Biomarkers for clinical decision-making in the management of pulmonary embolism
BACKGROUND: Pulmonary embolism (PE) is associated with high all-cause and PE-related mortality and requires individualized management. After confirmation of PE, a refined risk stratification is particularly warranted among normotensive patients. Previous prognostic models favored combinations of ech...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| In: |
Clinical chemistry
Year: 2016, Jahrgang: 63, Heft: 1, Pages: 91-100 |
| ISSN: | 1530-8561 |
| DOI: | 10.1373/clinchem.2016.255240 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: http://dx.doi.org/10.1373/clinchem.2016.255240 Verlag, kostenfrei, Volltext: http://clinchem.aaccjnls.org/content/63/1/91 
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| Verfasserangaben: | Evangelos Giannitsis and Hugo A. Katus |
MARC
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| 520 | |a BACKGROUND: Pulmonary embolism (PE) is associated with high all-cause and PE-related mortality and requires individualized management. After confirmation of PE, a refined risk stratification is particularly warranted among normotensive patients. Previous prognostic models favored combinations of echocardiography or computed tomography suggestive of right ventricular (RV) dysfunction together with biomarkers of RV dysfunction (natriuretic peptides) or myocardial injury (cardiac troponins) to identify candidates for thrombolysis or embolectomy. In contrast, current predictive models using clinical scores such as the Pulmonary Embolism Severity Index (PESI) or its simplified version (sPESI) rather seek to identify patients, not only those at higher risk requiring observation for early detection of hemodynamic decompensation, and the need for initiation of rescue reperfusion therapy, but also those at low risk qualifying for early discharge and outpatient treatment. Almost all prediction models advocate the additional measurement of biomarkers along with imaging of RV dysfunction as part of a comprehensive algorithm. CONTENT: The following mini-review will provide an updated overview on the individual components of different algorithms with a particular focus on guideline-recommended and new, less-established biomarkers for risk stratification, and how biomarkers should be implemented and interpreted. SUMMARY: Ideally, biomarkers should be part of a comprehensive risk stratification algorithm used together with clinical risk scores as a basis, and/or imaging. For this purpose, cardiac troponins, including high-sensitivity troponin generations, natriuretic peptides, and h-FABP (heart-type fatty acid-binding protein) are currently recommended in guidelines. There is emerging evidence for several novel biomarkers that require further validation before being applied in clinical practice. | ||
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