Akhmetzhan Galimzhanov1, Baurzhan Slymovich Azizov2
1Kazakhstan
2[Empty affiliation]
Citation example: Galimzhanov A, Azizov BSlymovich. Ticagrelor for Asian patients with acute coronary syndrome in "real-world" practice: a systematic review and meta-analysis of observational studies.. Cochrane Database of Systematic Reviews [Year], Issue [Issue].
Kazakhstan
E-mail: ahmed_galimzhan@mail.ru
Assessed as Up-to-date: | |
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Date of Search: | 02 October 2018 |
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Protocol First Published: | Not specified |
Review First Published: | Not specified |
Last Citation Issue: | Not specified |
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The existing evidence from RCTs is insufficient to define clearly a benefit/risk ratio for ticagrelor in Asian ACS patients.
we aimed to assess the efficacy and safety of ticagrelor compared to clopidogrel in Asian patients with acute coronary syndrome (ACS) in real-world practice.
PubMed, Web of Science and Scopus databases were searched systematically to obtain Asian observational studies that investigated the efficacy and safety of ticagrelor
compared to clopidogrel in ACS patients.
The observational studies that investigating the efficacy and safety of ticagrelor compared to clopidogrel in Asian ACS patients.
The pre-designed Excel form was used to extract necessary information from the full texts of the selected papers. We use an inverse-variance analysis method with random effects model (DerSimonian and Laird method) to estimate summary odds ratio (OR) with 95% confidence interval (CI). We also performed analyses with hazard ratio (HR) selected as an effect estimate
The meta-analysis included six studies with 27959 participants. Compared to clopidogrel, ticagrelor was significantly beneficial in prevention of major adverse cardiac events (MACCE) (OR=0.62; 95% CI: 0.46-0.83, I2=69%, p=0.001) mainly driven by reducing stroke (OR=0.62; 95% CI: 0.49-0.78, I2=0%, p<0.001). No differences were found between ticagrelor and clopidogrel in the risk of cardiovascular mortality (OR=0.66; 95% CI: 0.41-1.06, I2=0%, p=0.09), target vessel revascularization (OR=0.53; 95% CI: 0.21-1.35, I2=82%,p=0.18), major bleeding (OR=1.11; 95% CI: 0.62-2.00, I2=75%, p=0.73), and net adverse clinical and cerebral events (OR=0.76; 95% CI: 0.55-1.04, I2=78%, p=0.09). However, ticagrelor significantly increased the incidence of major/minor (OR=1.73; 95% CI: 1.36-2.21, I2=0%, p<0.001) and minor bleeding (OR=1.73; 95% CI: 1.29-2.32, I2=0%, p<0.001). Sensitivity analyses did not find consistent effect of ticagrelor on prevention of all-cause death and myocardial infarction.
This meta-analysis suggested that ticagrelor might reduce the risk of MACCE mainly by reducing stroke in Asian ACS patients without increasing the rates of major bleeding. Ticagrelor did not show a significant effect on other parts of MACCE. Considerable increase in the risk of major/minor and minor bleeding was observed in ticagrelor compared to clopidogrel users. Further high-quality studies are required to support these findings.
The international cardiac societies strongly recommend using ticagrelor or prasugrel as a first-line P2Y12 inhibitors in patients with acute coronary syndrome (ACS). The superiority of ticagrelor over clopidogrel was demonstrated in the multinational, randomized, double-blind Platelet Inhibition and Patient Outcomes (PLATO) trial. However, ticagrelor provides ischaemic benefits at the cost of significant increase in haemorrhagic events which is vitally important for bleeding-prone Asian patients.
Ticagrelor with a loading dose 180 mg and maintenance dose 90 mg twice daily.
Ticagrelor might significantly decrease major adverse cardiac events in Asian ACS patients.
Recent meta-analyses of randomized controlled trials (RCTs) reported that ticagrelor treatment increased numerically bleeding risk among Asian individuals and did not provide proper thrombotic benefits. Nevertheless, the existing evidence from RCTs is somewhat insufficient to formulate clear conclusions. Goto et al's trial, with equivalent study design, was underpowered to detect benefit/risk ratio for ticagrelor in Asian population. Other RCTs had serious limitations in randomization process which could bias their findings.
The aim of this review is to explore systematically and synthesize statistically the evidence from observational studies on the effectiveness and safety of ticagrelor compared to clopidogrel in Asian patients with ACS.
observational studies with clinical endpoints and a follow-up period of 6 months or more.
ticagrelor (loading dose of 180 mg and a maintenance dose of 90 mg twice daily)
Electronic databases searches, reference list and conference proceedings searches.
We searched systematically Asian studies in English language in PubMed (2010 to 2 October 2018), Web of Science and Scopus databases (2010 to 4 September 2018). The following keywords were typed in different combinations: "ticagrelor", AZD6140, "clopidogrel", "platelet aggregation inhibitors", "P2Y12 inhibitor", "acute coronary syndrome", "myocardial infarction". In PubMed, these search terms were combined with a Boolean operator ''AND'' and keywords depicting the Asian origin of studies. Medical Subject Headings synonyms of the most terms were also applied in the search strategy. In Scopus and Web of Science, we use filters for countries of origin to select only Asian trials.
Data collection and analysis was done by two reviewers with any disagreements resolved by discussion.
The observational studies that investigating the efficacy and safety of ticagrelor compared to clopidogrel in Asian ACS patients.
The pre-designed Excel form was used to extract necessary information from the full texts of selected papers. This data included data on study characteristics (authors, publication year, country, design, follow-up period, sample size), clinical and demographic features of participants, study endpoints, main results.
The included observational studies were evaluated according to the Newcastle-Ottawa quality assessment scale for cohort studies.
Summary odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI).
The missing data was planned to be obtained from authors of the original articales.
Statistical heterogeneity was calculated with both χ2 test and Higgins’s I2 statistics. Statistically significant heterogeneity was considered when χ2 p-value was less than 0.05 or an I2 statistic was more than 75%
Given the small number of included investigations, the assessment of publication bias and meta-regression analyses were not conducted.
We use inverse-variance analysis method with random effects model (DerSimonian and Laird method) to estimate summary odds ratio (OR) with 95% confidence interval (CI). In cases of rare events rates (below 1%), Peto analysis method with fixed effects model was used, as this method was shown to be superior in such situations. Several statistical methods and models were used to reveal possible discrepancies, however no significant differences were observed. As many of the included studies reported time-to-event outcomes, we also performed analyses with hazard ratio (HR) selected as an effect estimate to exclude the misinterpretation the data by calculating OR.
Subgroup analyses according to different characteristics of participants were also undertaken.
We also performed several types of sensitivity analyses. Firstly, we conducted a standard leave-one-out sensitivity analysis by removing the included studies one after another to validate the robustness of the results. Secondly, in cases of several studies based on the same database, we included all these studies one by one in the estimating of pooled OR to reveal possible discrepancies. Thirdly, we excluded the studies without adjustment statistics from the quantitative synthesis to minimize bias from confounding factors.
Several author teams analysed the data from the Korea Acute Myocardial Infarction Registry-National Institute of Health (KAMIR-NIH) database with controversial results. To avoid double-counting, we included only the findings provided by Sim et al, as this study incorporated the most recent data from KAMIR-NIH registry. Since Sim et al did not published the data on cardiovascular mortality, this information was obtained from the work of Park et al.
Lee et al included in their study 22815 patients, and, consequently, the weight of this study in the statistical analysis was the largest. Although patients' mean age and gender structure were similar, substantial differences in comorbidity status were observed between the selected studies.
The quality assessment demonstrated that the methodology of the included studies was not excellent. For example, Lee et al did not report data on clinical outcomes in 22.4% of ticagrelor users who switched from their initial P2Y12 antagonist treatment.
Primary efficacy endpoints
Ticagrelor significantly decreased the risk of MACCE compared to clopidogrel (OR=0.62; 95% CI: 0.46-0.83, I2=69%,p=0.001, Fig. 2A). The level of statistical heterogeneity can be rated as substantial. Nevertheless, ticagrelor proved its superiority over clopidogrel after performing of the sensitivity analyses (Table 3, 4).
Primary safety endpoints
The meta-analysis demonstrated no differences in the risk of major bleeding between ticagrelor and clopidogrel (OR=1.11; 95% CI: 0.62-2.00, I2=75%, p=0.73), nevertheless, one should interpret these findings with caution bearing in mind high heterogeneity of the analysis (Fig. 2B). The sensitivity analyses did not change these findings significantly (Table 3, 4).
Secondary endpoints
A substantial decline in the risk of all-cause mortality was observed in ticagrelor compared to clopidogrel users with a high level of heterogeneity (OR=0.54; 95% CI: 0.46-0.63, I2=74%, p<0.0001,Fig. 3A). Of note, after excluding the data provided by Lee et al. in the sensitivity analysis, the results of the analysis became insignificant although with no evidence of heterogeneity (OR=0.91; 95% CI: 0.67-1.23, I2=0%, p=0.53).
Ticagrelor did not reduce cardiovascular mortality significantly compared to clopidogrel, and the level of heterogeneity was low (OR=0.66; 95% CI: 0.41-1.06, I2=0%, p=0.09, Fig. 3B). Ticagrelor was associated with a numerical decrease in the rates of MI as opposed to clopidogrel with evidence of low heterogeneity (OR=0.82; 95% CI: 0.71-0.94, I2=38%, p=0.004, Fig. 3C). Nevertheless, after removing Lee et al's study in the sensitivity analysis, the pooled OR failed to reach statistical significance (OR=0.74; 95% CI: 0.51-1.07, I2=48%, p=0.11).
There were no significant differences in the rates of TVR between ticagrelor and clopidogrel users (OR=0.53; 95% CI: 0.21-1.35, I2=82%, p=0.18, Fig. 3D).
Ticagrelor demonstrated significant superiority over clopidogrel in reducing the risk of stroke with a low level of heterogeneity (OR=0.62; 95% CI: 0.49-0.78, I2=0%, p<0.0001, Fig. 4A), and this results were consistent in the leave-one-out sensitivity analyses.
Ticagrelor treatment was found to increase significantly the risk of major/minor bleeding (OR=1.73; 95% CI: 1.36-2.21, I2=0%, p<0.001) and minor bleeding (OR=1.73; 95%CI: 1.29-2.32, I2=0%,p<0.001, Fig. 4B and C). Moreover, the summary OR saved its statistical significance in the sensitivity analyses.
The risk of NACCE was not influenced by ticagrelor as compared to clopidogrel, and the level of heterogeneity was substantial (OR=0.76; 95% CI: 0.55-1.04, I2=78%, p=0.09, Fig. 4D).
Additional analyses with HR chosen as summary measure provided similar findings for all the available outcomes (Table 5).
Subgroup analyses
Subgroup analyses did not reveal any discrepancies in the results for MACCE depending on participants' age, sex, presence of diabetes mellitus (DM) or chronic kidney disease (CKD) (Table 6). The similar pattern was observed for major bleeding with exception of female patients who tended to have more major bleeding complications than male subjects with ticagrelor treatment (test for subgroup differences I2= 69.7% when HR was an effect measure).
Additional sensitivity analysis
As several groups of scientists conducted their studies investigating the data from KAMIR-NIH registry, we performed the sensitivity analysis with including these studies one by one instead of data from Sim et al. However, no major changes was detected in pooled OR for MACCE and major bleeding (Table 4).
Additionally, the estimated effect size did not altered significantly after eliminating the studies without propensity-score matching (MACCE: OR=0.72; 95% CI: 0.56-0.93, I2=62%,p=0.01; major bleeding: OR=1.24; 95% CI: 0.65-2.37, I2=80%, p=0.52).
Although several meta-analyses of RCTs devoted to this subject have been published recently, this review seems to be the first meta-analysis of observational studies on ticagrelor use in Asian ACS patients in real-world practice. As ticagrelor did not reduce significantly the risk of all-cause mortality, cardiovascular death, MI, TVR , we conclude that the decrease in the MACCE rates was driven mainly by the decline in the stroke incidence in ticagrelor compared to clopidogrel users, which was consistent even after excluding Lee et al's study. Additionally, while ticagrelor was not associated with the major bleeding risk, it was nevertheless accompanied with a considerable rise in major/minor and minor bleeding as opposed to clopidogrel.
The recent meta-analyses of RCTs demonstrated a strong association of ticagrelor with an increase in major bleeding among Asians without any ischaemic benefits. Notably, only two properly-designed RCTs with a sample size of 1907 patients are available at present. Strong inclusion and exclusion criteria, small sample sizes, low rates of events, a non-superiority design of Goto et al's trial, a retrospective design of the Asian sub-analysis of the PLATO trial make it difficult to extrapolate their findings on general Asian population. From this point of view, the fact that our meta-analysis included two large national-wide observational studies obviously increased the external validity of our results.
The inconsistency in the results between the observational investigations can be partially explained by the different characteristics of the study population among the reported studies. For example, among studies with low risk of bias, statins tended to be administered more frequently in investigations which failed to demonstrate the efficacy of ticagrelor. As a result, ticagrelor might not reach statistical significance in MACCE prevention in these studies because of low rates of events.
Additionally, a positive effect of ticagrelor on MACCE tended to be detected in the studies where study population had a higher prevalence of DM, CKD, dyslipidemia, and chronic heart failure as opposed to the studies with a neutral effect of ticagrelor on MACCE (Table 1). Consequently, it can be assumed that baseline risk profile of ACS patients should be bearing in mind while administering ticagrelor. This conception might be further supported by recently published data. Based on the national-wide KAMIR-NIH registry, Sim et al. found that ticagrelor proved its superiority over clopidogrel in prevention of all-cause death only in patients with high (>140 points) risk GRACE score. Moreover, the benefits of ticagrelor in patients with high ischaemic risk was demonstrated if bleeding risk according to ACUITY-HORIZONS score was low (below 20 points). Additionally, Wang et al. indicated that ticagrelor reduced the MACCE incidence in individuals with low baseline bleeding risk according to GRASADE score. On the other hand, the increase of major bleeding was shown only in patients with moderate-to-high bleeding potential. Further large-scale studies are required to establish the best strategies of ticagrelor use depending on baseline risk estimation.
The discrepancies in the findings among the retrieved studies also can be explained by different study endpoint definitions. Lee et al. considered as major bleeding the cases with intracranial haemorrhage or major gastrointestinal bleeding. Sim et al., meanwhile, used thrombolysis in myocardial infarction (TIMI) definition for major bleeding, which included a decrease in a haemoglobin level of more than 50g/l. Therefore, Lee et al. could underestimate the real impact of ticagrelor on major bleeding according to TIMI scale. Given the fact that the true magnitude of bias could not be evaluated, our data on the safety of ticagrelor should be considered with caution.
Our meta-analysis suggested that ticagrelor had a substantial protective effect on stroke in Asian ACS patients. Given the consistency between the included observational studies, these results are unlikely to be due to a play of chance. Of note, ticagrelor treatment increased, although insignificantly, the rates of stroke in the PLATO trial (HR=1.17; 95% CI: 0.91-1.52), Asian sub-analysis of PLATO trial (HR=1.01; 95% CI: 0.44-2.32) and Goto et al's trial (HR=1.50; 95% CI: 0.54-4.23). This discrepancy are likely to be related to the aforementioned limitations of RCTs. Additionally, our findings are consistent with recently published data claiming the benefits of ticagrelor for stroke prevention.
Concerning the subgroup analyses, ticagrelor seemed to increase the major bleeding complications among female as opposed to male subjects. Although the findings from subgroup analyses could be misleading, conflicting and limited data from other trials calls for high-quality studies to investigate the gender differences in bleeding risk with ticagrelor treatment, especially in Asian population.
The another direction for future researches is the application of reduced doses of ticagrelor in Asians. Although recent studies provided promising findings, the current evidence is somewhat insufficient. The future large-scale investigations are warranted to reach definite conclusions.
This meta-analysis suggested that ticagrelor might reduce the risk of MACCE mainly by decreasing the stroke incidence in Asian ACS patients. As compared to clopidogrel, ticagrelor did not demonstrated an significant effect on the rates of all-cause, cardiovascular mortality, MI, TVR, NACCE, and major bleeding. Ticagrelor was associated with a considerable rise in the major/minor and minor bleeding complications. However, further high-quality studies are of crucial importance, especially on the use of ticagrelor depending on baseline risk profile of patients.
There are some limitations of this review that should be mentioned. Firstly, selection bias are prone for observational studies and could not be avoided in their pooled analysis. Secondly, our meta-analysis was limited by a low number of the included studies. Thirdly, almost all the studies in the meta-analysis had a retrospective design. Consequently, further high-quality researches are needed to ascertain our findings.
Study name, year | Chen et al., 201616 | Lee et al., 201817 | Nur'amin et al., 201718 | Sim et al., 201819 | Wang et al., 201820 | Xin et al., 201821 |
Country | Taiwan | Taiwan | Indonesia | Korea | China | China |
Study design | RS | RS | RS | RS | RS | RS |
Study duration, months | 19 | 24 | Unknown | 49 | 34 | 12 |
Sample size, n | 928 | 27339 | 361 | 7791 | 20816 | 206 |
Ticagrelor | 324 | 2844 | 111 | 1554 | 779 | 145 |
Clopidogrel | 604 | 24495 | 250 | 6237 | 20037 | 61 |
Mean age, years | ||||||
Ticagrelor | 63.8 | 62.2 | 55.8 | 62.1 | 60.54 | 61.9 |
Clopidogrel | 63.7 | 63.1 | 55.9 | 62.6 | 60.97 | 65.0 |
Males, % | ||||||
Ticagrelor | 79.9 | 81.9 | 92.8 | 79.4 | 71.1 | 68.3 |
Clopidogrel | 79.5 | 78.6 | 92.8 | 77.9 | 71.7 | 73.8 |
Type of participants | ACS | Acute MI | PCI | Acute MI | ACS | ACS |
Smoking, % | Unknown | |||||
Ticagrelor | 47.3 | 36.0 | 64.6 | 57.3 | 55.2 | |
Clopidogrel | 46.0 | 43.6 | 65.2 | 57.8 | 55.7 | |
DM, % | ||||||
Ticagrelor | 37.1 | 35.9 | 40.5 | 24.6 | 24.6 | 37.9 |
Clopidogrel | 42.9 | 38.2 | 41.6 | 24.5 | 23.8 | 34.4 |
Hypertension, % | ||||||
Ticagrelor | 55.4 | 62.2 | 58.6 | 45.9 | 57.9 | 61.4 |
Clopidogrel | 57.6 | 64.5 | 64.8 | 47.6 | 54.7 | 57.4 |
CKD, % | Unknown | |||||
Ticagrelor | 39.3 | 14.6 | 6.3 | - | 2.4 | 28.9 |
Clopidogrel | 39.3 | 16.1 | 8.8 | - | 2.4 | 40.9 |
Dyslipidemia, % | Unknown | |||||
Ticagrelor | 46.0 | 42.8 | 34.2 | 11.0 | - | 19.3 |
Clopidogrel | 44.2 | 42.6 | 33.6 | 11.3 | - | 22.9 |
Previous MI | Unknown | Unknown | ||||
Ticagrelor | 8.0 | - | - | 4.1 | 15.5 | 15.2 |
Clopidogrel | 8.5 | - | - | 3.5 | 17.3 | 22.9 |
Previous stroke, % | ||||||
Ticagrelor | 8.0 | 6.5 | 36.0 | 3.9 | 8.9 | 22.7 |
Clopidogrel | 9.4 | 8.1 | 43.6 | 3.5 | 8.3 | 31.1 |
Heart failure, % | Unknown | Unknown | ||||
Ticagrelor | 5.4 | 8.9 | 20.7 | 0.6 | ||
Clopidogrel | 7.1 | 10.5 | 27.2 | 0.3 | ||
ACEI/ARB use, % | Unknown | |||||
Ticagrelor | 47.3 | 76.9 | 73.0 | 79.5 | 82.1 | |
Clopidogrel | 55.3 | 76.4 | 78.4 | 79.4 | 67.2 | |
Beta-blocker use, % | ||||||
Ticagrelor | 50.9 | 71.3 | 73.0 | 85.0 | 75.5 | 77.9 |
Clopidogrel | 58.9 | 71.0 | 78.0 | 86.9 | 75.4 | 52.4 |
Statin use, % | Unknown | |||||
Ticagrelor | 78.6 | 83.8 | 96.8 | 96.1 | 95.2 | |
Clopidogrel | 69.6 | 79.0 | 97.3 | 96.4 | 96.7 | |
Glycoprotein IIb/IIIa use, % | Unknown | Unknown | Unknown | |||
Ticagrelor | 0.4 | 18.3 | 18.2 | |||
Clopidogrel | 1.8 | 18.7 | 18.4 | |||
Follow-up, months | 12 | 12 | 12 | 12 | 12 | 6 |
Adjustment method | PSM | PSM | Bivariate analysis | PSM | PSM | UA |
Study endpoints | 1), 2), 3), 4), 5), 6) | 1), 3), 4), 5) | 1), 3), 7) | 1), 3), 4), 5), 6), 7) | 1), 3), 4), 5), 6), 7) | 2), 3), 4), 5), 6), |
Definition of MACCE | 2), 3), 4) | 1), 3), 4) | 1), 3), 7) | 1), 3), 4), 7) | 1), 3), 4), 7) | 2), 3), 4), 7) |
Definitions of major bleeding | PLATO | Self-defined | None | TIMI | BARC | TIMI |
he table presents only the characteristics for which the published data from most of the studies were available.
RS: retrospective; ACS: acute coronary syndrome; MI: myocardial infarction; PCI: percutaneous coronary intervention; DM: diabetes mellitus; CKD: chronic kidney disease; ACEI: angiotensin-converting enzyme inhibitor; ARB: angiotensin receptor blocker; PSM: propensity-score matching; UA: unadjusted; MACCE: major adverse cardiac and cerebrovascular events; PLATO:Platelet Inhibition and Patient Outcomes; TIMI: thrombolysis in myocardial infarction; BARC: Bleeding Academic Research Consortium
Outcomes: 1) all-cause mortality, 2) cardiovascular mortality, 3) MI, 4) stroke, 5) major bleeding, 6) minor bleeding, 7) repeat PCI..
Study name, year | Chen et al.16 | Lee et al.17 | Nur'amin et al.18 | Sim et al.19 | Wang et al.20 | Xin et al.21 |
Representativeness of the exposed cohort | * | * | - | * | * | * |
Selection of the non exposed cohort | * | * | * | * | * | * |
Ascertainment of exposure | * | * | * | * | * | * |
Demonstration that outcome of interest was not present at start of study | * | * | * | * | * | * |
Comparability | * | * | * | * | * | - |
Assessment of outcome | * | * | * | * | * | - |
Long enough follow-upa | * | * | * | * | * | - |
Adequacy of follow up of cohorts | - | - | * | * | - | * |
* - low risk of bias; "-" - unclear or high risk of bias.
a if 1 year or more.
Study name, year | Ticagrelor versus clopidogrel | |||||
MACCE | Major bleeding | |||||
OR (95% CI) | I2 statistics, % | p-value | OR (95% CI) | I2 statistics, % | p-value | |
Chen et al.16 | 0.62(0.44, 0.86) | 75 | 0.004 | 1.24 (0.60, 2.56) | 81 | 0.56 |
Lee et al.17 | 0.58(0.37, 0.92) | 71 | 0.02 | 1.28 (0.62, 2.65) | 63 | 0.50 |
Nur'amin et al.18 | 0.70 (0.55, 0.89) | 53 | 0.003 | - | - | - |
Sim et al.19 | 0.55(0.41, 0.74) | 47 | 0.0001 | 0.89 (0.53, 1.49) | 46 | 0.65 |
Wang et al.20 | 0.58(0.41, 0.83) | 75 | 0.003 | 0.95 (0.52, 1.74) | 75 | 0.87 |
Xin et al.21 | 0.63(0.46, 0.87) | 74 | 0.004 | 1.24 (0.65, 2.37) | 80 | 0.52 |
Notes: MACCE: major adverse cardiac and cerebrovascular events; OR: odds ratio; CI: confidence interval.
Study name | Ticagrelor versus clopidogrel | |||||
MACCE | Major bleeding | |||||
OR (95% CI) | I2 statistics, % | p-value | OR (95% CI) | I2 statistics, % | p-value | |
Choe et al.22 | 0.58(0.48, 0.69) | 35 | <0.0001 | 0.96 (0.66, 1.40) | 57 | 0.83 |
Kang et al.23 | 0.56(0.46, 0.70) | 36 | <0.0001 | 0.94 (0.63, 1.39) | 44 | 0.75 |
Park et al.24 | 0.60(0.46, 0.79) | 51 | 0.0003 | 1.12 (0.62, 2.03) | 74 | 0.71 |
Notes: MACCE: major adverse cardiac and cerebrovascular events; OR: odds ratio; CI: confidence interval.
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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4.1 MACCE | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.80 [0.65, 1.00] | |
4.1.1 Elderly | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.81 [0.63, 1.05] | |
4.1.2 Non-elderly | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.56 [0.20, 1.59] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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5.1 MACCE | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.76 [0.55, 1.04] | |
5.1.1 Male | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.72 [0.38, 1.36] | |
5.1.2 Female | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.94 [0.29, 3.02] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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6.1 MACCE | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.78 [0.68, 0.89] | |
6.1.1 DM | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.73 [0.60, 0.89] | |
6.1.2 Non-DM | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.82 [0.68, 0.99] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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7.1 MACCE | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.81 [0.71, 0.93] | |
7.1.1 CKD | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.86 [0.62, 1.17] | |
7.1.2 non-CKD | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.65 [0.29, 1.43] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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10.1 Major bleeding | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.35 [0.82, 2.23] | |
10.1.1 Male | 2 | Hazard Ratio (IV, Random, 95% CI) | 0.99 [0.59, 1.64] | |
10.1.2 Female | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.84 [1.19, 2.86] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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12.1 Major bleeding | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.23 [0.77, 1.94] | |
12.1.1 DM | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.00 [0.68, 1.47] | |
12.1.2 Non-DM | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.45 [0.50, 4.18] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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14.1 Major bleeding | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.26 [0.84, 1.88] | |
14.1.1 CKD | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.44 [0.40, 5.17] | |
14.1.2 Non-CKD | 2 | Hazard Ratio (IV, Random, 95% CI) | 1.33 [0.75, 2.36] |
Outcome or Subgroup | Studies | Participants | Statistical Method | Effect Estimate |
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Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.2 Major bleeding.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.3 All-cause mortality.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.4 Cardiovascular mortality.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.5 Myocardial infarction.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.6 Target vessel revascularization.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.7 Stroke.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.8 Major or minor bleeding.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.9 Minor bleeding.
Forest plot of comparison: 1 Ticagrelor vs Clopidogrel, outcome: 1.10 NACCE.
Forest plot of comparison: 4 Ticagrelor vs Clopidogrel in subgroup analysis according to age (HR), outcome: 4.1 MACCE.
Forest plot of comparison: 5 Ticagrelor vs Clopidogrel in subgroup analysis according to sex (HR), outcome: 5.1 MACCE.
Forest plot of comparison: 6 Ticagrelor vs Clopidogrel in subgroup analysis according to DM status (HR), outcome: 6.1 MACCE.
Forest plot of comparison: 7 Ticagrelor vs Clopidogrel in subgroup analysis according to CKD status (HR), outcome: 7.1 MACCE.
Forest plot of comparison: 8 Ticagrelor vs Clopidogrel in subgroup analysis according to age (OR), outcome: 8.1 Major bleeding.
Forest plot of comparison: 9 Ticagrelor vs Clopidogrel in subgroup analysis according to age (HR), outcome: 9.1 Major bleeding.
Forest plot of comparison: 10 Ticagrelor vs Clopidogrel in subgroup analysis according to sex (HR), outcome: 10.1 Major bleeding.
Forest plot of comparison: 11 Ticagrelor vs Clopidogrel in subgroup analysis according to sex (OR), outcome: 11.1 Major bleeding.
Forest plot of comparison: 12 Ticagrelor vs Clopidogrel in subgroup analysis according to DM (HR), outcome: 12.1 Major bleeding.
Forest plot of comparison: 13 Ticagrelor vs Clopidogrel in subgroup analysis according to DM status (OR), outcome: 13.1 Major bleeding.
Forest plot of comparison: 14 Ticagrelor vs Clopidogrel in subgroup analysis according to CKD (HR), outcome: 14.1 Major bleeding.
Forest plot of comparison: 15 Ticagrelor vs Clopidogrel in subgroup analysis according to CKD status (OR), outcome: 15.1 Major bleeding.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.1 MACCE.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.3 All-cause mortality.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.4 Cardiovascular mortality.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.5 Myocardial infarction.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.6 Stroke.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.7 Target vessel revascularization.
Forest plot of comparison: 2 Ticagrelor vs Clopidogrel (HR), outcome: 2.2 Major bleeding.