BACKGROUND: Red blood cell distribution width (RDW) is highly associated with various clinical states. In the present study, we aimed to determine the natures of associations between RDW changes and early adverse events after isolated coronary artery bypass grafting (CABG). METHODS: We retrospectively analyzed medical records of enrolled 117 patients. Patients were classified into two groups depending on early adverse events (No-event vs. Event). Delta RDW values were calculated (ΔRDW: Post-Peak RDW minus Pre-RDW). Patients were divided into tertiles based on ΔRDW. The ΔRDW cut-off point for an adverse event was determined by receiver operating characteristic curve analysis. In addition, logistic regression analysis was performed to identify independent factors of early adverse events. RESULTS: Thirty eight patients experienced 53 early adverse events. ΔRDW and ΔC-reactive protein were significantly higher in the Events group than in the No-event group. Incidences of early adverse events increased significantly between ΔRDW tertiles (P<0.001). The ROC curve of ΔRDW showed that a ΔRDW of ≥1.45 had a sensitivity of 71.1% and a specificity of 78.2% for predicting an early adverse event after CABG (P<0.001). Multivariable analysis showed ΔRDW (P=0.042) and length of ICU stay (P<0.001) independently predicted an adverse event. CONCLUSIONS: ΔRDW was identified to be an independent predictor of early adverse events, and a ΔRDW cut-off of 1.45 was found to predict early adverse events after CABG. Careful monitoring of RDW trends after isolated CABG provides a simple, inexpensive and objective means of predicting early adverse events.
BACKGROUND: Red blood cell distribution width (RDW) is highly associated with various clinical states. In the present study, we aimed to determine the natures of associations between RDW changes and early adverse events after isolated coronary artery bypass grafting (CABG). METHODS: We retrospectively analyzed medical records of enrolled 117 patients. Patients were classified into two groups depending on early adverse events (No-event vs. Event). Delta RDW values were calculated (ΔRDW: Post-Peak RDW minus Pre-RDW). Patients were divided into tertiles based on ΔRDW. The ΔRDW cut-off point for an adverse event was determined by receiver operating characteristic curve analysis. In addition, logistic regression analysis was performed to identify independent factors of early adverse events. RESULTS: Thirty eight patients experienced 53 early adverse events. ΔRDW and ΔC-reactive protein were significantly higher in the Events group than in the No-event group. Incidences of early adverse events increased significantly between ΔRDW tertiles (P<0.001). The ROC curve of ΔRDW showed that a ΔRDW of ≥1.45 had a sensitivity of 71.1% and a specificity of 78.2% for predicting an early adverse event after CABG (P<0.001). Multivariable analysis showed ΔRDW (P=0.042) and length of ICU stay (P<0.001) independently predicted an adverse event. CONCLUSIONS: ΔRDW was identified to be an independent predictor of early adverse events, and a ΔRDW cut-off of 1.45 was found to predict early adverse events after CABG. Careful monitoring of RDW trends after isolated CABG provides a simple, inexpensive and objective means of predicting early adverse events.
Entities:
Keywords:
Red blood cell distribution width (RDW); complication; coronary artery bypass grafting (CABG)
Authors: Jason M Lappé; Benjamin D Horne; Svati H Shah; Heidi T May; Joseph B Muhlestein; Donald L Lappé; Abdallah G Kfoury; John F Carlquist; Deborah Budge; Rami Alharethi; Tami L Bair; William E Kraus; Jeffrey L Anderson Journal: Clin Chim Acta Date: 2011-07-27 Impact factor: 3.786
Authors: Nay Aung; Hua Zen Ling; Adrian S Cheng; Suneil Aggarwal; Julia Flint; Michelle Mendonca; Mohammed Rashid; Swan Kang; Susanne Weissert; Caroline J Coats; Toby Richards; Martin Thomas; Simon Woldman; Darlington O Okonko Journal: Int J Cardiol Date: 2013-01-22 Impact factor: 4.164
Authors: Piotr Duchnowski; Piotr Szymański; Ewa Orłowska-Baranowska; Mariusz Kuśmierczyk; Tomasz Hryniewiecki Journal: Kardiol Pol Date: 2015-10-27 Impact factor: 3.108
Authors: Halldor B Olafsson; Gissur A Sigurdarson; Kenneth B Christopher; Sigurbergur Karason; Gisli H Sigurdsson; Martin I Sigurdsson Journal: Br J Anaesth Date: 2020-03-23 Impact factor: 9.166
Authors: Gian Franco Veraldi; Luca Mezzetto; Lorenzo Scorsone; Marco Macrì; Fabio Simoncini; Giuseppe Lippi Journal: J Med Biochem Date: 2019-07-30 Impact factor: 3.402