AUTHOR=Yuan Ying , Qiu Hong , Hu Xiaoying , Zhang Jun , Wu Yuan , Qiao Shubin , Yang Yuejin , Gao Runlin 

TITLE=A risk score model of contrast-induced acute kidney injury in patients with emergency percutaneous coronary interventions

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.989243

DOI=10.3389/fcvm.2022.989243

ISSN=2297-055X

ABSTRACT=Background: Previous score models of contrast-induced acute kidney injury (CI-AKI) were mostly based on selective percutaneous coronary intervention (PCI) cases. Our study was to form a risk score model of CI-AKI and make a temporal validation in a population who underwent emergency PCIs.
Methods: Patients who underwent emergency PCIs from 2013 to 2018 were enrolled and divided into derivation and validation cohorts. Logistic regression analysis was used to create the risk model. In the study, CI-AKI was defined as an increase in serum creatinine (SCr) ≥0.5 mg/dL (44.2 μmol/L) above baseline within seven days after exposure to contrast.
Results: A total of 3564 patients who underwent emergency PCIs were enrolled and divided into the derivation (2376 cases) and validation cohorts (1188 cases), with CI-AKI incidence of 6.61% and 5.39%, respectively. By logistic analysis, the CI-AKI risk score model was constituted by 8 variables: female (1 point), history of transient ischemic attack (TIA)/stroke (1 point), left ventricular ejection fraction (LVEF) classification (1 point per class), big endothelin-1 (ET-1) classification (1 point per class), estimated glomerular filtration rate (eGFR) classification (1 point per class), intro-aortic balloon pump (IABP) application (1 point), left anterior descending (LAD) stented (1 point), and administration of diuretic (2 points). The CI-AKI risk score model performed well in discrimination and calibration ability and showed a superior clinical utility.
Conclusion: We developed a simple CI-AKI risk score model which performs well as a tool for CI-AKI prediction in patients who underwent emergency PCIs.