Application of disease prediction model in chronic kidney disease complicated with contrast induced nephropathy - ZHOU Min; ZHOU Ying; XU Xiao-jin; ZHU Ying; CHEN Min; WANG Guo-yi; ZHANG Qing; ZHANG Xiao-bo (Linchuang shenzangbing zazhi)
Objective To develop a disease prediction model of chronic kidney disease(CKD) with contrast induced Nephropathy(CIN), and to test its clinical value. Methods Patients with CKD complicated with cardiovascular disease(CVD) receiving contrast-based examination who were hospitalized in the department of nephropathy, the affiliated Huai’an No.1 people’s hospital of Nanjing medical university from February 2012 to May 2018, were selected as study subjects. A retrospective study was conducted to record the clinical data of all patients before and after the use of contrast medium, including gender, age, etiology of CKD, left ventricular ejection fraction(LVEF), fasting blood glucose, blood pressure, 24-hour urine volume, hemoglobin(HGB), Serum creatinine(Scr), estimated glomerular filtration rate(eGFR) and contrast dose. The end point of this study was the occurrence of CIN. The variables that were tightly related to CIN were screened and scored, the total score of each patient was summarized in order to evaluate the risk of CIN, then the disease prediction model was developed based on the total score and risk for CIN in the patients was assessed by using the model. Results A total of 311 patients were included in the study, according to the established CIN standard, 80 patients were found to have developed contrast induced nephropathy on the basis of original CKD. And the patients at the age of over 65, with high blood glucose, low LVEF, and high Scr had higher risk to develop CIN. After the four variables of scoring age, Scr, LVEF and blood glucose, and the total scores were divided into three grades to develop the disease prediction model. It was found that with higher the total score, CKD patients had higher risk for CIN. Conclusions Using four variables to build the corresponding risk prediction model has a significant predictive effect on the occurrence of CKD complicated with CIN, and can guide early prevention and intervention in clinical treatment.