日本語フィールド
著者：○Yuhei Goriki, Goro Yoshioka, Masahiro Natsuaki, Kodai Shinzato, Kensaku Nishihira, Nehiro Kuriyama, Mitsuhiro Shimomura, Yohei Inoue, Toshiyuki Nishikido, Tetsuya Kaneko, Kensuke Yokoi, Ayumu Yajima, Yoshiko Sakamoto, Motoko Tago, Atsushi Kawaguchi, Fumi Yamamoto, Atsushi Tanaka, Takanori Yamaguchi, Aya Shiraki, Machiko Asaka, Norihiko Kotooka, Shinjo Sonoda, Yutaka Hikichi, Yoshisato Shibata, Koichi Node題名：Simple risk-score model for in-hospital major bleeding based on multiple blood variables in patients with acute myocardial infarction 発表情報：Int J Cardiol 巻: 346 ページ: 1-7キーワード：Acute myocardial infarction; Biomarker; Bleeding概要：Background: In-hospital bleeding is associated with poor prognosis in patients with acute myocardial infarction (AMI). We sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of in-hospital major bleeding in patients with AMI.
Methods and results: A total of 1684 consecutive AMI patients who underwent primary percutaneous coronary intervention (PCI) were recruited and randomly divided into derivation (n = 1010) and validation (n = 674) cohorts. A risk-score model was created based on a combination of parameters assessed on routine blood tests on admission. In the derivation cohort, multivariate analysis revealed that the following 5 variables were significantly associated with in-hospital major bleeding: hemoglobin level < 12 g/dL (odds ratio [OR], 3.32), white blood cell count >10,000/μL (OR, 2.58), platelet count <150,000/μL (OR, 2.51), albumin level < 3.8 mg/dL (OR, 2.51), and estimated glomerular filtration rate < 60 mL/min/1.73 m2 (OR, 2.31). Zero to five points were given according to the number of these factors each patient had. Incremental risk scores were significantly associated with a higher incidence of in-hospital major bleeding in both cohorts (P < 0.001). Receiver operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without in-hospital major bleeding (derivation cohort: area under the curve [AUC], 0.807; 95% confidence interval [CI], 0.759-0.848; validation cohort: AUC, 0.793; 95% CI, 0.725-0.847).
Conclusions: Our novel laboratory-based bleeding risk model could be useful for simple and objective prediction of in-hospital major bleeding events in patients with AMI.抄録：英語フィールド
Author：○Yuhei Goriki, Goro Yoshioka, Masahiro Natsuaki, Kodai Shinzato, Kensaku Nishihira, Nehiro Kuriyama, Mitsuhiro Shimomura, Yohei Inoue, Toshiyuki Nishikido, Tetsuya Kaneko, Kensuke Yokoi, Ayumu Yajima, Yoshiko Sakamoto, Motoko Tago, Atsushi Kawaguchi, Fumi Yamamoto, Atsushi Tanaka, Takanori Yamaguchi, Aya Shiraki, Machiko Asaka, Norihiko Kotooka, Shinjo Sonoda, Yutaka Hikichi, Yoshisato Shibata, Koichi NodeTitle：Simple risk-score model for in-hospital major bleeding based on multiple blood variables in patients with acute myocardial infarction Announcement information:Int J Cardiol Vol: 346 Page: 1-7Keyword：Acute myocardial infarction; Biomarker; BleedingAn abstract：Background: In-hospital bleeding is associated with poor prognosis in patients with acute myocardial infarction (AMI). We sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of in-hospital major bleeding in patients with AMI.
Methods and results: A total of 1684 consecutive AMI patients who underwent primary percutaneous coronary intervention (PCI) were recruited and randomly divided into derivation (n = 1010) and validation (n = 674) cohorts. A risk-score model was created based on a combination of parameters assessed on routine blood tests on admission. In the derivation cohort, multivariate analysis revealed that the following 5 variables were significantly associated with in-hospital major bleeding: hemoglobin level < 12 g/dL (odds ratio [OR], 3.32), white blood cell count >10,000/μL (OR, 2.58), platelet count <150,000/μL (OR, 2.51), albumin level < 3.8 mg/dL (OR, 2.51), and estimated glomerular filtration rate < 60 mL/min/1.73 m2 (OR, 2.31). Zero to five points were given according to the number of these factors each patient had. Incremental risk scores were significantly associated with a higher incidence of in-hospital major bleeding in both cohorts (P < 0.001). Receiver operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without in-hospital major bleeding (derivation cohort: area under the curve [AUC], 0.807; 95% confidence interval [CI], 0.759-0.848; validation cohort: AUC, 0.793; 95% CI, 0.725-0.847).
Conclusions: Our novel laboratory-based bleeding risk model could be useful for simple and objective prediction of in-hospital major bleeding events in patients with AMI.