International journal of cardiology · 2016
A novel risk score model for prediction of contrast-induced nephropathy after emergent percutaneous coronary intervention.
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Lin Kai-Yang, Zheng Wei-Ping, Bei Wei-Jie, Chen Shi-Qun, Islam Sheikh Mohammed Shariful, Liu Yong, Xue Lin, Tan Ning, Chen Ji-Yan
Аннотация
BACKGROUND: A few studies developed simple risk model for predicting CIN with poor prognosis after emergent PCI. The study aimed to develop and validate a novel tool for predicting the risk of contrast-induced nephropathy (CIN) in patients undergoing emergent percutaneous coronary intervention (PCI).
METHODS: 692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n=461) and a validation dataset (n=231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration.
RESULTS: The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age >75years old, baseline serum creatinine (SCr)>1.5mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score=0), 1.0%, moderate risk (score:1-2), 13.4%, high risk (score≥3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population.
CONCLUSIONS: Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI.
METHODS: 692 consecutive patients undergoing emergent PCI between January 2010 and December 2013 were randomly (2:1) assigned to a development dataset (n=461) and a validation dataset (n=231). Multivariate logistic regression was applied to identify independent predictors of CIN, and established CIN predicting model, whose prognostic accuracy was assessed using the c-statistic for discrimination and the Hosmere Lemeshow test for calibration.
RESULTS: The overall incidence of CIN was 55(7.9%). A total of 11 variables were analyzed, including age >75years old, baseline serum creatinine (SCr)>1.5mg/dl, hypotension and the use of intra-aortic balloon pump(IABP), which were identified to enter risk score model (Chen). The incidence of CIN was 32(6.9%) in the development dataset (in low risk (score=0), 1.0%, moderate risk (score:1-2), 13.4%, high risk (score≥3), 90.0%). Compared to the classical Mehran's and ACEF CIN risk score models, the risk score (Chen) across the subgroup of the study population exhibited similar discrimination and predictive ability on CIN (c-statistic:0.828, 0.776, 0.853, respectively), in-hospital mortality, 2, 3-years mortality (c-statistic:0.738.0.750, 0.845, respectively) in the validation population.
CONCLUSIONS: Our data showed that this simple risk model exhibited good discrimination and predictive ability on CIN, similar to Mehran's and ACEF score, and even on long-term mortality after emergent PCI.