A risk model for the early diagnosis of acute myocardial infarction in patients with chronic kidney disease

Xiao-Feng Su; Xu Chen; Tao Zhang; Jun-Mei Song; Xin Liu; Xing-Li Xu; Na Fan

doi:10.3389/fcvm.2023.1253619

A risk model for the early diagnosis of acute myocardial infarction in patients with chronic kidney disease

Front Cardiovasc Med. 2023 Oct 10:10:1253619. doi: 10.3389/fcvm.2023.1253619. eCollection 2023.

Authors

Xiao-Feng Su^{1

2}, Xu Chen^{1

2}, Tao Zhang^{1

2}, Jun-Mei Song^{1

2}, Xin Liu^{1

2}, Xing-Li Xu^{1

2}, Na Fan^{1

2}

Affiliations

¹ Ultrasound in Cardiac Electrophysiology and Biomechanics Key Laboratory of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
² Department of Cardiovascular Ultrasound & Noninvasive Cardiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.

Abstract

Introduction: Acute myocardial infarction (AMI) remains a critical disease, characterized by a high fatality rate in several countries. In clinical practice, the incidence of AMI is increased in patients with chronic kidney disease (CKD). However, the early diagnosis of AMI in the above group of patients is still poor.

Methods: In the present study, a total of 829 patients with CKD, defined by an estimated glomerular filtration rate (eGFR) of <60 ml/min/1.73 m² or 60-90 ml/min/1.73 m² for patients with mildly reduced kidney function, who attended the Sichuan Provincial People's Hospital (SPPH) between January 2018 and November 2022 were enrolled. All patients underwent coronary angiography due to the presence of typical or atypical symptoms of AMI. Patients were divided into the following two groups: The training cohort, including 255 participants with AMI and 242 without AMI; and the testing cohort, including 165 and 167 subjects with and without AMI, respectively. Furthermore, a forward stepwise regression model and a multivariable logistic regression model, named SPPH-AMI-model, were constructed to select significant predictors and assist the diagnosis of AMI in patients with CKD, respectively.

Results: The following factors were evaluated in the model: Smoking status, high sensitivity cardiac troponin I, serum creatinine and uric acid levels, history of percutaneous coronary intervention and electrocardiogram. Additionally, the area under the curve (AUC) of the receiver operating characteristic curve were determined in the risk model in the training set [AUC, 0.78; 95% confidence interval (CI), 0.74-0.82] vs. the testing set (AUC, 0.74; 95% CI, 0.69-0.79) vs. the combined set (AUC, 0.76; 95% CI, 0.73-0.80). Finally, the sensitivity and specificity rates were 71.12 and 71.21%, respectively, the percentage of cases correctly classified was 71.14%, while positive and negative predictive values of 71.63 and 70.70%, respectively, were also recorded.

Discussion: The results of the current study suggested that the SPPH-AMI-model could be currently considered as the only risk scoring system for the early diagnosis of AMI in patients with CKD. This method could help clinicians and emergency physicians to quickly and accurately diagnose AMI in patients with CKD to promote the immediate and effective treatment of these patients.

Keywords: SPPH-AMI-model; acute myocardial infarction; chronic kidney disease; early diagnose; test accuracy.

Grants and funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.