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Prevalence and Clinical Impact of Electrocardiographic Abnormalities in Patients with Chronic Kidney Disease

Authors :
Sejun Park
Yunjin Yum
Jung-Joon Cha
Hyung Joon Joo
Jae Hyoung Park
Soon Jun Hong
Cheol Woong Yu
Do-Sun Lim
Source :
Journal of Clinical Medicine, Vol 11, Iss 18, p 5414 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Chronic kidney disease (CKD) is a strong risk factor for cardiovascular disease. An electrocardiogram (ECG) is a basic test for screening cardiovascular disease. However, the impact of ECG abnormalities on cardiovascular prognosis in patients with CKD is largely unknown. A total of 2442 patients with CKD (stages 3–5) who underwent ECG between 2013 and 2015 were selected from the electronic health record database of the Korea University Anam Hospital. ECG abnormalities were defined using the Minnesota classification. The five-year major adverse cerebrocardiovascular event (MACCE), the composite of death, myocardial infarction (MI), and stroke were analyzed. The five-year incidences for MACCE were 27.7%, 20.8%, and 17.2% in patients with no, minor, and major ECG abnormality (p < 0.01). Kaplan–Meier curves also showed the highest incidence of MI, death, and MACCE in patients with major ECG abnormality. Multivariable Cox regression analysis revealed age, sex, diabetes, CKD stage, hsCRP, antipsychotic use, and major ECG abnormality as independent risk predictors for MACCE (adjusted HR of major ECG abnormality: 1.39, 95% CI: 1.09–1.76, p < 01). Among the detailed ECG diagnoses, sinus tachycardia, myocardial ischemia, atrial premature complex, and right axis deviation were proposed as important ECG diagnoses. The accuracy of cardiovascular risk stratification was improved when the ECG results were added to the conventional SCORE model (net reclassification index 0.07). ECG helps to predict future cerebrocardiovascular events in CKD patients. ECG diagnosis can be useful for cardiovascular risk evaluation in CKD patients when applied in addition to the conventional risk stratification model.

Details

Language :
English
ISSN :
11185414 and 20770383
Volume :
11
Issue :
18
Database :
Directory of Open Access Journals
Journal :
Journal of Clinical Medicine
Publication Type :
Academic Journal
Accession number :
edsdoj.90e31248c664bb98b21c765b73d0cb0
Document Type :
article
Full Text :
https://doi.org/10.3390/jcm11185414