Preprocedural Prediction Model for Contrast‐Induced Nephropathy Patients

Autor: Yi-hu Yi, Ling-yun Zhou, Jiang-lin Wang, Xiao-Feng Guan, Xiao-Cong Zuo, Wen-Jun Yin, Dai-Yang Li
Rok vydání: 2017
Předmět:
Male
Epidemiology
medicine.medical_treatment
030232 urology & nephrology
Contrast Media
Coronary Artery Disease
030204 cardiovascular system & hematology
Coronary Angiography
risk prediction
chemistry.chemical_compound
contrast‐induced nephropathy
0302 clinical medicine
Risk Factors
Clinical Studies
Myocardial Revascularization
Prospective Studies
Original Research
Incidence
Incidence (epidemiology)
Contrast (statistics)
Acute Kidney Injury
Middle Aged
Matthews correlation coefficient
risk factor
Injections
Intravenous
Preoperative Period
Female
Radiology
Cardiology and Cardiovascular Medicine
Glomerular Filtration Rate
China
medicine.medical_specialty
Contrast-induced nephropathy
Risk Assessment
03 medical and health sciences
medicine
Humans
Risk factor
Creatinine
Kidney in Cardiovascular Disease
Computerized Tomography (CT)
business.industry
percutaneous coronary intervention
Percutaneous coronary intervention
medicine.disease
Surgery
Data set
ROC Curve
chemistry
business
Forecasting
Zdroj: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
ISSN: 2047-9980
DOI: 10.1161/jaha.116.004498
Popis: Background 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. Methods and Results 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. Conclusions The newly established model shows excellent predictive ability of CIN development and thereby provides preventative measures for CIN .
Databáze: OpenAIRE
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