Preprocedural Prediction Model for Contrast-Induced Nephropathy Patients.
Several models have been developed for prediction of contrast-induced nephropathy (CIN); however, they only contain patients receiving intra-arterial contrast media for coronary angiographic procedures, which represent a small proportion of all contrast procedures. In addition, most of them evaluate radiological interventional procedure-related variables. So it is necessary for us to develop a model for prediction of CIN before radiological procedures among patients administered contrast media.



A total of 8800 patients undergoing contrast administration were randomly assigned in a 4:1 ratio to development and validation data sets. CIN was defined as an increase of 25% and/or 0.5 mg/dL in serum creatinine within 72 hours above the baseline value. Preprocedural clinical variables were used to develop the prediction model from the training data set by the machine learning method of random forest, and 5-fold cross-validation was used to evaluate the prediction accuracies of the model. Finally we tested this model in the validation data set. The incidence of CIN was 13.38%. We built a prediction model with 13 preprocedural variables selected from 83 variables. The model obtained an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.907 and gave prediction accuracy of 80.8%, sensitivity of 82.7%, specificity of 78.8%, and Matthews correlation coefficient of 61.5%. For the first time, 3 new factors are included in the model: the decreased sodium concentration, the INR value, and the preprocedural glucose level.



The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN.
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Wen-Jun
WJ
Yin
Xiangya School of Medical Science of Central South University, Changsha, Hunan, China.
Yi-Hu
YH
Yi
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Xiao-Feng
XF
Guan
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Ling-Yun
LY
Zhou
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Jiang-Lin
JL
Wang
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China.
Dai-Yang
DY
Li
Clinical Pharmacy and Pharmacology Research Institute, The Third Xiangya Hospital of Central South University, Changsha, Hunan, China zuoxc08@126.com.
Xiao-Cong
XC
Zuo
D058186:Acute Kidney Injury / Q000139:chemically induced* / Q000175:diagnosis / Q000453:epidemiology; D002681:China / Q000453:epidemiology; D003287:Contrast Media / Q000008:administration & dosage / Q000009:adverse effects*; D017023:Coronary Angiography / Q000009:adverse effects* / Q000379:methods; D003324:Coronary Artery Disease / Q000175:diagnosis*; D005260:Female; D005544:Forecasting; D005919:Glomerular Filtration Rate / Q000187:drug effects; D006801:Humans; D015994:Incidence; D007275:Injections, Intravenous; D008297:Male; D008875:Middle Aged; D009204:Myocardial Revascularization / Q000379:methods; D057234:Preoperative Period; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
D003287:Contrast Media
contrast media; contrast‐induced nephropathy; percutaneous coronary intervention; risk factor; risk prediction