensive drugs, 9 narrative reviews, 27 studies of combined drugs, and 6 systematic reviews or meta-analyses, 48 non-RCTs. Twenty-two studies were eventually included and 2 studies were supplemented by tracking references. Twenty-four studies reported 10 RCTs.
3.2 Main characteristics of some of the included studies
We extracted some data from the included RCTs, the main characteristics of some of the included studies are summarized in Table 1.
4 Ethics and dissemination
Ethics approval and patient consent are not required as this study is a NMA based on published RCTs. This study will summarize all the available data to provide reliable evidence of the value of different doses of azilsartan medoxomil for the treatment of hypertension. The results will be submitted to a peer-reviewed journal for publication. We hope the findings of this NMA will help clinicians and patients choose the optimal dose of azilsartan medoxomil in treating hypertension.
Conceptualization: Yan Zhang, Gen Chen.
Data curation: Yan Zhang, Huijin Yu, Xinyue Luo.
Formal analysis: Yan Zhang, Gen Chen.
Funding acquisition: Jiancheng Wang.
Investigation: Yan Zhang, Huijin Yu, Kangmei Shao, Xinyue Luo.
Methodology: Jiancheng Wang, Gen Chen.
Project administration: Gen Chen.
Resources: Yan Zhang, Huijin Yu, Kangmei Shao, Xinyue Luo, Gen Chen.
Software: Yan Zhang, Huijin Yu.
Supervision: Gen Chen.
Validation: Gen Chen.
Visualization: Kangmei Shao.
Writing – original draft: Yan Zhang, Huijin Yu, Gen Chen.
Writing – review & editing: Yan Zhang, Jiancheng Wang, Gen Chen.
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