A Novel Prediction Model of Acute Kidney Injury Based on Combined Blood Variables in STEMI

Yuhei Goriki; Atsushi Tanaka; Kensaku Nishihira; Nehiro Kuriyama; Yoshisato Shibata; Koichi Node

doi:10.1016/j.jacasi.2021.07.013

A Novel Prediction Model of Acute Kidney Injury Based on Combined Blood Variables in STEMI

JACC Asia. 2021 Oct 26;1(3):372-381. doi: 10.1016/j.jacasi.2021.07.013. eCollection 2021 Dec.

Authors

Yuhei Goriki^{1

2}, Atsushi Tanaka², Kensaku Nishihira³, Nehiro Kuriyama³, Yoshisato Shibata³, Koichi Node²

Affiliations

¹ Department of Cardiovascular Medicine, National Hospital Organization Ureshino Medical Center, Saga, Japan.
² Department of Cardiovascular Medicine, Saga University, Saga, Japan.
³ Miyazaki Medical Association Hospital Cardiovascular Center, Miyazaki, Japan.

Abstract

Background: Development of acute kidney injury (AKI) is associated with poor prognosis in patients with ST-segment elevation myocardial infarction (STEMI).

Objective: This study sought to investigate whether a combination of pre-procedural blood tests could predict the incidence of AKI in patients with STEMI.

Methods: A total of 908 consecutive Japanese patients with STEMI who underwent primary percutaneous coronary intervention within 48 hours of symptom onset were recruited and divided into derivation (n = 617) and validation (n = 291) cohorts. A risk score model was created based on a combination of parameters assessed on routine blood tests on admission.

Results: In the derivation cohort, multivariate analysis showed that the following 4 variables were significantly associated with AKI: blood sugar ≥200 mg/dL (odds ratio [OR]: 2.07), high-sensitivity troponin I >1.6 ng/mL (upper limit of normal ×50) (OR: 2.43), albumin ≤3.5 mg/dL (OR: 2.85), and estimated glomerular filtration rate <45 mL/min/1.73 m² (OR: 2.64). Zero to 4 points were given according to the number of those factors. Incremental risk scores were significantly associated with a higher incidence of AKI in both cohorts (P < 0.001). Receiver-operating characteristic curve analysis of risk models showed adequate discrimination between patients with and without AKI (derivation cohort, area under the curve: 0.754; 95% confidence interval: 0.733-0.846; validation cohort, area under the curve: 0.754; 95% confidence interval: 0.644-0.839).

Conclusions: Our novel laboratory-based model might be useful for early prediction of the post-procedural risk of AKI in patients with STEMI.

Keywords: AKI, acute kidney injury; AMI, acute myocardial infarction; AUC, area under the curve; BS, blood sugar; IABP, intra-aortic balloon pumping; PCI, percutaneous coronary intervention; STEMI, ST-segment elevation myocardial infarction; acute kidney injury; biomarker; eGFR, estimated glomerular filtration rate; hsTnI, high-sensitivity troponin I; myocardial infarction.