Background Increased D-dimer levels have been shown to correlate with adverse outcomes in various clinical conditions. However, few studies with a large sample size have been performed thus far to evaluate the prognostic value of D-dimer in patients with infective endocarditis (IE). Methods 613 patients with IE were included in the study and categorized into two groups according to the cut-off of D-dimer determined by receiver operating characteristic (ROC) curve analysis for in-hospital death: > 3.5 mg/L (n = 89) and ≤ 3.5 mg/L (n = 524). Multivariable regression analysis was used to determine the association of D-dimer with in-hospital adverse events and six-month death. Results In-hospital death (22.5% vs. 7.3%), embolism (33.7% vs 18.2%), and stroke (29.2% vs 15.8%) were significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L. Multivariable analysis showed that D-dimer was an independent risk factor for in-hospital adverse events (odds ratio = 1.11, 95% CI 1.03–1.19, P = 0.005). In addition, the Kaplan–Meier curve showed that the cumulative 6-month mortality was significantly higher in patients with D-dimer > 3.5 mg/L than in those with D-dimer ≤ 3.5 mg/L (log-rank test = 39.19, P < 0.0001). Multivariable Cox regression analysis showed that D-dimer remained a significant predictor for six-month death (HR 1.11, 95% CI 1.05–1.18, P < 0.001). Conclusions D-dimer is a reliable prognostic biomarker that independently associated with in-hospital adverse events and six-month mortality in patients with IE.
Background Accurate risk assessment and prospective stratification are of great importance for treatment of acute coronary syndrome (ACS). However, the optimal risk evaluation systems for predicting different type of ACS adverse events in Chinese population have not been established. Material/Methods Our data were derived from the Improving Care for Cardiovascular Disease in China-ACS (CCC-ACS) Project, a multicenter registry program. We incorporated data on 44 750 patients in the study. We compared the performance of the following 4 different risk score systems with regard to prediction of in-hospital adverse events: the Global Registry for Acute Coronary Events (GRACE) risk score system; the age, creatinine and ejection fraction (ACEF) risk score system, and its modified version (AGEF), and the Canada Acute Coronary Syndrome (C-ACS) risk assessment system. Results Admission AGEF risk score was a better prognosis index of potential for in-hospital mortality for patients with ST segment elevation myocardial infarction (STEMI) than GRACE risk score (AUC: 0.845 vs 0.819, P =0.012), ACEF (AUC: 0.845 vs 0.827, P =0.014), C-ACS (AUC: 0.845 vs 0.767, P <0.001). In patients with non-ST segment-elevation acute coronary syndrome (NSTE-ACS), there was no statistically significant difference between the GRACE risk scale and AGEF (AUC: 0.853 vs 0.832, P=0.140) for in-hospital death. Conclusions AGEF risk score showed a non-inferior utility compared with the other 3 scoring systems in estimating in-hospital mortality in ACS patients.
Background Renal insufficiency is an important risk factor for mortality in various populations. The present study was conducted to determine the optimal equation for the estimation of renal function in predicting adverse events in community population in US. Methods We examined the Cockcroft–Gault, modification of diet in renal disease (MDRD), Mayo Healthy-Chronic Kidney Disease (Mayo), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) derived estimated glomerular filtration rates (eGFR) and the association with cardiovascular or non-cardiovascular mortality among 25,677 participants of US National Health and Nutrition Examination Survey from 2005 to 2014. Results The cardiovascular mortality and non-cardiovascular mortality increased with decrease in renal function. The MDRD derived eGFR exhibited the lowest predictive ability for all-cause mortality in all participants. For cardiovascular mortality, the Cockcroft–Gault derived eGFR exhibited the highest predictive power compared with the MDRD (area under the curve [AUC]: 0.842 vs. 0.764, p < 0.001), Mayo (AUC: 0.842 vs. 0.812, p < 0.001) and CKD-EPI (AUC: 0.842 vs. 0.813, p < 0.001) derived eGFR. For non-cardiovascular mortality, the Cockcroft–Gault derived eGFR exhibited similar superiority in non-cardiovascular mortality. Conclusions The value of the Cockcroft–Gault equation was superior to the other three equations for the prediction of cardiovascular or non-cardiovascular mortality in community population. This equation can serve as a risk-stratification tool for long-term events in community population.
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