Biomarkers for Risk Stratification in Patients With Type A Acute Aortic Dissection

Type A acute aortic dissection (AAD) is a fatal disease and thus, accurate and objective risk stratification is essential. In this study, we evaluated the prognostic value of readily available and assessable biomarkers in patients with type A AAD. This was a retrospective, multicenter, observational study. A total of 703 patients with type A AAD diagnosed using contrast-enhanced computed tomography were included. Therapeutic strategies were left to the physician's discretion in a real-world clinical setting. The prognostic value for in-hospital mortality was examined in 15 circulating biomarkers on admission, which are routinely available in clinical practice. Of the 703 patients, 126 (17.9%) died during the hospitalization. Of the 15 biomarkers, the multivariable analysis identified positive cardiac troponin, a low total bilirubin (T-Bil) level, and increased levels of brain natriuretic peptide (BNP) and lactate dehydrogenase (LDH) as significant predictors of in-hospital death. The receiver operating characteristics curve analysis showed that these 4 biomarkers had an independent additive prognostic value. With the cut-off values of T-Bil, BNP, and LDH, in combination with positive troponin, the increase in the number of positive biomarkers was progressively associated with higher in-hospital mortality from 1.3% to 9.8%, 20.5%, 36.4%, and 75.0% (p <0.001). In conclusion, in patients with type A AAD, positive cardiac troponin, a low T-Bil level, and increased levels of BNP and LDH on admission were related to higher in-hospital mortality, with an incremental prognostic value, suggesting that the readily available and assessable biomarkers can aid in decision-making in therapeutic strategies. Type A acute aortic dissection (AAD) is a fatal disease and thus, accurate and objective risk stratification is essential. In this study, we evaluated the prognostic value of readily available and assessable biomarkers in patients with type A AAD. This was a retrospective, multicenter, observational study. A total of 703 patients with type A AAD diagnosed using contrast-enhanced computed tomography were included. Therapeutic strategies were left to the physician's discretion in a real-world clinical setting. The prognostic value for in-hospital mortality was examined in 15 circulating biomarkers on admission, which are routinely available in clinical practice. Of the 703 patients, 126 (17.9%) died during the hospitalization. Of the 15 biomarkers, the multivariable analysis identified positive cardiac troponin, a low total bilirubin (T-Bil) level, and increased levels of brain natriuretic peptide (BNP) and lactate dehydrogenase (LDH) as significant predictors of in-hospital death. The receiver operating characteristics curve analysis showed that these 4 biomarkers had an independent additive prognostic value. With the cut-off values of T-Bil, BNP, and LDH, in combination with positive troponin, the increase in the number of positive biomarkers was progressively associated with higher in-hospital mortality from 1.3% to 9.8%, 20.5%, 36.4%, and 75.0% (p <0.001). In conclusion, in patients with type A AAD, positive cardiac troponin, a low T-Bil level, and increased levels of BNP and LDH on admission were related to higher in-hospital mortality, with an incremental prognostic value, suggesting that the readily available and assessable biomarkers can aid in decision-making in therapeutic strategies. Crystal Ball of Prognostication: Role of Biomarkers for Risk Stratification in Patient With Type A Acute Aortic DissectionAmerican Journal of CardiologyVol. 213PreviewAcute type A aortic dissection (ATAAD) is a devastating disease that requires prompt diagnosis and treatment. Risk stratification algorithms are generally simple bedside tools that guide surgeons in the decision-making process when considering whether or not to proceed with surgical intervention in patients presenting with extreme clinical conditions. These risk models, usually consisting of clinical variables, not only assist mortality prediction but are also useful for evaluation of quality of risk assessment and improvement tools. Full-Text PDF