Growth differentiation factor-15 significantly improves prognostication of short-term mortality in acute settings
Author | Affiliation |
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Čerlinskaitė, Kamilė | |
Date |
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2020-08-29 |
Heart Failure – Acute Heart Failure: Biomarkers
Introduction Acute dyspnoea is one of the most common complaints in the emergency department with a diverse range of diagnoses and a vastly different prognosis. There is still a need for a reliable biomarker for prognostication in acute settings. Purpose To evaluate and compare the role of three biomarkers, which represent different pathophysiological pathways, in stratifying short-term mortality risk in patients admitted to the emergency department with acute dyspnoea. Methods Prospective observational cohort enrolled 1455 acute dyspnoea patients admitted to emergency departments of two university centres from 2015 to 2017. The present study included 867 (71 [62–79] years, 40% female) patients who had N-terminal pro–B-type natriuretic peptide (NT-proBNP), high-sensitive troponin T (hsTnT) and growth differentiation factor 15 (GDF-15) measured at admission. Clinical variables known to affect prognosis (age, gender, use of beta blockers, angiotensin-converting-enzyme inhibitors/angiotensin II receptor blockers at admission, history of chronic heart failure, diabetes and coronary artery disease, systolic blood pressure, heart rate, creatinine, sodium, haemoglobin and C-reactive protein levels) were used to build a baseline model for all-cause 3-month mortality risk prediction in acute dyspnoea [“Clinical score”]. The ability of each biomarker to improve risk prediction on top of the Clinical score was evaluated by area under the curve (AUC), comparing AUC of the model with the biomarker to the AUC of the clinical model alone using the DeLong test. The clinical benefit in risk prediction of adding a biomarker was further assessed by reclassification analysis, including the net reclassification improvement (NRI) and the integrated discrimination index (IDI). The statistical analyses were performed using R statistical software. P value of <0.05 was considered statistically significant. Results Mortality at [...].
Funding(s) | Grant No | Project ID |
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Lietuvos mokslo taryba |