Searching across hundreds of databases
This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
High-sensitivity troponin assay brought new challenges as we detect elevated concentration in many other diseases, and it became difficult to distinguish the real cause of this elevation. In this notion, diagnosis of acute coronary syndrome (ACS) remains a challenge in emergency department (ED). We aim to examine different approaches for rule-in and rule-out of ACS using risk scores, copeptin, and coronary computed tomography angiography (CCTA). A prospective observational study was designed to evaluate chest pain patients. Consecutive adult patients admitted to the ED with a chief complaint of chest pain due to any cause were included. All patients were followed-up for 6 months after discharge for major adverse cardiovascular events and readmissions. Admission data, ED processes, and diagnoses were analyzed. One hundred forty-six patients were included, average age was 63 ± 13.4 years, and 95 (65.1%) were male. Global Registry of Acute Coronary Events (GRACE) and History, ECG, Age, Risk factors, Troponin (HEART) scores showed good prognostic abilities, but HEART combination with copeptin improves diagnoses of myocardial infarction (area under the curve [AUC] 0.764 vs AUC 0.864 P = .0008). Patients with elevated copeptin were older, had higher risk scores, and were more likely to be admitted to hospital and diagnosed with ACS in ED. For copeptin, AUC was 0.715 (95% confidence interval 0.629-0.803), and for combination with troponin, AUC of 0.770 (0.703-0.855) did not improve rule-in of myocardial infarction. High-sensitivity troponin I assay alongside prior stroke, history of carotid stenosis, dyslipidemia, use of diuretics, and electrocardiogram changes (left bundle branch block or ST depression) are good predictors of myocardial infarction (χ² = 52.29, AUC = 0.875 [0.813-0.937], P < .001). The regression analysis showed that combination of copeptin and CCTA without significant stenosis can be used for ACS rule-out (χ² = 26.36, P < .001, AUC = 0.772 [0.681-0.863], negative predictive value of 96.25%). For rule-in of ACS, practitioner should consider not only scores for risk stratification but carefully analyze medical history and nonspecific electrocardiogram changes and even with normal troponin results, we strongly suggest thorough evaluation in chest pain unit. For rule-out of ACS combination of copeptin and CCTA holds great potential.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count:
The SciCrunch Infrastructure was developed as a cooperative data platform to be used by diverse communities in making data more FAIR.
scicrunch.org
