The Predictive Value of Hyperuricemia on Renal Outcome after Contrast-Enhanced Computerized Tomography

Contrast-induced acute kidney injury: a review of definition, pathogenesis, risk factors, prevention and treatment

Contrast-induced acute kidney injury (CI-AKI) has become the third leading cause of hospital-acquired AKI, which seriously threatens the health of patients. To date, the precise pathogenesis of CI-AKI has remained not clear and may be related to the direct cytotoxicity, hypoxia and ischemia of medulla, and oxidative stress caused by iodine contrast medium, which have diverse physicochemical properties, including cytotoxicity, permeability and viscosity. The latest research shows that microRNAs (miRNAs) are also involved in apoptosis, pyroptosis, and autophagy which caused by iodine contrast medium (ICM), which may be implicated in the pathogenesis of CI-AKI. Unfortunately, effective therapy of CI-AKI is very limited at present. Therefore, effective prevention of CI-AKI is of great significance, and several preventive options, including hydration, antagonistic vasoconstriction, and antioxidant drugs, have been developed. Here, we review current knowledge about the features of iodine contrast medium, the definition, pathogenesis, molecular mechanism, risk factors, prevention and treatment of CI-AKI.

A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study

BACKGROUND

In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients.

METHODS

3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used.

RESULTS

In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777-0.853)) and external validation (AUC: 0.816 (95% CI 0.770-0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783-0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755-0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688-0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator.

CONCLUSION

We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions.

Association between uric acid level and contrast-induced acute kidney injury in patients with type 2 diabetes mellitus after coronary angiography: a retrospective cohort study

BACKGROUND

This study assessed the predictive value of uric acid (UA) for contrast-induced acute kidney injury (CI-AKI) in patients with type 2 diabetes mellitus (T2DM) who underwent coronary angiography (CAG). A nomogram to aid in the prediction of CI-AKI was also developed and validated, and the construction of a prognostic nomogram combined with clinical features was attempted.

METHODS

This study retrospectively enrolled T2DM patients who underwent CAG between December 2019 and December 2020 at the Affiliated Zhongda Hospital of Southeast University. Multivariable logistic regression analysis was used for the analysis of clinical outcomes. Receiver operating characteristic (ROC) analyses were performed to determine the area under the ROC curve (AUC) and the cut-off points for continuous clinical data. The prediction accuracies of models for CI-AKI were estimated through Harrell's concordance indices (C-index). Nomograms of the prognostic models were plotted for individualized evaluations of CI-AKI in T2DM patients after CAG.

RESULTS

A total of 542 patients with T2DM who underwent CAG were included in this study. We found that a high UA level (≥ 425.5 µmol/L; OR = 6.303), BUN level (≥ 5.98 mmol/L; OR = 3.633), Scr level (≥ 88.5 µmol/L; OR = 2.926) and HbA1C level (≥ 7.05%; OR = 5.509) were independent factors for CI-AKI in T2DM patients after CAG. The nomogram model based on UA, BUN, Scr and HbA1C levels presented outstanding performance for CI-AKI prediction (C-index: 0.878). Decision curve analysis (DCA) showed good clinical applicability in predicting the incidence of CI-AKI in T2DM patients who underwent CAG.

CONCLUSION

High UA levels are associated with an increased incidence of CI-AKI in T2DM patients after CAG. The developed nomogram model has potential predictive value for CI-AKI and might serve as an economic and efficient prognostic tool in clinical practice.

Diagnostics, Risk Factors, Treatment and Outcomes of Acute Kidney Injury in a New Paradigm

Acute kidney injury (AKI) is a common clinical condition among patients admitted in the hospitals. The condition is associated with both increased short-term and long-term mortality. With the development of a standardized definition for AKI and the acknowledgment of the impact of AKI on patient outcomes, there has been increased recognition of AKI. Two advances from past decades, the usage of computer decision support and the discovery of AKI biomarkers, have the ability to advance the diagnostic method to and further management of AKI. The increasingly widespread use of electronic health records across hospitals has substantially increased the amount of data available to investigators and has shown promise in advancing AKI research. In addition, progress in the finding and validation of different forms of biomarkers of AKI within diversified clinical environments and has provided information and insight on testing, etiology and further prognosis of AKI, leading to future of precision and personalized approach to AKI management. In this this article, we discussed the changing paradigms in AKI: from mechanisms to diagnostics, risk factors, and management of AKI.