A Novel Risk Assessment Model Using Urinary System Contrast Blush Grading to Predict Contrast-Induced Acute Kidney Injury in Low-Risk Profile Patients

2, 2-dimethylthiazolidine hydrochloride protects against experimental contrast-induced acute kidney injury via inhibition of tubular ferroptosis

Contrast-induced acute kidney injury (CI-AKI) has become the third leading cause of AKI acquired in hospital, lacking of effective interventions. In the study, we identified the renal beneficial role of 2, 2-dimethylthiazolidine hydrochloride (DMTD), a safer compound which is readily hydrolyzed to cysteamine, in the rodent model of CI-AKI. Our data showed that administration of DMTD attenuated the impaired renal function and tubular injury induced by the contrast agent. Levels of MDA, 4-hydroxynonenal, ferrous iron and morphological signs showed that contrast agent induced ferroptosis, which could be inhibited in the DMTD group. In vitro, DMTD suppressed ferroptosis induced by ioversol in the cultured tubular cells. Treatment of DMTD upregulated glutathione (GSH) and glutathione peroxidase 4 (GPX4). Moreover, we found that DMTD promoted the ubiquitin-mediated proteasomal degradation of Keap1, and thus increased the activity of nuclear factor erythroid 2-related factor 2 (Nrf2). Mechanistically, increase of the ubiquitylation degradation of Keap1 mediates the upregulated effect of DMTD on Nrf2. Consequently, activated Nrf2/Slc7a11 results in the increase of GSH and GPX4, and therefore leads to the inhibition of ferroptosis. Herein, we imply DMTD as a potential therapeutic agent for the treatment of CI-AKI.

Prognostic Nutritional Index Combined with Triglyceride-Glucose Index to Contrast a Nomogram for Predicting Contrast-Induced Kidney Injury in Type 2 Diabetes Mellitus Patients with Acute Coronary Syndrome After Percutaneous Coronary Intervention

Objective

Our objective was to develop and validate a nomogram model aiming at predicting the risk of contrast-induced acute kidney injury (CI-AKI) following percutaneous coronary intervention (PCI) in patients suffering from type 2 diabetes mellitus (T2DM) and also diagnosed with acute coronary syndrome (ACS).

Methods

The study gathered data from 722 T2DM patients with ACS who received PCI treatment at the Affiliated Hospital of Xuzhou Medical University between February 2019 and December 2022, serving as the training set. Considering the validation set, the study included 217 patients who received PCI at the East Affiliated Hospital of Xuzhou Medical University. The patients were classified into CI-AKI and non-CI-AKI groups. The study employed univariate and multivariate logistic analysis for identifying independent risk factors for CI-AKI, followed by developing a predictive nomogram model for CI-AKI risk using R software. The predictive performance and clinical utility of the nomogram were assessed through internal and external validation, utilizing the areas under the receiver operating characteristic curve (AUC-ROC), the Hosmer-Lemeshow test and calibration correction curve, and decision curve analysis (DCA).

Results

The nomogram comprised four variables: age, estimated glomerular filtration rate (eGFR), triglyceride-glucose (TyG) index, and prognostic nutritional index (PNI). The AUC-ROC were 0.785 (95% confidence interval (CI) 0.729-0.841) and 0.802 (95% CI 0.699-0.905) for the training and validation cohorts, respectively, indicating a high discriminative ability of the nomogram. The calibration assessment and decision curve analysis have substantiated the strong concordance and clinical usefulness of the aforementioned.

Conclusion

The nomogram exhibits favorable discrimination and accuracy, enabling it to visually and individually identify pre-procedure high-risk patients, and possesses a predictive capacity regarding CI-AKI incidence after PCI in patients diagnosed with both T2DM and ACS.

A Predictive Model for Contrast-Induced Acute Kidney Injury After Percutaneous Coronary Intervention in Elderly Patients with ST-Segment Elevation Myocardial Infarction

Purpose

Development and validation of a nomogram model to predict the risk of Contrast-Induced Acute Kidney Injury (CI-AKI) after emergency percutaneous coronary intervention (PCI) in elderly patients with acute ST-segment elevation myocardial infarction (STEMI).

Patients and Methods

Retrospective analysis of 542 elderly (≥65 years) STEMI patients undergoing emergency PCI in our hospital from January 2019 to June 2022, with all patients randomized to the training cohort (70%; n=380) and the validation cohort (30%; n=162). Univariate analysis, LASSO regression, and multivariate logistic regression analysis were used to determine independent risk factors for developing CI-AKI in elderly STEMI patients. R software is used to generate a nomogram model. The predictive power of the nomogram model was compared with the Mehran score 2. The area under the ROC curve (AUC), calibration curves, and decision curve analysis (DCA) was used to evaluate the prediction model's discrimination, calibration, and clinical validity, respectively.

Results

The nomogram model consisted of five variables: diabetes mellitus (DM), left ventricular ejection fraction (LVEF), Systemic immune-inflammatory index (SII), N-terminal pro-brain natriuretic peptide (NT-proBNP), and highly sensitive C-reactive protein(hsCRP). In the training cohort, the AUC is 0.84 (95% CI: 0.790-0.890), and in the validation cohort, it is 0.844 (95% CI: 0.762-0.926). The nomogram model has better predictive ability than Mehran score 2. Based on the calibration curves, the predicted and observed values of the nomogram model were in good agreement between the training and validation cohort. Decision curve analysis (DCA) and clinical impact curve showed that the nomogram prediction model has good clinical utility.

Conclusion

The established nomogram model can intuitively and specifically screen high-risk groups with a high degree of discrimination and accuracy and has a specific predictive value for CI-AKI occurrence in elderly STEMI patients after PCI.

Elevated Serum Uric Acid/Albumin Ratio as a Predictor of Post-Contrast Acute Kidney Injury After Percutaneous Coronary Intervention in Patients with ST-Segment Elevation Myocardial Infarction

Background

The serum uric acid/albumin ratio (sUAR), a novel inflammatory marker, effectively predicts acute kidney injury (AKI) and cardiovascular outcomes. However, whether the sUAR predicts post-contrast acute kidney injury (PC-AKI) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI) remains uncertain. In this study, we evaluated the association between the sUAR and PC-AKI in patients with STEMI undergoing PCI.

Methods

We consecutively recruited patients with STEMI who underwent PCI and stratified them into three groups according to the terciles of the sUAR. The primary outcome was the incidence of PC-AKI. The association between the sUAR and PC-AKI was assessed by multivariate logistic regression analysis.

Results

A total of 2861 patients with STEMI were included in this study. The incidence of PC-AKI increased stepwise with increasing sUAR tercile (2.6% vs 4.0% vs 11.6%, p < 0.001), and the incidence of in-hospital major adverse clinical events (MACEs) was highest among patients in the Q3 group. Multivariate logistic regression analysis revealed that the sUAR was also an independent predictor of PC-AKI (continuous sUAR, per 1-unit increase, odds ratio [OR] [95% confidence interval (CI)]: 1.06 [1.02–1.10], p = 0.005; tercile of sUAR, OR [95% CI] for Q2 and Q3: 1.18 [0.69–2.01] and 1.85 [1.12–3.06], respectively, with Q1 as a reference) but not in-hospital MACEs. In the receiver operating characteristic (ROC) analysis, the area under the curve (AUC) of the sUAR for predicting PC-AKI was 0.708 (95% CI: 0.666–0.751), and ROC analysis also showed that the sUAR was superior to uric acid and albumin alone in predicting PC-AKI.

Conclusion

Increasing sUAR was significantly associated with a higher risk of PC-AKI but not in-hospital MACEs in patients with STEMI who underwent PCI, suggesting that sUAR had a predictive value for PC-AKI after PCI in patients with STEMI. Further studies are required to confirm this finding.

Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study

OBJECTIVE

Clear and specific reporting of a research paper is essential for its validity and applicability. Some studies have revealed that the reporting of studies based on the clinical prediction models was generally insufficient based on the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist. However, the reporting of studies on contrast-induced nephropathy (CIN) prediction models in the coronary angiography (CAG)/percutaneous coronary intervention (PCI) population has not been thoroughly assessed. Thus, the aim is to evaluate the reporting of the studies on CIN prediction models for the CAG/PCI population using the TRIPOD checklist.

DESIGN

A cross-sectional study.

METHODS

PubMed and Embase were systematically searched from inception to 30 September 2021. Only the studies on the development of CIN prediction models for the CAG/PCI population were included. The data were extracted into a standardised spreadsheet designed in accordance with the 'TRIPOD Adherence Assessment Form'. The overall completeness of reporting of each model and each TRIPOD item were evaluated, and the reporting before and after the publication of the TRIPOD statement was compared. The linear relationship between model performance and TRIPOD adherence was also assessed.

RESULTS

We identified 36 studies that developed CIN prediction models for the CAG/PCI population. Median TRIPOD checklist adherence was 60% (34%-77%), and no significant improvement was found since the publication of the TRIPOD checklist (p=0.770). There was a significant difference in adherence to individual TRIPOD items, ranging from 0% to 100%. Moreover, most studies did not specify critical information within the Methods section. Only 5 studies (14%) explained how they arrived at the study size, and only 13 studies (36%) described how to handle missing data. In the Statistical analysis section, how the continuous predictors were modelled, the cut-points of categorical or categorised predictors, and the methods to choose the cut-points were only reported in 7 (19%), 6 (17%) and 1 (3%) of the studies, respectively. Nevertheless, no relationship was found between model performance and TRIPOD adherence in both the development and validation datasets (r=-0.260 and r=-0.069, respectively).

CONCLUSIONS

The reporting of CIN prediction models for the CAG/PCI population still needs to be improved based on the TRIPOD checklist. In order to promote further external validation and clinical application of the prediction models, more information should be provided in future studies.

Comparative analysis of four established risk scores for predicting contrast induced acute kidney injury after primary percutaneous coronary interventions

Objectives

 This study aimed to compare Mehran Risk Score (MRS) with three well -known scoring systems namely CHA₂DS₂-VASc score, Canada Acute Coronary Syndrome Risk Score (C-ACS), and Thrombolysis in Myocardial Infarction risk index (TRI) to predict the contrast-induced acute kidney injury (CI-AKI) after primary percutaneous coronary intervention (PCI).

Background

CI-AKI is a common complication after primary PCI associated with an adverse prognosis.

Methods

In this study consecutive patients of primary PCI were included. Patients with chronic kidney diseases, exposure to the contrast medium within the past 7 days, and Killip class IV at presentation were excluded. MRS along with three risk scores namely CHA₂DS₂-VASc, C-ACS, and TRI were calculated for all patients and CI-AKI was defined as either 0.5 mg/dL or 25% relative increase in post-procedure serum creatinine. The area under the curve (AUC) curve was reported.

Results

Post primary PCI CI-AKI was observed in 63 (9.1%) patients out of 691 patients. The AUC was 0.745 [0.679-0.810] for MRS, 0.725 [0.662-0.788] for CHA₂DS₂-VASc, 0.671 [0.593-0.749] for C-ACS, and 0.734 [0.674-0.795] for TRI. Sensitivity and specificity were 61.9% [48.8-73.8%] and 76.0% [72.4-79.3%] for MRS ≥ 6.5, 66.7% [53.7-78.0%] and 66.7% [62.9-70.4%] for CHA₂DS₂-VASc ≥ 2, 52.4% [39.4-65.1%] and 79.9% [76.6-83.0%] for C-ACS ≥ 1, and 87.3% [76.5-94.4%] and 49.2% [45.2-53.2%] for TRI ≥ 16 respectively.

Conclusions

The MRS has shown higher discriminating power than CHA₂DS₂-VASc, C-ACS, and TRI. However, the TRI can be of good value in clinical practice due to its simplicity and high sensitivity in detecting patients at higher risk of CI-AKI after primary PCI.

Predictive value of creatine kinase MB for contrast-induced acute kidney injury among myocardial infarction patients

Background

Predictive value of creatine kinase MB (CK-MB) for contrast-induced acute kidney injury (CI-AKI) among myocardial infarction (MI) patients has rarely been reported. We aim to evaluate the predictive value of CK-MB for CI-AKI among MI patients.

Methods

Totally, 1131 MI patients were included from the REduction of rIsk for Contrast-Induced Nephropathy (REICIN) study. The peak CK-MB before coronary angiography (CAG) was chosen. The study population was divided into two groups by log-transformed CK-MB cut-off point. The association between CK-MB and CI-AKI was tested by multivariable logistic regression. CK-MB was integrated with Age, creatinine and ejection fraction (ACEF) score and Mehran risk score (MRS) to evaluate the additive value of CK-MB. The integrated models were validated internally by the bootstrap method and externally by the PREdictive Value of COntrast voluMe to creatinine Clearance Ratio (PRECOMIN) study data set.

Results

Overall, 62(5.48%) patients developed CI-AKI, patients with CK-MB point > 4.7 displayed a higher incidence of CI-AKI than those without (11.9% vs. 4.0%, p < 0.001). CK-MB point > 4.7 was independently associated with CI-AKI (adjusted OR: 3.40, 95% CI: 1.93–5.98, p < 0.001). The additions of CK-MB to ACEF score, Mehran score A and Mehran score B resulted in increases in C-statistics, which ranged from 0.680 to 0.733 (p = 0.046), 0.694 to 0.727 (p = 0.091), 0.704 to 0.734 (p = 0.102), respectively. Internal validation also showed increases in C-statistics, and external validation performed well in discrimination and calibration.

Conclusions

Preprocedural peak CK-MB was a predictor of CI-AKI among MI patients.