Bivalirudin vs heparin in cardiac-cerebral ischemic and bleeding events among Chinese STEMI patients during percutaneous coronary intervention: a retrospective cohort study

Although bivalirudin has been recently made available for purchase in China, large-scale analyses on the safety profile of bivalirudin among Chinese patients is lacking. Thus, this study aimed to compare the safety profile of bivalirudin and heparin as anticoagulants in Chinese ST-segment elevation myocardial infarction (STEMI) patients undergoing percutaneous coronary intervention (PCI). A total of 1063 STEMI patients undergoing PCI and receiving bivalirudin (n=424, bivalirudin group) or heparin (n=639, heparin group) as anticoagulants were retrospectively enrolled. The net adverse clinical events (NACEs) within 30 days after PCI were recorded, including major adverse cardiac and cerebral events (MACCEs) and bleeding events (bleeding academic research consortium (BARC) grades 2-5 (BARC 2-5)). The incidences of NACEs (10.1 vs 15.6%) (P=0.010), BARC 2-5 bleeding events (5.2 vs 10.3%) (P=0.003), and BARC grades 3-5 (BARC 3-5) bleeding events (2.1 vs 5.5%) (P=0.007) were lower in the bivalirudin group compared to the heparin group, whereas general MACCEs incidence (8.9 vs 6.4%) (P=0.131) and each category of MACCEs (all P>0.05) did not differ between two groups. Furthermore, the multivariate logistic analyses showed that bivalirudin (vs heparin) was independently correlated with lower risk of NACEs (OR=0.508, P=0.002), BARC 2-5 bleeding events (OR=0.403, P=0.001), and BARC 3-5 bleeding events (OR=0.452, P=0.042); other independent risk factors for NACEs, MACCEs, or BARC bleeding events included history of diabetes mellitus, emergency operation, multiple lesional vessels, stent length >33.0 mm, and higher CRUSADE score (all P<0.05). Thus, bivalirudin presented a better safety profile than heparin among Chinese STEMI patients undergoing PCI.

Applications of serum amino acid levels in identification of cancer

Chemometric method of uncorrelated linear discriminant analysis (ULDA) was applied to the data of amino acid levels in serum of lung cancer patients and healthy people, eventually successfully classifies the samples of cancer patients and healthy people. Simultaneously, several potential amino acid biomarkers were possibly chosen. So the method of amino acid levels combined with ULDA algorithm could be applied to the identification of cancer and exploration of tumor biomarker, which has certain practical value and application prospects.