Risk factors of contrast-induced nephropathy in patients with STEMI and pump failure undergoing percutaneous coronary intervention

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Predicting Contrast-induced Renal Complications

Chronic kidney disease is an independent risk factor for the development of coronary artery disease and overlaps with other risk factors such as hypertension and diabetes. Percutaneous coronary intervention is a cornerstone of therapy for coronary artery disease and requires contrast media, which can lead to renal injury. Identifying patients at risk for contrast-associated acute kidney injury (CA-AKI) is critical for preventing kidney damage, which is associated with both short- and long-term mortality. Determination of the potential risk for CA-AKI and a new need for dialysis using validated risk prediction tools identifies patients at high risk for this complication. Identification of patients at risk for renal injury after contrast exposure is the first critical step in prevention. Contrast media volume, age and sex of the patient, a history of chronic kidney disease and/or diabetes, clinical presentation, and hemodynamic and volume status are factors known to predict incident contrast-induced nephropathy. Recognition of at-risk patient subpopulations allows for targeted, efficient, and cost-effective strategies to reduce the risk of renal complications resulting from contrast media exposure.

The utility of systemic immune-inflammation index for predicting contrast-induced nephropathy in patients with ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention

OBJECTIVE

The systemic immune-inflammation index (SII), derived from counts of neutrophil, platelet and lymphocyte, have been developed to predict clinical outcomes in several cancers and cardiovascular diseases. The aim of this study was to evaluate the utility of SII to predict contrast-induced nephropathy (CIN) in patients with ST-segment elevation myocardial infarction (STEMI) who underwent primary percutaneous coronary intervention (PCI).

METHODS

A total of 632 patients with STEMI who underwent primary PCI were retrospectively included. The patients divided into two groups based on the presence or absence of CIN. Baseline demographic, laboratory and clinic characteristics were evaluated between the two groups. Logistic regression analysis was used to identify independent predictors of CIN.

RESULTS

The receiver-operating characteristic (ROC) curve analysis demonstrated that the optimal cut-off value of SII for predicting CIN was 1282 with a sensitivity 76.1% and specificity 86.7% (AUC: 0.834; 95% CI 0.803-0.863; p<0.001). Multivariate analysis performed in two models (SII; as separate continuous and categorical variables) showed age, estimated glomerular filtration rate (eGFR), diabetes, left ventricular ejection fraction (LVEF), Killip class ≥2, use of intravenous diuretic, Troponin I, and SII as independent predictors of CIN in Model 1. In Model 2, age, eGFR, diabetes, LVEF, Killip class ≥2, use of intravenous diuretic, Troponin I, and a value of SII >1282 (p<0.001, OR 6.205, 95% CI 2.301-12.552) remained as independent predictors of CIN.

CONCLUSION

SII may be a useful and reliable indicator to predict the development of CIN in patients with STEMI undergoing primary PCI than NLR and PLR.

Hypertension as a Risk Factor for Contrast-Associated Acute Kidney Injury: A Meta-Analysis Including 2,830,338 Patients

OBJECTIVE

Previous studies have shown that the relationship between hypertension (HT) and contrast-associated acute kidney injury (CA-AKI) is not clear. We apply a systematic review and meta-analysis to assess the association between HT and CA-AKI.

METHODS

We searched for articles on the study of risk factors for CA-AKI in the Embase, Medline, and Cochrane Database of Systematic Reviews (by March 25, 2021). Two authors independently performed quality assessment and extracted data such as the studies' clinical setting, the definition of CA-AKI, and the number of patients. The CA-AKI was defined as a serum creatinine (SCr) increase ≥25% or ≥0.5 mg/dL from baseline within 72 h. We used fixed or random models to pool adjusted OR (aOR) by STATA.

RESULTS

A total of 45 studies (2,830,338 patients) were identified, and the average incidence of CA-AKI was 6.48%. There was an increased risk of CA-AKI associated with HT (aOR: 1.378, 95% CI: 1.211-1.567, I2 = 67.9%). In CA-AKI with a SCr increase ≥50% or ≥0.3 mg/dL from baseline within 72 h, an increased risk of CA-AKI was associated with HT (aOR: 1.414, 95% CI: 1.152-1.736, I2 = 0%). In CA-AKI with a Scr increase ≥50% or ≥0.3 mg/dL from baseline within 7 days, HT increases the risk of CA-AKI (aOR: 1.317, 95% CI: 1.049-1.654, I2 = 51.5%).

CONCLUSION

Our meta-analysis confirmed that HT is an independent risk factor for CA-AKI and can be used to identify risk stratification. Physicians should pay more attention toward prevention and treatment of patients with HT in clinical practice.