Chongyang Duan1, Yingshu Cao1, Yong Liu2, Lizhi Zhou1, Kaike Ping1, Ming T Tan3, Ning Tan2, Jiyan Chen4, Pingyan Chen5. 1. State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, and Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China. 2. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. 3. State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, and Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China; Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, Washington DC, USA. 4. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China. Electronic address: chenjiyandr@126.com. 5. State Key Laboratory of Organ Failure Research, National Clinical Research Center for Kidney Disease, and Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, China. Electronic address: chenpy99@126.com.
Abstract
BACKGROUND: Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for only postcontrast exposure prediction; however, prediction before the procedure is more valuable in practice. This study aimed to develop a risk scoring system based on preprocedural characteristics for early prediction of CI-AKI in patients after coronary angiography or percutaneous coronary intervention (PCI). METHODS: We prospectively recruited 1777 consecutive patients who were randomized in an approximate 3:2 ratio to create a development data set (n = 1076) and a validation data set (n = 701). A risk score model based on preprocedural risk factors was developed using stepwise logistic regression. Validation was performed by bootstrap and split-sample methods. RESULTS: The occurrence of CI-AKI was 5.97% (106 of 1777), 5.95% (64 of 1076), and 5.99% (42 of 701) in the overall, developmental, and validation data sets, respectively. The risk score was developed with 5 prognostic factors (age, serum creatinine levels, N-terminal pro b-type natriuretic peptide levels, high-sensitivity C-reactive protein, and primary PCI), ranged from 0-36, and was well calibrated (Hosmer-Lemeshow χ2 = 4.162; P = 0.842). Good discrimination was obtained both in the developmental and validation data sets (C-statistic, 0.809 and 0.798, respectively). The risk score was highly and positively associated with CI-AKI (P for trend < 0.001) in-hospital and long-term outcomes. CONCLUSIONS: The novel risk score model we developed is a simple and accurate tool for early/preprocedural prediction of CI-AKI in patients undergoing coronary angiography or PCI. This tool allows assessment of the risk of CI-AKI before contrast exposure, allowing for timely initiation of appropriate preventive measures.
RCT Entities:
BACKGROUND: Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for only postcontrast exposure prediction; however, prediction before the procedure is more valuable in practice. This study aimed to develop a risk scoring system based on preprocedural characteristics for early prediction of CI-AKI in patients after coronary angiography or percutaneous coronary intervention (PCI). METHODS: We prospectively recruited 1777 consecutive patients who were randomized in an approximate 3:2 ratio to create a development data set (n = 1076) and a validation data set (n = 701). A risk score model based on preprocedural risk factors was developed using stepwise logistic regression. Validation was performed by bootstrap and split-sample methods. RESULTS: The occurrence of CI-AKI was 5.97% (106 of 1777), 5.95% (64 of 1076), and 5.99% (42 of 701) in the overall, developmental, and validation data sets, respectively. The risk score was developed with 5 prognostic factors (age, serum creatinine levels, N-terminal pro b-type natriuretic peptide levels, high-sensitivity C-reactive protein, and primary PCI), ranged from 0-36, and was well calibrated (Hosmer-Lemeshow χ2 = 4.162; P = 0.842). Good discrimination was obtained both in the developmental and validation data sets (C-statistic, 0.809 and 0.798, respectively). The risk score was highly and positively associated with CI-AKI (P for trend < 0.001) in-hospital and long-term outcomes. CONCLUSIONS: The novel risk score model we developed is a simple and accurate tool for early/preprocedural prediction of CI-AKI in patients undergoing coronary angiography or PCI. This tool allows assessment of the risk of CI-AKI before contrast exposure, allowing for timely initiation of appropriate preventive measures.
Authors: Ali O Malik; Amit Amin; Kevin Kennedy; Mohammed Qintar; Ali Shafiq; Roxana Mehran; John A Spertus Journal: Am Heart J Date: 2021-01-25 Impact factor: 4.749