Background and AimsSystemic immune-inflammation index (SII) is an emerging indicator and correlated to the incidence of cardiovascular diseases. This study aimed to explore the association between SII and contrast-induced acute kidney injury (CI-AKI).MethodsIn this retrospective cross-sectional study, 4,381 subjects undergoing coronary angiography (CAG) were included. SII is defined as neutrophil count × platelet count/lymphocyte count. CI-AKI was determined by the elevation of serum creatinine (Scr). Multivariable linear and logistic regression analysis were used to determine the relationship of SII with Scr and CI-AKI, respectively. Receiver operator characteristic (ROC) analysis, structural equation model analysis, and subgroup analysis were also performed.ResultsOverall, 786 (17.9%) patients suffered CI-AKI after the intravascular contrast administration. The subjects were 67.1 ± 10.8 years wold, with a mean SII of 5.72 × 1011/L. Multivariable linear regression analysis showed that SII linearly increased with the proportion of Scr elevation (β [95% confidence interval, CI] = 0.315 [0.206 to 0.424], P < 0.001). Multivariable logistic regression analysis demonstrated that higher SII was associated with an increased incidence of CI-AKI ([≥12 vs. <3 × 1011/L]: odds ratio, OR [95% CI] = 2.914 [2.121 to 4.003], P < 0.001). Subgroup analysis showed consistent results. ROC analysis identified a good predictive value of SII on CI-AKI (area under the ROC curve [95% CI]: 0.625 [0.602 to 0.647]). The structural equation model verified a more remarkable direct effect of SII (β = 0.102, P < 0.001) on CI-AKI compared to C-reactive protein (β = 0.070, P < 0.001).ConclusionsSII is an independent predictor for CI-AKI in patients undergoing CAG procedures.

Background Nutritional risk is prevalent in various diseases, but its association with contrast-induced acute kidney injury (CI-AKI) remains unclear. This study aimed to explore this association in patients undergoing coronary angiography (CAG). Methods In this retrospective cross-sectional study, 4386 patients undergoing CAG were enrolled. Nutritional risks were estimated by nutritional risk screening 2002 (NRS-2002), controlling nutritional status (CONUT), prognostic nutritional index (PNI), and geriatric nutritional risk index (GNRI), respectively. CI-AKI was determined by the elevation of serum creatinine (Scr). Multivariable logistic regression analyses and receiver operator characteristic (ROC) analyses were conducted. Subgroup analyses were performed according to age (< 70/≥70 years), gender (male/female), percutaneous coronary intervention (with/without), and estimated glomerular filtration rate (< 60/≥60 ml/min/1.73m2). Results Overall, 787 (17.9%) patients were diagnosed with CI-AKI. The median score of NRS-2002, CONUT, PNI, and GNRI was 1.0, 3.0, 45.8, and 98.6, respectively. Nutritional risk was proven to be associated with CI-AKI when four different nutritional tools were employed, including NRS-2002 ([3–7 vs. 0]: odds ratio [95% confidence interval], OR [95%CI] = 4.026 [2.732 to 5.932], P < 0.001), CONUT ([6–12 vs. 0–1]: OR [95%CI] = 2.230 [1.586 to 3.136], P < 0.001), PNI ([< 38 vs. ≥52]: OR [95%CI] = 2.349 [1.529 to 3.610], P < 0.001), and GNRI ([< 90 vs. ≥104]: OR [95%CI] = 1.822 [1.229 to 2.702], P = 0.003). This is consistent when subgroup analyses were performed. Furthermore, nutritional scores were proved to be accurate in predicting CI-AKI (area under ROC curve: NRS-2002, 0.625; CONUT, 0.609; PNI, 0.629; and GNRI, 0.603). Conclusions Nutritional risks (high scores of NRS-2002 and CONUT; low scores of PNI and GNRI) were associated with CI-AKI in patients undergoing CAG.

BackgroundIdentifying high-risk patients for contrast-associated acute kidney injury (CA-AKI) helps to take early preventive interventions. The current study aimed to establish and validate an online pre-procedural nomogram for CA-AKI in patients undergoing coronary angiography (CAG).MethodsIn this retrospective dataset, 4,295 patients undergoing CAG were enrolled and randomized into the training or testing dataset with a split ratio of 8:2. Optimal predictors for CA-AKI were determined by Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) algorithm. Nomogram was developed and deployed online. The discrimination and accuracy of the nomogram were evaluated by receiver operating characteristic (ROC) and calibration analysis, respectively. Clinical usefulness was estimated by decision curve analysis (DCA) and clinical impact curve (CIC).ResultsA total of 755 patients (17.1%) was diagnosed with CA-AKI. 7 pre-procedural predictors were identified and integrated into the nomogram, including age, gender, hemoglobin, N-terminal of the prohormone brain natriuretic peptide, neutrophil-to-lymphocyte ratio, cardiac troponin I, and loop diuretics use. The ROC analyses showed that the nomogram had a good discrimination performance for CA-AKI in the training dataset (area under the curve, AUC = 0.766, 95%CI [0.737 to 0.794]) and testing dataset (AUC = 0.737, 95%CI [0.693 to 0.780]). The nomogram was also well-calibrated in both the training dataset (P = 0.965) and the testing dataset (P = 0.789). Good clinical usefulness was identified by DCA and CIC. Finally, this model was deployed in a web server for public use (https://duanbin-li.shinyapps.io/DynNomapp/).ConclusionAn easy-to-use pre-procedural nomogram for predicting CA-AKI was established and validated in patients undergoing CAG, which was also deployed online.