Journal of thoracic disease · 2019
Simple pre-procedure risk stratification tool for contrast-induced nephropathy.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Ni Zhonghan, Liang Yan, Xie Nianjin, Liu Jin, Sun Guoli, Chen Shiqun, Ye Jianfeng, He Yibo, Guo Wei, Tan Ning, Chen Jiyan, Liu Yong, Chen Zhujun, Wang Shouhong
Аннотация
BACKGROUND: A few simple and pre-procedural risk models have been developed for predicting contrast-induced nephropathy (CIN), which allow for early administration of preventative strategies before coronary angiography (CAG). The study aims to develop and validate simple pre-procedure tools for predicting risk of CIN following CAG.
METHODS: We retrospectively analyzed the data from 3,469 consecutive patients undergoing CAG, who were randomly assigned to a development dataset (n=2,313) and a validation dataset (n=1,156). CIN was defined as an increase in serum creatinine (SCr) ≥0.5 mg/dL from baseline within 72 hours after CAG. Multivariate logistic regression was applied to identify independent predictors of CIN to develop risk models. The possible predictors included age >75 years, hypotension, acute myocardial infarction (AMI), SCr ≥1.5 mg/dL, and congestive heart failure (CHF).
RESULTS: The incidences of CIN were 3.20% and 3.55% in the training and validation dataset respectively. Compared to classical Mehran' and ACEF CIN risk score, the new score across the validation dataset exhibited similar discrimination and predictive ability on CIN (c-statistic: 0.829, 0.832, 0.812 respectively) and in-hospital mortality (c-statistic: 0.909, 0.937, 0.866 respectively) (all P>0.05).
CONCLUSIONS: The easy-to-use pre-procedural prediction model only containing 5 factors had similar predictive ability on CIN and mortality.
METHODS: We retrospectively analyzed the data from 3,469 consecutive patients undergoing CAG, who were randomly assigned to a development dataset (n=2,313) and a validation dataset (n=1,156). CIN was defined as an increase in serum creatinine (SCr) ≥0.5 mg/dL from baseline within 72 hours after CAG. Multivariate logistic regression was applied to identify independent predictors of CIN to develop risk models. The possible predictors included age >75 years, hypotension, acute myocardial infarction (AMI), SCr ≥1.5 mg/dL, and congestive heart failure (CHF).
RESULTS: The incidences of CIN were 3.20% and 3.55% in the training and validation dataset respectively. Compared to classical Mehran' and ACEF CIN risk score, the new score across the validation dataset exhibited similar discrimination and predictive ability on CIN (c-statistic: 0.829, 0.832, 0.812 respectively) and in-hospital mortality (c-statistic: 0.909, 0.937, 0.866 respectively) (all P>0.05).
CONCLUSIONS: The easy-to-use pre-procedural prediction model only containing 5 factors had similar predictive ability on CIN and mortality.