Background: Conflicting results have been reported on the association of C-reactive protein (CRP) level with adverse outcomes in patients with stable coronary artery disease (CAD). The objective of this meta-analysis was to evaluate the predictive value of baseline CRP level in stable CAD patients.Methods: Two reviewers independently searched PubMed and Embase databases from their inception to November 28, 2021 to identify studies assessing the value of baseline CRP level in predicting adverse outcomes in stable CAD patients. The endpoints of interest included cardiovascular mortality, all-cause mortality, or major adverse cardiovascular events (MACEs). The predictive value of CRP level was estimated by pooling the multivariable adjusted risk ratio with 95% confidence intervals (CI) compared the highest to the lowest CRP level.Results: Twenty-six studies involving of 22,602 patients with stable CAD satisfied the inclusion criteria. In a comparison of the highest with the lowest CRP level, the pooled multivariable adjusted risk ratio was 1.77 (95% CI 1.60-1.96) for MACEs, 1.64 (95% CI 1.13-2.33) for cardiovascular mortality, and 1.62 (95% CI 2.62-5.12) for all-cause mortality, respectively. Subgroup analyses indicated that the values of elevated CRP level in predicting MACEs were consistently observed in each subgroup.Conclusion: Elevated baseline CRP level was an independent predictor of MACEs, cardiovascular mortality, and all-cause mortality in patients with stable CAD. Baseline CRP level can provide important predictive information in stable CAD patients.Abbreviations: CAD = coronary artery disease, CI = confidence intervals, CRP = C-reactive protein, MACEs = major adverse cardiovascular events, RR = risk ratio.
Enterprise Application Integration (EAI) is a critical function that enables collaboration within an enterprise.Historically, EAI occurs at two levels: syntactic and semantic. Syntactic Integration refers to interchange of data without regard to explicit representation of the meaning -this includes parameter passing mechanisms, external data accesses, and timing mechanisms. Semantic Integration refers to the interchange of data where there is explicit representation of the meaning of the data. Semantic integration produces integrated systems and applications that are qualitatively superior and robust.In this paper, we present an ontology-driven approach to achieving semantically-assured EAI. In particular, we will describe how ontologies can be used to semantically map concepts among information systems formats and how complex, end-to-end integration may be achieved using SOA-enabled workflow automation.
Enterprise architecture development is inherently collaborative in nature, where members of the model development team share different perspectives and facets of the enterprise or system. In this paper, we describe an approach by which the executable content of enterprise architectures can be extracted from core enterprise architectural modeling languages (e.g., IDEF, UML) and be extended to develop abstractions of various discrete simulation paradigms such as Colored Petri nets, Monte Carlo, and Discrete Event Simulation. We also describe the use of representation structures at various levels of abstraction to enable collaboration by members of the systems modeling and simulation team in order to perform numerous verification and validation checks of the system or system-of-systems that is being modeled. Use of various levels of enterprise architecture abstractions facilitates not only model reuse but also collaborative effort within a team of diverse skills ranging from domain expertise, enterprise modeling, abstract and concrete simulation modeling, and design of experiments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.