Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе: https://open.uns.ac.rs/handle/123456789/7753
Nаziv: Echocardiographic parameters as predictors of in-hospital mortality in patients with acute coronary syndrome undergoing percutaneous coronary intervention
Аutоri: Miroslava Sladojević
Srđan Sladojević 
Dubravko Ćulibrk 
Snežana Tadić 
Robert Jung 
Ključnе rеči: Echocardiography;prediction;mortality;Acute Coronary Syndrome;Percutaneous Coronary Intervention
Dаtum izdаvаnjа: 1-јан-2014
Čаsоpis: The Scientific World Journal
Sažetak: Different ways have been used to stratify risk in acute coronary syndrome (ACS) patients. The aim of the study was to examine the usefulness of echocardiographic parameters as predictors of in-hospital outcome in patients with ACS after percutaneous coronary intervention (PCI). A data of 2030 patients with diagnosis of ACS hospitalized from December 2008 to December 2011 was used to develop a risk model based on echocardiographic parameters using the binary logistic regression. This model was independently evaluated in validation cohort prospectively (954 patients admitted during 2012). In-hospital mortality in derivation cohort was 7.73%, and 6.28% in validation cohort. Developed model has been designed with 4 independent echocardiographic predictors of in-hospital mortality: left ventricular ejection fraction (LVEF RR = 0.892; 95%CI = 0.854 - 0.932, P < 0.0005), aortic leaflet separation diameter (AOvs RR = 0.131; 95%CI = 0.027 - 0.627, P = 0.011), right ventricle diameter (RV RR = 2.675; 95%CI = 1.109 - 6.448, P = 0.028) and right ventricle systolic pressure (RVSP RR = 1.036; 95%CI = 1.000 - 1.074, P = 0.048). Model has good prognostic accuracy (AUROC = 0.84) and it retains good (AUROC = 0.78) when testing on the validation cohort. Risks for in-hospital mortality after PCI in ACS patients using echocardiographic measurements could be accurately predicted in contemporary practice. Incorporation of such developed model should facilitate research, clinical decisions, and optimizing treatment strategy in selected high risk ACS patients. © 2014 Miroslava Sladojevic et al.
URI: https://open.uns.ac.rs/handle/123456789/7753
DOI: 10.1155/2014/818365
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