Renal Outcomes and Associated Contributing Factors in Patients with Acute Myocardial Infarction-A Retrospective Cohort Study

Background: Acute kidney injury (AKI) following acute myocardial infarction (AMI) is a specific type of cardio-renal syndrome (CRS) with a complex pathogenesis that has not been fully understood. This study aimed to explore the possible risk factors, and predict cardiovascular outcomes, AKI, and long-term renal function deterioration after AMI to provide guidance for improving cardiac and renal prognosis in type 1 CRS. Methods: A total of 723 patients who were admitted for AMI were enrolled and were grouped based on in-hospital death, major adverse cardiovascular events (MACEs) within 1 year, AKI, and long-term decline in renal function defined as estimated glomerular filtration rate (eGFR) decline of at least 25% or development of end-stage renal disease within 1 year of discharge in sequence, respectively. The influence of common cardiovascular risk factors as well as cardiac and renal indicators on these outcomes was investigated. The prediction models of AKI after AMI were established and compared. Results: For patients with AMI, AKI was found to be an independent risk factor for in-hospital death [Odds ratio (OR) = 4.28, 95% confidence interval (CI): 1.745-10.501, p = 0.001], MACEs within 1 year (OR = 2.249, 95% CI: 1.37-3.692, p = 0.001), and long-term renal impairment (OR = 5.292, 95% CI: 2.422-11.567,p < 0.001). Fasting blood glucose (FBG) (OR =1.146, 95% CI: 1.038-1.264, p = 0.007), cystatin C (OR = 3.900, 95% CI: 1.805-8.430, p = 0.001), and left ventricular ejection fraction (LVEF) (OR = 0.977, 95% CI: 0.956-0.999,p = 0.041) were identified as independent risk factors for AKI after AMI. A prediction model that combines FBG, LVEF, cystatin C, hypertension, and N-terminal pro-brain natriuretic peptide (NT-proBNP) [ln(p/1-p) = 0.082 x FBG - 0.026 x LVEF + 0.743 x cystatin C + 0.698 x HBP + 0.001 x NT-proBNP - 2.414, the area under the ROC curve (AUC) = 0.800, 95% CI: 0.754-0.847] was relatively effective in predicting AKI after AMI, with sensitivity and specificity of 0.779 and 0.722, respectively. Conclusions: AKI is an independent risk factor for in-hospital death, major cardiovascular adverse events, and long-term decline in renal function in AMI patients. A prediction model consisting of FBG, LVEF, cystatin C, hypertension, and NT-proBNP could be useful in predicting AKI after AMI.