Dataset Information

Exploring diagnostic and prognostic predictive values of microRNAs for acute myocardial infarction: A PRISMA-compliant systematic review and meta-analysis.

ABSTRACT:

Objective

Previous investigations yielded inconsistent results for diagnostic and prognostic predictive values of MicroRNAs (miRNAs) for acute myocardial infarction (AMI).

Methods and results

We systematically searched on PubMed and Web of Science for articles explored association of miRNAs and AMI published from January 1989 to March 2019. For diagnostic studies, a summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR), which indicated the accuracy of microRNAs in the differentiation of AMI and no AMI, were calculated from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of each study. In addition, the summary receive-operating characteristics (SROC) curve was constructed to summarize the TP and FP rates. For follow-up study, we computed hazard ratios (HRs) and 95% confidence intervals (CIs) for individual clinical outcomes. The meta-analysis showed a sensitivity [0.72 (95% CI: 0.61--0.81)] and specificity [0.88 (95% CI: 0.79--0.94)] of miR-1 for AMI. In addition, miR-133 showed a sensitivity [0.73 (95% CI: 0.55--0.85)] and specificity [0.88 (95% CI: 0.74--0.95)] for AMI. Moreover, the present study showed a sensitivity [0.83 (95% CI: 0.74--0.89)] and specificity [0.96 (95% CI: 0.82--0.99)] of miR-208 for AMI. A significant association was found between miR-208 and mortality after AMI (HR 1.09, 95% CI 1.01--1.18). It also indicated a sensitivity [0.84 (95% CI: 0.70--0.92)] and specificity [0.97 (95% CI: 0.87--0.99)] of miR-499 for AMI.

Conclusions

Circulating miR-1, miR-133, miR-208, and miR-499 showed diagnostic values in AMI.

SUBMITTER: Lee GK

PROVIDER: S-EPMC8294880 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Exploring diagnostic and prognostic predictive values of microRNAs for acute myocardial infarction: A PRISMA-compliant systematic review and meta-analysis.

Lee Gien-Kuo GK Hsieh Yen-Ping YP Hsu Shang-Wei SW Lan Shou-Jen SJ

Medicine 20210701 29

<h4>Objective</h4>Previous investigations yielded inconsistent results for diagnostic and prognostic predictive values of MicroRNAs (miRNAs) for acute myocardial infarction (AMI).<h4>Methods and results</h4>We systematically searched on PubMed and Web of Science for articles explored association of miRNAs and AMI published from January 1989 to March 2019. For diagnostic studies, a summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnos ...[more]

PMID: 34398018

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Project description:BackgroundDepression is common in the aftermath of myocardial infarction (MI) and may not only lead to impaired long-term quality of life, but also cause increased mortality among patients with MI. The reported prevalence of depression among patients with MI varied considerably across studies, for which a pooled prevalence was obtained in the only 1 meta-analysis conducted in March 2004. Subsequently, numerous relevant studies have been published, indicating the need for an update on the pooled prevalence. Therefore, this study was aimed at updating the pooled prevalence of depression among patients with MI.MethodsA comprehensive literature search in 3 electronic databases, PubMed, Embase, and PsycINFO, was performed in April 2018. The heterogeneity across studies was examined by the Cochran's Q test and quantified by the I statistic. If significant heterogeneity was observed, meta-regression analyses and subgroup analyses were performed to identify the source of heterogeneity. Publication bias was assessed by a funnel plot and verified by the Egger's and Begg's tests.ResultsNineteen eligible studies conducted in 10 countries were included, which consisted of 12,315 patients with MI, among whom 3818 were identified with depression. High heterogeneity was observed across the eligible studies (I = 98.4%), with the reported prevalence of depression ranging from 9.17% to 65.88%. The pooled prevalence of depression among patients with MI was 28.70% (95% CI: 22.39-35.46%) by a random effects model. Subgroup analyses showed that the pooled prevalence differed significantly by region, tool used to identify depression, study quality, sex, race, anterior MI, and diabetes status (P < .05). Meta-regression analyses did not identify any moderators of heterogeneity, and the heterogeneity was high within most subgroups. Nonetheless, for unmarried subjects, the heterogeneity was low (I = 19.5). The Egger's test and the Begg's test indicated no evidence of publication bias (P > .05).ConclusionsGiven the high pooled prevalence of depression found in this study and the association between depression and adverse health outcomes among patients with MI, more psychological resources including early assessment and effective treatment of depression should be allocated to patients with MI.

Project description:Background Cardiovascular magnetic resonance imaging is considered the reference methodology for cardiac morphology and function but requires manual postprocessing. Whether novel artificial intelligence-based automated analyses deliver similar information for risk stratification is unknown. Therefore, this study aimed to investigate feasibility and prognostic implications of artificial intelligence-based, commercially available software analyses. Methods and Results Cardiovascular magnetic resonance data (n=1017 patients) from 2 myocardial infarction multicenter trials were included. Analyses of biventricular parameters including ejection fraction (EF) were manually and automatically assessed using conventional and artificial intelligence-based software. Obtained parameters entered regression analyses for prediction of major adverse cardiac events, defined as death, reinfarction, or congestive heart failure, within 1 year after the acute event. Both manual and uncorrected automated volumetric assessments showed similar impact on outcome in univariate analyses (left ventricular EF, manual: hazard ratio [HR], 0.93 [95% CI 0.91-0.95]; P<0.001; automated: HR, 0.94 [95% CI, 0.92-0.96]; P<0.001) and multivariable analyses (left ventricular EF, manual: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001; automated: HR, 0.95 [95% CI, 0.92-0.98]; P=0.001). Manual correction of the automated contours did not lead to improved risk prediction (left ventricular EF, area under the curve: 0.67 automated versus 0.68 automated corrected; P=0.49). There was acceptable agreement (left ventricular EF: bias, 2.6%; 95% limits of agreement, -9.1% to 14.2%; intraclass correlation coefficient, 0.88 [95% CI, 0.77-0.93]) of manual and automated volumetric assessments. Conclusions User-independent volumetric analyses performed by fully automated software are feasible, and results are equally predictive of major adverse cardiac events compared with conventional analyses in patients following myocardial infarction. Registration URL: https://www.clinicaltrials.gov; Unique identifiers: NCT00712101 and NCT01612312.

Dataset Information

Exploring diagnostic and prognostic predictive values of microRNAs for acute myocardial infarction: A PRISMA-compliant systematic review and meta-analysis.

Objective

Methods and results

Conclusions

Publications

Exploring diagnostic and prognostic predictive values of microRNAs for acute myocardial infarction: A PRISMA-compliant systematic review and meta-analysis.

Similar Datasets

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