Mehmet Dedemoğlu1, Mehmet Erin Tüysüz2. 1. Department of Pediatric Cardiovascular Surgery, Mersin City Training and Research Hospital, Mersin, Turkey. mehmetdedemoglu@gmail.com. 2. Department of Cardiovascular Surgery, Mersin City Training and Research Hospital, Mersin, Turkey.
Abstract
OBJECTIVES: Risk prediction for postoperative acute kidney injury (AKI) has a great clinical value to achieve early prevention strategies for AKI after cardiac surgery. We aimed to identify the patients at risk of postoperative AKI and to create patient risk group for AKI using a simple risk estimation model in patients undergoing heart valve replacement surgery. METHODS: Between May 2008 and February 2018, 219 consecutive patients undergoing heart valve replacement surgery with or without concomitant coronary artery bypass grafting (CAGB) were included in the study. To define postoperative AKI and its severity stages, KDIGO classification which is the latest uniform classification for determining and staging of AKI was used. RESULTS: The AKI incidence was 38.8%, and Class I was the dominant stage (43.5%). Postoperative AKI development was associated with a serious of postoperative adverse events, early, and long-term mortality. Furthermore, the incidence of poor outcomes increased with the degree of AKI severity. The presence of older age, chronic obstructive pulmonary disease, NYHA class III-IV, diabetes, concomitant CABG, and longer cardiopulmonary bypass duration was found to be an independent predictor for AKI, and each factor was scored according to the integer value of their odds ratio, based on risk estimation model. Patient risk groups from mild to severe for AKI development were created. The patients at severe risk group exhibited a significantly higher rate of adverse events, early, and long-term mortality as well as lower long-term survival rates. CONCLUSIONS: The risk estimation model is a useful tool to identify the patients at risk and to create patient risk groups for postoperative AKI defined by KDIGO after heart valve replacement surgery.
OBJECTIVES: Risk prediction for postoperative acute kidney injury (AKI) has a great clinical value to achieve early prevention strategies for AKI after cardiac surgery. We aimed to identify the patients at risk of postoperative AKI and to create patient risk group for AKI using a simple risk estimation model in patients undergoing heart valve replacement surgery. METHODS: Between May 2008 and February 2018, 219 consecutive patients undergoing heart valve replacement surgery with or without concomitant coronary artery bypass grafting (CAGB) were included in the study. To define postoperative AKI and its severity stages, KDIGO classification which is the latest uniform classification for determining and staging of AKI was used. RESULTS: The AKI incidence was 38.8%, and Class I was the dominant stage (43.5%). Postoperative AKI development was associated with a serious of postoperative adverse events, early, and long-term mortality. Furthermore, the incidence of poor outcomes increased with the degree of AKI severity. The presence of older age, chronic obstructive pulmonary disease, NYHA class III-IV, diabetes, concomitant CABG, and longer cardiopulmonary bypass duration was found to be an independent predictor for AKI, and each factor was scored according to the integer value of their odds ratio, based on risk estimation model. Patient risk groups from mild to severe for AKI development were created. The patients at severe risk group exhibited a significantly higher rate of adverse events, early, and long-term mortality as well as lower long-term survival rates. CONCLUSIONS: The risk estimation model is a useful tool to identify the patients at risk and to create patient risk groups for postoperative AKI defined by KDIGO after heart valve replacement surgery.