Prevalence and clinical impact of electrocardiographic abnormalities in patients with chronic kidney disease

doi:10.21203/rs.3.rs-1507372/v1

Download PDF

Research Article

Prevalence and clinical impact of electrocardiographic abnormalities in patients with chronic kidney disease

https://doi.org/10.21203/rs.3.rs-1507372/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Chronic kidney disease (CKD) is a strong risk factor for cardiovascular disease. Electrocardiogram (ECG) is the basic test for screening cardiovascular disease. However, the impact of ECG abnormalities on cardiovascular prognosis in patients with CKD is largely unknown.

Methods

A total of 1,295 patients with CKD (stage 3, 4, and 5) who underwent ECG between 2013 and 2015 were selected from the tertiary hospital in Korea. ECG abnormalities were defined using the Minnesota classification. Five-year major adverse cerebrocardiovascular event (MACCE), a composite of death, myocardial infarction (MI) and stroke, was analyzed after excluding 251 patients (n=1,044) with prior MI or stroke.

Results

The higher the CKD stage, the higher the proportion of patients with a major ECG abnormality. Five-year MACCE incidences were 34%, 23.9%, and 21.7% in patients with major, minor and no ECG abnormalities. MACCE and MI incidences were significantly higher in patients with severe ECG abnormalities (log-rank p < 0.001). Major ECG abnormalities were significantly associated with MACCE (adjusted HR 1.48, 95% CI 1.11-1.97). In particular, atrial fibrillation, atrial premature complex, and QT interval prolongation were identified as important ECG diagnoses. The accuracy of cardiovascular risk stratification was improved when ECG diagnosis was added to the conventional SCORE model (net reclassification index 0.09).

Conclusions

Major ECG abnormality was a significant predictor of cardiovascular events in CKD patients. ECG diagnosis can be useful in evaluating the risk of cardiovascular disease in CKD patients when applied in addition to the conventional risk stratification model.

Electrocardiogram

Chronic kidney disease

Major adverse cerebrocardiovascular events

Chronic kidney disease (CKD) has been suggested to be a very strong risk factor for cardiovascular disease (CVD) and increased cardiovascular mortality(1–4). Recent US Medicare data demonstrated that the prevalences of heart failure, acute myocardial infarction, and cerebrovascular accident/transient ischemic attack in CKD patients are two to four times higher compared to non-CKD patients(5). Survival probability after a first CVD diagnosis was also decreased with higher stage of CKD. Conventional cardiovascular risk factors such as hypertension and diabetes mellitus are very common in CKD patients and are implicated in higher incidence of CVD and its mortality in CKD patients. Therefore, early identification of high-risk CVD patients can lead to targeted strategies to improve cardiovascular prognosis in CKD patients.

The standard 12-lead electrocardiogram (ECG) is an accessible and inexpensive test and has therefore been widely used for diagnosing or screening CVD. ECG reflects the electrophysiological and structural state of the heart. Previous studies demonstrated that abnormal ECG findings are associated with cardiovascular mortality even in the general population(6, 7). Left ventricular hypertrophy (LVH) diagnosed by ECG is a surrogate marker for target organ damage in hypertensive patients, but other ECG abnormalities are also associated with kidney damage such as microalbuminuria(8). Several ECG indices such as RP and QT intervals were also reported to be associated with CVD incidence and mortality in CKD patients(9, 10). However, few studies have been conducted on more than 1,000 CKD patients, especially in Asian people.

Here, we explored the demographic characteristics according to ECG abnormality and compared their cerebrocardiovascular prognostic impact.

Study Design

The present study was a retrospective cohort study using the electronic health record database of Korea University Anam Hospital in Korea. For the study population selection, among patients who took ECG between January 2013 and December 2015, 76,877 patients with adequate quality ECG results were screened from the database (Fig. 1). Patients on medications that potentially affect ECG results (n = 12,044) (Supplemental Table 1), patients with missing clinical and laboratory data (e.g., serum creatinine level) within three months of ECG acquisition (n = 15,227), and non-CKD patients with more than 60 mL/min/1.73m² of Modification of Diet in Renal Disease (MDRD) glomerular filtration rate (GFR) (n = 48,311) were excluded. Finally, 1,295 CKD patients remained for further analysis. The study was approved by the Institutional Review Board of Korea University Anam Hospital (IRB No. 2021AN0450). Written informed consent was waived due to the use of a retrospective study design of anonymized data with minimal risk to study subjects. The study also complied with the Declaration of Helsinki.

Standardization of computerized ECG diagnosis

ECG machines automatically generated ECG diagnoses and ancillary descriptions through the approved computerized algorithm. Performance of computerized ECG diagnosis is known to be comparable to physicians’ interpretations(11, 12). Automated ECG interpretation is also cost- and time-effective with less intra- and inter-observer variability. For the present study, the computerized ECG diagnoses were transformed to the terminology of SNOMED CT(13). Practically, SNOMED CT mapping for ECG diagnosis was performed using web-based software, which is an integrated algorithm using cosine similarity and rule-based hierarchy (available at cdal.korea.ac.kr/ECG2CDM). Its conversion accuracy is 99.9%. Then, they were further categorized into the Minnesota code classification(14). For patients with multiple ECG results, only the earliest ECG results were selected. Each patient was grouped in the order of major, minor, and no ECG abnormality of the Minnesota code classification among different ECG diagnoses. Major ECG abnormalities include major Q wave abnormalities, minor Q wave abnormalities plus ST-T abnormalities, major isolated ST-T abnormalities, complete or intermittent left bundle branch block (LBBB)/right bundle branch block (RBBB), non-specific intraventricular block, RBBB with left anterior hemiblock, Brugada pattern, LVH plus ST-T abnormalities, major QT prolongation, atrial fibrillation or flutter, major AV conduction abnormalities, ventricular fibrillation or asystole, and supraventricular tachycardia. Minor ECG abnormalities include minor isolated Q/QS waves, minor ST/T abnormalities, high R waves, ST segment elevation, incomplete LBBB/RBBB, minor QT prolongation, short/long PR interval, left/right axis deviation, premature beats, wandering atrial pacemaker, sinus tachycardia, sinus bradycardia, supraventricular rhythm persistent, low voltage QRS, high amplitude P wave, left atrial enlargement, fragmented QRS, and early repolarization.

Definitions and study endpoint

Hypertension was defined as being on anti-hypertensive medication or having diagnosis codes for hypertension. Diabetes mellitus was defined as HbA1c ≥ 6.5%, being on antidiabetic medication, or having diagnosis codes for diabetes mellitus. Dyslipidemia was defined as having statin or ezetimibe prescription, serum total cholesterol ≥ 240 mg/dL, low-density lipoprotein-cholesterol (LDL-C) ≥ 160 mg/dL, triglyceride ≥ 200 mg/dL, or high-density lipoprotein-cholesterol (HDL-C) < 40 mg/dL. Patients on dialysis were defined as having diagnosis codes for end-stage renal failure on dialysis or procedure codes for dialysis and dialysis care education. Cardiovascular risk was calculated with the Systemic Coronary Risk Evaluation (SCORE) model and classified into low to moderate, high, and very high (15–17).

The primary endpoints of this study were five-year major adverse cerebrocardiovascular event (MACCE), a composite of death, new-onset myocardial infarction (MI) and stroke. MI was defined as having diagnosis codes for MI or serum CK-MB levels greater than the upper limit of normal. Stroke was defined as having diagnosis codes for stroke. Survival time was from follow-up start date to date of MACCE or until the end of follow-up, whichever came first.

Statistical analysis

Baseline characteristics are shown as the mean ± SD or n (%). Chi-square test and Analysis of Variance (ANOVA) were used to compare the categorical variables and continuous variables between groups. The probabilities for MACCE and the other endpoints were calculated by the Kaplan-Meier curves and compared by log-rank test. Multivariable Cox proportional hazards regression analysis was performed to evaluate the relationship between ECG result group and MACCE risk. Age, sex, current smoking, alcohol drinking, presence of hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease stage, and high-sensitivity C-reactive protein (hsCRP) level were considered as the potential confounding variables. The discrimination of the models was assessed by c-statistics and the net reclassification index (NRI)(18–20). The proportional hazards assumption for variables in the models were assessed by inspecting Schoenfeld residuals. All analyses were performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) program and R program (Ver 3.6.1).

Baseline characteristics

Baseline characteristics of the patients are described in Table 1. The numbers of patients with major, minor and no ECG abnormality were 387 (30%), 344 (27%) and 564 (43%). Patients with major abnormal ECG results were older and the proportions of hypertension, diabetes mellitus and dyslipidemia were also higher compared with patients with no or minor ECG abnormalities. The proportions of CKD stage V and dialysis patients were also higher in the group of patients with a major ECG abnormality than the other two groups (11.5%, 13.4%, 17.8%, p = 0.05 for CKD stage V; 6.9%, 7.0%, 12.4%, p < 0.01 for dialysis). Laboratory findings showed higher serum creatinine and hsCRP levels in patients with a major ECG abnormality than the others. The proportion of very high-risk group in 10-year CVD risk estimate (SCORE) was also higher in patients with a major ECG abnormality than the others (37.6%, 48.3%, 54.3%, p < 0.001).

Table 1

Baseline characteristics of study groups according to ECG abnormality
	Total population (N = 1295)	No abnormality (N = 564)	Minor abnormality (N = 344)	Major abnormality (N = 387)	p
Age	69.0 ± 13.2	67.2 ± 12.8	68.9 ± 13.7	71.8 ± 12.7	< 0.001
Male	656 (50.7)	272 (48.2)	183 (53.2)	201 (51.9)	0.29
Body mass index	24.6 ± 4.2	24.4 ± 3.7	24.7 ± 5.3	24.8 ± 3.8	0.23
Current Smoker	313 (24.2)	137 (24.3)	83 (24.1)	93 (24.0)	0.99
Alcohol drinking	335 (25.9)	148 (26.2)	93 (27.0)	94 (24.3)	0.67
Hypertension	775 (59.9)	316 (56.0)	199 (57.9)	260 (67.2)	0.002
Anti-hypertensive medication	734 (56.7)	303 (53.7)	188 (54.7)	243 (62.8)	0.01
Diabetes mellitus	665 (51.4)	272 (48.2)	171 (49.7)	222 (57.4)	0.02
Oral hypoglycemic agent	334 (25.8)	138 (24.5)	82 (23.8)	114 (29.5)	0.14
Insulin use	277 (21.4)	119 (21.1)	53 (15.4)	105 (27.1)	< 0.001
Dyslipidemia	775 (59.9)	318 (56.4)	201 (58.4)	256 (66.2)	0.01
Lipid-lowering medication	516 (39.9)	211 (37.4)	131 (38.1)	174 (45.0)	0.05
Chronic kidney disease Stage III Stage IV Stage V	1015 (78.4) 100 (7.7) 180 (13.9)	457 (81.0) 42 (7.4) 65 (11.5)	274 (79.7) 24 (7.0) 46 (13.4)	284 (73.4) 34 (8.8) 69 (17.8)	0.05
Dialysis	111 (8.6)	39 (6.9)	24 (7.0)	48 (12.4)	0.01
Laboratory findings
Total cholesterol (mg/dL)	168.2 ± 53.8	169.9 ± 55.1	169.0 ± 54.9	165.3 ± 50.6	0.42
LDL-cholesterol (mg/dL)	108.7 ± 39.7	108.8 ± 41.2	111.3 ± 39.2	106.5 ± 38.2	0.42
HDL-cholesterol (mg/dL)	43.1 ± 12.3	44.2 ± 12.9	42.6 ± 11.8	42.3 ± 11.9	0.16
Triglyceride (mg/dL)	152.7 ± 112.1	161.5 ± 127.9	153.9 ± 82.2	140.5 ± 110.4	0.05
Fasting glucose (mg/dL)	140.8 ± 73.1	139.5 ± 76.9	138.6 ± 68.6	144.7 ± 71.4	0.45
Hba1c (mg/dL)	6.9 ± 1.9	6.8 ± 1.8	7.0 ± 2.0	7.0 ± 1.9	0.51
Creatinine (mg/dL)	2.2 ± 2.4	2.0 ± 2.1	2.1 ± 2.2	2.6 ± 2.9	0.003
hsCRP (mg/dL)	2.5 ± 2.5	2.3 ± 2.4	2.4 ± 2.5	2.9 ± 2.7	0.01
10-year CVD risk (SCORE) Low/moderate High Very high	381 (29.4) 326 (25.2) 588 (45.4)	190 (33.7) 162 (28.7) 212 (37.6)	97 (28.2) 81 (23.6) 166 (48.3)	94 (24.3) 83 (21.5) 210 (54.3)	< 0.001
Values are presented as n (%) or mean ± standard deviation. LDL, low density lipoprotein; HDL, high density lipoprotein; hsCRP, high-sensitive C reactive protein.

Table 1. Baseline characteristics of study groups according to ECG abnormality

Proportion of ECG abnormalities

The two most common ECG diagnoses of major ECG abnormality were prolonged QT interval and AV block (1st degree) (Table 2). Sinus bradycardia, abnormal T wave and QT interval (prolonged) were the most common abnormal ECG diagnoses in CKD III/IV/V, respectively. These were followed by abnormal T wave in CKD III/V patients and sinus bradycardia, myocardial ischemia (lateral) and QT interval (prolonged) in CKD IV patients. Of note, the prevalence of prolonged QT interval increased as CKD stage increased (4.7% in CKD III, 11.0% in CKD IV, 20.6% in CKD V).

Table 2

Top 3 ECG diagnosis according to CKD stage and ECG group
CKD stage	Minnesota code classification	ECG diagnosis (SNOMED)	No. (%)
III	No ECG abnormalities	Normal sinus rhythm/ Sinus rhythm	759 (74.8)
		Sinus arrhythmia	16 (1.6)
		Aberrant conduction complex	9 (0.9)
	Minor ECG abnormalities	Sinus rhythm (bradycardia)	148 (14.6)
		T wave (abnormal)	112 (11.0)
		LVH	71 (7.0)
	Major ECG abnormalities	Myocardial infarction (inferior)	57 (5.6)
		QT interval (prolonged)	48 (4.7)
		AV block (1st degree)	43 (4.2)
IV	No ECG abnormalities	Normal sinus rhythm/ Sinus rhythm	77 (77.0)
		Sinus arrhythmia	2 (2.0)
		Aberrant conduction complex	1 (1.0)
	Minor ECG abnormalities	T wave (abnormal)	18 (18.0)
		Sinus rhythm (bradycardia)	11 (11.0)
		Left axis deviation	7 (7.0)
	Major ECG abnormalities	Myocardial ischemia (lateral)	11 (11.0)
		QT interval (prolonged)	11 (11.0)
		AV block (1st degree)	5 (5.0)
V	No ECG abnormalities	Normal sinus rhythm/ Sinus rhythm	157 (87.2)
		Sinus arrhythmia	2 (1.1)
		Junctional escape rhythm	1 (0.6)
	Minor ECG abnormalities	T wave (abnormal)	25 (13.9)
		LVH	23 (12.8)
		Sinus rhythm (bradycardia)	10 (5.6)
	Major ECG abnormalities	QT interval (prolonged)	37 (20.6)
		AV block (1st degree)	13 (7.2)
		Myocardial ischemia (lateral)	11 (6.1)
Values are presented as n (%). LVH, left ventricular hypertrophy; AV, atrioventricular.

Table 2. Top 3 ECG diagnosis according to CKD stage and ECG group

ECG abnormality and MACCE

After excluding patients with prior history of MI or stroke (n = 251), 1,044 patients were analyzed to evaluate the clinical impact of ECG abnormality on MACCE in CKD patients. Median follow-up period was 1826 days. The cumulative incidence of MACCE is shown in Table 3. The incidence rate of MACCE in the group with major ECG abnormality was higher compared with those in the other groups (21.7%, 23.9%, 34.0%, p < 0.001). Among MACCE, new-onset MI also showed higher incidence in the group with major ECG abnormalities, but death and stroke were not statistically different among the groups. Kaplan-Meier curves showed similar results (Fig. 2).

Table 3

The cumulative incidence rates of new-onset MI, stroke, death, and MACCE
	Total population (N = 1044)	No abnormality (N = 480)	Minor abnormality (N = 276)	Major abnormality (N = 288)	p
New-onset MI	160 (15.3)	53 (11.0)	42 (15.2)	65 (22.6)	< 0.001
New-onset stroke	64 (6.1)	25 (5.2)	16 (5.8)	23 (8.0)	0.13
All-cause death	121 (11.6)	52 (10.8)	30 (10.9)	39 (13.5)	0.29
MACCE	268 (25.7)	104 (21.7)	66 (23.9)	98 (34.0)	< 0.001
Values are presented as number of incidence (%). MI, myocardial infarction; MACCE, major adverse cerebro-cardiovascular event.

Multivariable Cox regression analysis

Cox regression analysis was performed to evaluate whether ECG abnormality was a meaningful risk factor for MACCE occurrence after adjusting for various confounding factors (Table 4). It proposed major ECG abnormality for the independent risk factor for MACCE in CKD patients (HR 1.48, 95%CI 1.11–1.97, p < 0.01). The other independent risk factors were male sex and diabetes mellitus. CKD stage was not presented as an independent risk factor in predicting MACCE in CKD patients.

Table 4

Multivariable Cox regression analyses for MACCE
Risk Factor	HR (95% CI)
Risk Factor	Unadjusted	Adjusted
A. Using ECG abnormality category of the Minnesota ECG classification
Age, years	1.01 (1.00-1.02)	1.01 (1.00-1.02)
Male	1.39 (1.09–1.77)**	1.45 (1.10–1.93)**
Current smoking	1.17 (0.90–1.54)	1.07 (0.77–1.49)
Alcohol drinking	0.96 (0.73–1.26)	0.81 (0.58–1.12)
Hypertension	1.07 (0.84–1.37)	0.82 (0.62–1.08)
Diabetes mellitus	1.48 (1.16–1.88)**	1.41 (1.07–1.86)*
Dyslipidemia	1.23 (0.96–1.57)	1.11 (0.84–1.46)
CKD stage III IV V	Reference 1.51 (1.01–2.28)* 1.34 (0.97–1.85)	Reference 1.49 (0.98–2.27) 1.33 (0.93–1.90)
hsCRP, mg/dL	1.05 (1.01–1.10)*	1.04 (1.00-1.09)
ECG abnormality Normal Minor Major	Reference 1.13 (0.83–1.53) 1.68 (1.27–2.21)**	Reference 1.09 (0.80–1.49) 1.48 (1.11–1.97)**
B. Using the detailed ECG diagnosis
Male	1.39 (1.09–1.77)**	1.35 (1.06–1.73)*
Diabetes mellitus	1.48 (1.16–1.88)**	1.38 (1.08–1.76)*
ECG diagnosis Atrial fibrillation Atrial premature complex QT interval (prolonged)	1.95 (1.11–3.40)* 2.95 (1.52–5.74) 2.17 (1.51–3.12)	1.98 (1.13–3.46)* 2.34 (1.20–4.60)* 1.98 (1.37–2.87)**
Significant at the p < 0.05, *Significant at the p < 0.01. Values are presented as hazard ratio (95% confidence interval). CKD, chronic kidney disease; hsCRP, high-sensitive C reactive protein.

There was a total of 70 detailed ECG diagnoses in the study population. Twenty-two ECG diagnoses show a prevalence of more than 1%. Nine ECG diagnoses (atrial fibrillation, atrial premature complex, myocardial infarction (inferior), myocardial ischemia (lateral), QT interval (prolonged), right bundle branch block, sinus arrhythmia, sinus rhythm (bradycardia), and T wave (abnormal)) had a p-value less than 0.2 for univariate analysis. Multivariable analysis considering these nine ECG diagnoses proposed three ECG diagnoses (atrial fibrillation, atrial premature complex, QT interval (prolonged)) as the independent predictors for MACCE.

The net reclassification index and c-statistics were analyzed by adding ECG diagnosis to the SCORE risk assessment model to evaluate whether it improves predictive power for major cardiovascular events (Table 5). The new model combined with nine ECG diagnoses (c-statistics: 0.62; 95% CI 0.59–0.66) showed higher performance compared to the SCORE model (c-statistics: 0.59; 95% CI 0.56–0.63). The new model adopting ECG diagnoses significantly improved the net reclassification index (0.09; 95% CI 0.01–0.18).

Table 5

Model comparison of the SCORE model and SCORE model with ECG diagnosis added
	SCORE model	SCORE model + ECG diagnosis
C-statistics (95% CI)	0.59 (0.56–0.63)	0.62 (0.59–0.66)
NRI (95% CI)	Reference	0.09 (0.01–0.18)
NRI, net reclassification index. SCORE model : Sex + Systolic blood pressure + Current smoker + Total cholesterol. ECG diagnosis : Atrial fibrillation + Atrial premature complex + Myocardial infarction (inferior) + Myocardial ischemia (lateral) + QT interval (prolonged) + RBBB + Sinus arrhythmia + Sinus rhythm (bradycardia) + T wave (abnormal).

This study is the first long-term observational study to explore the prevalence and clinical impact of ECG abnormality on cerebrocardiovascular prognosis in Asian CKD patients. We found several clinical perspectives on ECG with respect to cerebrocardiovascular prognosis in CKD patients. First, the prevalence of major ECG abnormalities increases as CKD stage increases. In CKD patients, the most common ECG diagnosis corresponding to major ECG abnormalities was prolonged QT interval (7%). CKD patients with major ECG abnormalities had poor cerebrocardiovascular prognosis, in particular a higher incidence of MI.

Several surrogate markers, such as albumin-creatinine ratio (ACR), pulse wave velocity, and carotid ultrasound, have been proposed in predicting CVD in CKD patients(21). Of note, depending on the race, there are differences in the association of some surrogate markers with CVD, and their decision criteria are accordingly different. For example, ACR levels are higher and have a stronger association with CVD in Asians compared to Europeans(22). Previously, several studies demonstrated that ECG abnormalities are associated with poor cardiovascular prognosis in CKD patients(9, 10, 23). Although almost all these studies have been conducted in CKD patients in Caucasian and black people, our study expands these associations in Asian CKD patients.

Previous studies included patients taking medications that affect ECG results. For example, the use of 𝛽-blockers capable of increasing PR intervals was at least 15% up to 65% in the previous studies. The prevalence of CVD and psychotic disease also rises as renal function decreases in CKD patients, and since many drugs for CVD and psychotic disease affect the ECG results, it is difficult to confirm the prevalence of ECG abnormalities when excluding this confounding factor. Our study excluded those patients taking almost all medications affecting ECG results such as 𝛽-blockers, non-dihydropyridine calcium channel blockers, digoxin, ivabradine, type IA/IC/III antiarrhythmics, antipsychotics, antidepressants, antihistamines, macrolides and chloroquines. Our study confirmed the solid independent association of major ECG abnormalities with CKD patients and their cerebrocardiovascular prognosis.

Interestingly, Cox regression analysis of MACCE in our study suggested major ECG abnormality as an independent risk predictor, but not CKD stage. This suggests that ECG results are more important than CKD stage for the occurrence of MACCE. Among various ECG diagnoses, prolonged QT interval was the most common major ECG abnormality and was associated with CKD stage and CVD prognosis. This is consistent with previous reports that QT interval is associated with cardiovascular events and deaths(9, 23–26). QT interval reflects both conduction and repolarization of the heart and is affected by electrolyte imbalance as well as myocardial ischemic condition. Therefore, QT interval is likely to be associated with the occurrence of MI and sudden death, and prolonged QT interval is frequently observed in patients with sudden death and MI(27), further suggesting it as a risk predictor for obstructive coronary artery disease(28).

There are several limitations in our study. First, there is selection bias. We excluded CKD patients taking medications that can affect the ECG results. As mentioned earlier, CKD patients often have other comorbidities, so many of them take drugs that affect ECG. In our study, 20% of patients were excluded because of use of these drugs. Therefore, our results cannot be extended to CKD patients taking these drugs. Second, since different ECG abnormality criteria can be used for each study, we should be careful when comparing or applying our study to other studies. Some previous studies adopted different criteria for major and minor ECG abnormality(29–31). Although the Minnesota code classification, which was adopted in our study, cannot be considered the only standard for classification of ECG abnormalities, we applied the latest version of the Minnesota code classification. This is the first study to use the updated version. Third, only 280 (21.6%) patients with advanced CKD under eGFR 30 mL/min/1.73m² were included in our study. Similarly, the proportion of severe CKD patients was about 5–20% in previous studies(9, 10, 23)^,. In addition, considering that cardiovascular comorbidities become more common in severe CKD patients, the rate of drug use that affects ECG results will also be higher. Since these patients were excluded in this study, the number of severe CKD patients in this study was smaller. Therefore, it is limited in inferring the clinical impact of ECG abnormalities in patients with severe CKD.

In conclusion, major ECG abnormalities in Asian CKD patients increase the risk of cerebrocardiovascular events, especially MI. Further research is needed on more precise cerebrocardiovascular risk assessment and appropriate intervention strategies using ECG in the future.

CKD

Chronic kidney disease

ECG

Electrocardiogram

MACCE

Major adverse cerebrocardiovascular event

Myocardial infarction

CVD

Cardiovascular disease

LVH

Left ventricular hypertrophy

MDRD

Modification of Diet in Renal Disease

GFR

Glomerular filtration rate

LBBB

Left bundle branch block

RBBB

Right bundle branch block

LDL-C

Low-density lipoprotein-cholesterol

HDL-C

High-density lipoprotein-cholesterol

SCORE

Systemic Coronary Risk Evaluation

ANOVA

Analysis of Variance

hsCRP

High-sensitivity C-reactive protein

NRI

Net reclassification index

ACR

Albumin-creatinine ratio

Ethics approval and consent to participate

This study was complied with the Declaration of Helsinki. The study was approved by the Institutional Review Board of Korea University Anam Hospital (IRB No. 2021AN0450). The need for Informed Consent was waived by the the Institutional Review Board of Korea University Anam Hospital due to the retrospective nature of the study. All methods were performed in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

The data that support the findings of this study are not publicly available because Korea's domestic law prohibits export of personal health information overseas without prior informed consent, even if anonymized data but are available from the corresponding author upon reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

None.

Authors’ contributions

H.J., S.P. and S.K. designed the study. H.J., S.P. and Y.Y. planned the experiments and performed the analyses. H.J. and S.P. wrote the manuscript. Y.Y. prepared the figures and tables. H.J., J.C., J.P. and S.H. supervised the conceptualization and writing. H.J., S.P., Y.Y., C.Y., T.A., and D.L. contributed to the collection of the data, discussions, and interpretation of the data.

Jeyaruban A, Hoy W, Cameron A, Healy H, Wang Z, Zhang J, et al. Impact of cardiovascular events on mortality and progression of renal dysfunction in a Queensland CKD cohort. Nephrology (Carlton). 2020;25(11):839–44.
Wright RS, Reeder GS, Herzog CA, Albright RC, Williams BA, Dvorak DL, et al. Acute myocardial infarction and renal dysfunction: a high-risk combination. Ann Intern Med. 2002;137(7):563–70.
Best PJ, Lennon R, Ting HH, Bell MR, Rihal CS, Holmes DR, et al. The impact of renal insufficiency on clinical outcomes in patients undergoing percutaneous coronary interventions. J Am Coll Cardiol. 2002;39(7):1113–9.
Tonelli M, Karumanchi SA, Thadhani R. Epidemiology and Mechanisms of Uremia-Related Cardiovascular Disease. Circulation. 2016;133(5):518–36.
System USRD. 2020 USRDS Annual Data Report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2020.
Larsen CT, Dahlin J, Blackburn H, Scharling H, Appleyard M, Sigurd B, et al. Prevalence and prognosis of electrocardiographic left ventricular hypertrophy, ST segment depression and negative T-wave; the Copenhagen City Heart Study. Eur Heart J. 2002;23(4):315–24.
Greenland P, Xie X, Liu K, Colangelo L, Liao Y, Daviglus ML, et al. Impact of minor electrocardiographic ST-segment and/or T-wave abnormalities on cardiovascular mortality during long-term follow-up. Am J Cardiol. 2003;91(9):1068–74.
Sciarretta S, Pontremoli R, Rosei EA, Ambrosioni E, Costa V, Leonetti G, et al. Independent association of ECG abnormalities with microalbuminuria and renal damage in hypertensive patients without overt cardiovascular disease: data from Italy-Developing Education and awareness on MicroAlbuminuria in patients with hypertensive Disease study. J Hypertens. 2009;27(2):410–7.
Dobre M, Brateanu A, Rashidi A, Rahman M. Electrocardiogram abnormalities and cardiovascular mortality in elderly patients with CKD. Clin J Am Soc Nephrol. 2012;7(6):949–56.
Deo R, Shou H, Soliman EZ, Yang W, Arkin JM, Zhang X, et al. Electrocardiographic Measures and Prediction of Cardiovascular and Noncardiovascular Death in CKD. J Am Soc Nephrol. 2016;27(2):559–69.
Smulyan H. The Computerized ECG: Friend and Foe. Am J Med. 2019;132(2):153–60.
Willems JL, Abreu-Lima C, Arnaud P, van Bemmel JH, Brohet C, Degani R, et al. The diagnostic performance of computer programs for the interpretation of electrocardiograms. N Engl J Med. 1991;325(25):1767–73.
Lee D, de Keizer N, Lau F, Cornet R. Literature review of SNOMED CT use. J Am Med Inform Assoc. 2014;21(e1):e11-9.
Prineas RJ, Blackburn HW, Crow RS, Blackburn H, Crow RS. The Minnesota code manual of electrocardiographic findings: standards and procedures for measurement and classification. Boston: Wright-PSG; 1982.
Jorstad HT, Boekholdt SM, Wareham NJ, Khaw KT, Peters RJ. The Dutch SCORE-based risk charts seriously underestimate the risk of cardiovascular disease. Neth Heart J. 2017;25(3):173–80.
Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.
Authors/Task Force M, Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, et al. 2016 European Guidelines on cardiovascular disease prevention in clinical practice: The Sixth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts): Developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR). Eur J Prev Cardiol. 2016;23(11):NP1-NP96.
Hlatky MA, Greenland P, Arnett DK, Ballantyne CM, Criqui MH, Elkind MS, et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association. Circulation. 2009;119(17):2408–16.
Pencina MJ, D'Agostino RB, Sr., D'Agostino RB, Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–72; discussion 207 – 12.
Graversen P, Abildstrom SZ, Jespersen L, Borglykke A, Prescott E. Cardiovascular risk prediction: Can Systematic Coronary Risk Evaluation (SCORE) be improved by adding simple risk markers? Results from the Copenhagen City Heart Study. Eur J Prev Cardiol. 2016;23(14):1546–56.
Rubin MF, Rosas SE, Chirinos JA, Townsend RR. Surrogate markers of cardiovascular disease in CKD: what's under the hood? Am J Kidney Dis. 2011;57(3):488–97.
Eastwood SV, Chaturvedi N, Sattar N, Welsh PI, Hughes AD, Tillin T. Impact of Kidney Function on Cardiovascular Risk and Mortality: A Comparison of South Asian and European Cohorts. Am J Nephrol. 2019;50(6):425–33.
Kestenbaum B, Rudser KD, Shlipak MG, Fried LF, Newman AB, Katz R, et al. Kidney function, electrocardiographic findings, and cardiovascular events among older adults. Clin J Am Soc Nephrol. 2007;2(3):501–8.
Genovesi S, Rossi E, Nava M, Riva H, De Franceschi S, Fabbrini P, et al. A case series of chronic haemodialysis patients: mortality, sudden death, and QT interval. Europace. 2013;15(7):1025–33.
Flueckiger P, Pastan S, Goyal A, McClellan WW, Patzer RE. Associations of ECG interval prolongations with mortality among ESRD patients evaluated for renal transplantation. Ann Transplant. 2014;19:257–68.
Malik M, Huikuri H, Lombardi F, Schmidt G, e-Health/Digital Rhythm Study Group of the European Heart Rhythm A. The purpose of heart rate variability measurements. Clin Auton Res. 2017;27(3):139–40.
Puddu PE, Bourassa MG, Lesperance J, Helias J, Danchin N, Goulet C. Can the mode of death be predicted in patients with angiographically documented coronary artery disease? Clin Cardiol. 1983;6(8):384–95.
Cho DH, Choi J, Kim MN, Kim HD, Hong SJ, Yu CW, et al. Incremental value of QT interval for the prediction of obstructive coronary artery disease in patients with chest pain. Sci Rep. 2021;11(1):10513.
Denes P, Larson JC, Lloyd-Jones DM, Prineas RJ, Greenland P. Major and minor ECG abnormalities in asymptomatic women and risk of cardiovascular events and mortality. JAMA. 2007;297(9):978–85.
Soliman EZ, Prineas RJ, Roediger MP, Duprez DA, Boccara F, Boesecke C, et al. Prevalence and prognostic significance of ECG abnormalities in HIV-infected patients: results from the Strategies for Management of Antiretroviral Therapy study. J Electrocardiol. 2011;44(6):779–85.
Jimenez-Corona A, Nelson RG, Sievers ML, Knowler WC, Hanson RL, Bennett PH. Electrocardiographic abnormalities predict deaths from cardiovascular disease and ischemic heart disease in Pima Indians with type 2 diabetes. Am Heart J. 2006;151(5):1080–6.

No competing interests reported.

ECGCKDSupplementaryInformationfinal.docx

Download PDF

Version 1

posted

You are reading this latest preprint version

Prevalence and clinical impact of electrocardiographic abnormalities in patients with chronic kidney disease

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Study Design

Standardization of computerized ECG diagnosis

Definitions and study endpoint

Statistical analysis

Results

Baseline characteristics

Proportion of ECG abnormalities

ECG abnormality and MACCE

Multivariable Cox regression analysis

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1