Inflammatory markers of contrast-induced nephropathy in patients with acute coronary syndrome.
Yildirim Erkan,Ermis Emrah,Cengiz Mahir
Coronary artery disease
OBJECTIVE:Contrast-induced nephropathy (CIN) is among the serious complications of invasive cardiovascular procedures that are performed with the administration of contrast agents. We investigated the role of the inflammatory markers in predicting CIN in acute coronary syndrome patients. METHODS:This study included 232 consecutive patients with acute coronary syndrome who underwent emergency angiography at our center. RESULTS:There were 38 (19.1%) patients in the CIN group (mean age: 62.4 ± 10.2; 68.4% male), and 162 patients in the non-CIN group (mean age: 62.1 ± 11.5; 60.5% male). In the CIN positive group, serum gamma-glutamyl transferase (GGT) (P < 0.001), uric acid (P < 0.001), high sensitivity C-reactive protein (P < 0.001), the neutrophil-to-lymphocyte ratio (P = 0.02) were higher, whereas vitamin D (P < 0.001), hemoglobin (P < 0.001) and baseline glomerular filtration rate (P = 0.011) were lower compared with the CIN negative group. The receiver operating characteristic analysis showed that the cutoff point of GGT was 56 U/L for predicting CIN with a 84.2% sensitivity and a 72.2% specificity (area under the curve = 0.879, P < 0.001). The predictive value of GGT was the highest compared other inflammatory markers for CIN (area under the curve = 0.879). CONCLUSION:Our study showed that the levels of GGT, high sensitivity C-reactive protein, vitamin D, uric acid and neutrophil-to-lymphocyte ratio were the effective factors in development of CIN. The level of GGT was found as the most effective factor in prediction of the development of CIN.
A novel risk assessment model of contrast-induced nephropathy after percutaneous coronary intervention in patients with diabetes.
Yao Zhi-Feng,Shen Hong,Tang Min-Na,Yan Yan,Ge Jun-Bo
Basic & clinical pharmacology & toxicology
The purpose of our study was to develop a simple clinical pre-procedure risk model based on clinical characteristics for the prediction of contrast-induced nephropathy (CIN) and major adverse cardiac events (MACEs) after percutaneous coronary intervention (PCI) in patients with diabetes. A total of 1113 patients with diabetes who underwent PCI with contrast exposure were randomized into a development group (n = 742) and a validation group (n = 371) in a 2:1 ratio. CIN was defined as an increase of either 25% or 0.5 mg/dL (44.2 μmol/L) in serum creatinine within 72 hours after contrast infusion. A simple CIN risk score based on independent predictors was established. Four variables were identified for our risk score model: LVEF < 40%, acute coronary syndrome (ACS), eGFR < 60, and contrast volume > 300 mL. Based on this new CIN risk score, the incidence of CIN had a significant trend with increased predicting score values of 5.9%, 32.9% and 60.0%, corresponding to low-, moderate- and high-risk groups, respectively. The novel risk assessment exhibited moderate discrimination ability for predicting CIN, with an AUC of 0.759 [95% CI 0.668-0.852, P = .001] in the validation cohort. It also had similar prognostic values for one-year follow-up MACE (C-statistic: 0.705 and 0.606 for new risk score and Mehran score, respectively). This novel risk prediction model could be effective for preventing nephropathy in diabetic patients receiving contrast media during surgical procedures.
Random forest for prediction of contrast-induced nephropathy following coronary angiography.
Liu Yong,Chen Shiqun,Ye Jianfeng,Xian Ying,Wang Xia,Xuan Jianwei,Tan Ning,Li Qiang,Chen Jiyan,Ni Zhonghan
The international journal of cardiovascular imaging
The majority of prediction models for contrast-induced nephropathy (CIN) have moderate performance. Therefore, we aimed to develop a better pre-procedural prediction tool for CIN following contemporary percutaneous coronary intervention (PCI) or coronary angiography (CAG). A total of 3469 patients undergoing PCI/CAG between January 2010 and December 2013 were randomly divided into a training (n = 2428, 70%) and validation data-sets (n = 1041, 30%). Random forest full models were developed using 40 pre-procedural variables, of which 13 variables were selected for a reduced CIN model. CIN developed in 78 (3.21%) and 37 of patients (3.54%) in the training and validation datasets, respectively. In the validation dataset, the full and reduced models demonstrated improved discrimination over classic Mehran, ACEF CIN risk scores (AUC 0.842 and 0.825 over 0.762 and 0.701, respectively, all P < 0.05) and common estimated glomerular filtration rate. Compared to that for the Mehran risk score model, the full and reduced models had significantly improved fit based on the net reclassification improvement (all P < 0.001) and integrated discrimination improvement (P = 0.001, 0.028, respectively). Using the above models, 2462 (66.7%), 661, and 346 patients were categorized into low (< 1%), moderate (1% to 7%), and high (> 7%) risk groups, respectively. Our pre-procedural CIN risk prediction algorithm (http://cincalc.com) demonstrated good discriminative ability and was well calibrated when validated. Two-thirds of the patients were at low CIN risk, probably needing less peri-procedural preventive strategy; however, the discriminative ability of CIN risk requires further external validation. TRIAL REGISTRATION: ClinicalTrials.gov NCT01400295.