Abstract
Chronic kidney disease (CKD) is associated with cardiac conduction defects and is a strong risk factor for heart failure. Complete left bundle branch block (cLBBB), a cardiac conduction abnormality, may have an unfavorable effect on ventricular mechanical synchrony and lead to the progression of heart failure. Once heart failure develops, it seems to act together with underlying CKD in a vicious circle. Therefore, this study aimed to explore the influence of CKD in patients with cLBBB by assessing the estimated glomerular filtration rate (eGFR). We examined a hospital-based sample of 416 adult patients with cLBBB from 2010 to 2013. The eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cox proportional hazard models were used to estimate the hazard ratio for all-cause mortality and cardiovascular mortality. A total of 416 adult patients with a mean age of 71 ± 13 years were enrolled. The median follow-up period was 3.6 years. After adjusting for clinical, electrocardiographic parameters, and medication use, cox regression analysis showed that total mortality was significantly associated with older age (Hazard Ratio (HR) = 1.03, 95% CI = 1.01–1.05, p = 0.002), presence of congestive heart failure (HR = 2.39, 95% CI = 1.63–3.49, p < 0.001), advanced CKD (HR = 2.48, 95% CI = 1.71–3.59, p < 0.001), higher HR (HR = 1.02, 95% CI = 1.01–1.03, p < 0.001) and without use of ACEI/ARB (HR = 0.59, 95% CI = 0.41–0.85, p = 0.005) were independent predictors of the total mortality. Multivariate Cox hazard regression analysis demonstrated that, in comparison to patients lacking cLBBB, the coexistence of CKD (eGFR < 60 mL/min/1.73 m2) among those with LBBB significantly heightened the risks of both total mortality (HR ratio of 5.01 vs. 2.40) and CV death (HR ratio of 61.78 vs. 14.41) even following adjustment for clinical covariates and ECG parameters. In summary, within patients exhibiting cLBBB, the presence of CKD serves as a significant risk factor for all-cause mortality.
Keywords: Complete left bundle branch block, Chronic kidney disease, Outcome
Subject terms: Cardiology, Nephrology
Introduction
Chronic kidney disease (CKD) is an increasing global health burden with an estimated prevalence of 13% in the adult population worldwide1. CKD is especially burdensome in Taiwan, with a growing prevalence and incidence2. Emerging evidence has shown a significant association between CKD and cardiac conduction defects in patients with advanced CKD, type 2 diabetes, or hypertension3–5. CKD is a strong risk factor for the incidence of heart failure through the cardiac remodeling process6. Complete left bundle branch block (cLBBB) is a cardiac conduction abnormality that appears to have minimal effects on cardiovascular (CV) morbidity and mortality in relatively healthy adults7. However, in the presence of underlying structural heart disease, cLBBB is an independent predictor of cardiovascular mortality8,9. The influence of CKD on conduction defects in patients with cLBBB has not been evaluated. Moreover, growing evidence has suggested that cLBBB may have an unfavorable effect on ventricular mechanical synchrony in some patients, leading to the progression of LV dysfunction through alterations in the pattern of ventricular activation (septum is activated earlier than the lateral wall)10. Once heart failure develops, it seems to act together with any underlying CKD in a vicious circle, in which each condition influences or exacerbates the other, leading to higher risks of hospitalization, morbidity, and death, as well as expensive health care costs. Unlike to a study from Mayo Clinic in the United States, our previous study found a low prevalence of reduced LV ejection fraction and cLBBB in Taiwan, highlighting the importance of risk stratification in patients with cLBBB11,12.
As far as we know, data on the effect of CKD in patients with cLBBB are scarce. Hence, this study aimed to evaluate the prognostic significance of CKD in patients with cLBBB using the estimated glomerular filtration rate (eGFR).
Methods
Study population
We conducted a retrospective cohort study by reviewing the electrocardiograms (ECGs) of adult patients who visited the National Taiwan University Hospital between January 2010 and December 2013. The study population included 434 patients > 20 years of age who had a cLBBB pattern on the ECG. cLBBB was defined based on the American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society recommendations13. Body mass index (BMI) was calculated as weight in kilograms (kg) per height in meters squared (m2). Patients were considered to have hypertension if their blood pressure was > 140/90 mmHg or if they were taking any anti-hypertensive drugs. Patients were diagnosed with diabetes if they had a fasting blood glucose level > 126 mg/dl or HbA1c level > 6.5, or if they were taking any oral hypoglycemic drugs or insulin via injection. Dyslipidemia was defined as an abnormal LDL level based on the 2017 Taiwan lipid guidelines for high-risk patients, or the use of any oral lipid-lowering drugs14. The presence of CAD in a patient was defined as a documented history of myocardial infarction, angina pectoris, or coronary revascularization procedures. Chronic obstructive pulmonary disease (COPD) was confirmed by reviewing hospital medical records, including diagnostic symptom patterns, results of lung function tests, and the use of inhaled bronchodilators. A history of congestive heart failure (CHF) was confirmed by reviewing hospital medical records, including diagnostic symptom patterns and diuretic use. A history of atrial fibrillation (AF) was confirmed by electrocardiography, including 24-h Holter monitoring results. The use of cardiovascular medications (antiplatelet agents, anticoagulant drugs,α-blockers, ß-blockers, calcium channel blockers, angiotensin-converting enzyme inhibitors (ACEI) and angiotensin-II receptor blockers (ARB)) was reviewed from patient medical records and charts. Patients with pacing rhythm and other non-LBBB ECG results, transient LBBB, with acute events such as acute coronary syndrome or acute renal failure were excluded. For contrasting the CKD’s prognostic significance in patients with cLBBB, controls without cLBBB were selected from the NTUH database using random matching at a 1:1 ratio according to age, gender, and date of baseline electrocardiogram were included. The follow-up period ended on December 31, 2016 or earlier if either death or follow-up loss occurred.
Electrocardiogram
For all patients, each standard 12-lead surface ECG was performed using a digital ECG recorder (MAC5000; GE Medical Systems, Milwaukee, WI, USA) at a sampling rate of 500 Hz. The definition of LBBB was native QRS duration ≥ 120 ms; broad R waves in leads I, aVL, V5, or V6; absent q waves in leads I, V5, and V6; R peak time > 60 ms in leads V5 and V6; normal waves in leads V1, V2, and V3; and small initial r waves in the aforementioned leads13.
Echocardiography
Baseline transthoracic echocardiographic data for enrolled patients within one year of the index ECG date were retrospectively traced and documented. Trained physicians or technicians utilized a Philips iE33 cardiovascular ultrasound machine (Philips Medical Systems, Andover, MA, USA) for image acquisition. Valve calcification was defined as mitral or aortic valve calcification. Because of the dyssynchronous contraction in patients with cLBBB, the LV volumes and the LVEF were calculated from the apical two- and four-chamber images using the biplane method of discs (modified Simpson’s rule). All echocardiographic measurements were acquired following the recommendations of the American Society of Echocardiography14. All images were taken using the Philips iE33 scanner (Philips Medical Systems, Andover, MA, USA).The study protocol was approved by the National Taiwan University Hospital Research Ethics Committee (study no. 202012282RINA) and waived the requirement of informed consent. All study methods were carried out in accordance with relevant guidelines and regulations.
Renal function
The glomerular filtration rate was estimated from calibrated serum creatinine values using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation15. The CKD-EPI equation, which uses the same four variables as the Modification of diet in renal disease (MDRD) equation16, has been shown to be more accurate than the MDRD study equation for estimating GFR in different populations. CKD staging was performed according to the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (KDOQL) guidelines17. Stages of CKD were defined as follows: stage 1 (eGFR ≥ 90 mL/min/1.73 m2); stage 2 (eGFR 60–89 mL/min/1.73 m2); stage 3 (eGFR 30–59 mL/min/1.73 m2); stage 4 (eGFR 15–29 mL/min/1.73 m2); and stage 5 (eGFR < 15 mL/min/1.73 m2)17. Advanced CKD was defined as an eGFR < 60 mL/min/1.73 m2. We also employed Cockcroft-Gault equations to estimate the glomerular filtration rate (GFR) as an additional method of validation for assessing the impact of CKD on both total mortality and cardiovascular mortality.
Outcome measurements
The primary endpoint was total mortality. All patients were followed up until loss to follow-up or death. The follow-up period ended on December 31, 2016. The definition of CV death included acute coronary syndrome, stroke, CHF, or sudden cardiac arrest.
Statistical analysis
Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA) and Stata version 14.1 (StataCorp LP). Normally distributed data are expressed as means ± standard deviations, data with a skewed distribution as medians and interquartile ranges, and categorical variables as frequencies and percentages. Comparisons among different patient groups were performed using nonparametric tests or the Mann–Whitney U test for continuous variables and the chi-square test for discrete variables to avoid multiple comparison errors. The Cox proportional hazard model was used to estimate the hazard ratio of all-cause mortality and CV mortality associated with CKD status, adjusted for potential confounders, including clinical characteristics, medication use, ECG parameters, and echocardiographic parameters.. The echocardiographic parameters was not put into the Cox regression models that’s because not all patients with cLBBB underwent echocardiography. Constructed Kaplan–Meier cumulative event rate curves were analyzed using the stratified log-rank test to compare the event rates among the different stage CKD groups.
Statistical significance was defined as a two-sided p < 0.05.
Results
Baseline clinical characteristics
The mean age of the 416 adult patients with cLBBB was 70.9 ± 12.7 years; 199 (47.8%) were men, 131 (31.5%) had a history of diabetes, and 100 (24%) were diagnosed with CHF. A total of 112 (26.9%), 160 (38.5%), 94 (22.6%), and 50 (12.0%) patients had eGFRCKD-EPI 90 mL/min/1.73 m2 (CKD stage 1), 89–60 mL/min/1.73 m2 (CKD stage 2), 59–30 mL/min/1.73 m2 (CKD stage 3), and < 30 mL/min/1.73 m2 (CKD stage 4–5), respectively. Compared to the cLBBB patients with stage 1 CKD, patients with advanced CKD stages had a greater prevalence of diabetes (19.6% vs. 50%; p < 0.001), CAD (18.8% vs. 48%; p < 0.001) and CHF (25.9% vs. 44%; p < 0.001). Of the 321 cLBBB patients who had echocardiographic examinations, patients who were diagnosed with a later stage of CKD had significantly lower LVEF (Table 1).
Table 1.
Clinical characteristics and ECG parameters for cLBBB adult patients in four stages of chronic kidney disease.
| CKD stages | All patients (N = 416) |
CKD stage 1 (N = 112) |
CKD stage 2 (N = 160) |
CKD stage 3 (N = 94) |
CKD stage 4–5 (N = 50) |
p-value |
|---|---|---|---|---|---|---|
| Age (SD), (years) | 70.9 (12.7) | 64.5 (14.8) | 71.3 (11.0) | 75.9 (11.2) | 74.3 (9.1) | < 0.001 |
| Male sex (%) | 199 (47.8) | 65 (58.0) | 71 (44.4) | 38 (40.4) | 25 (50.0) | 0.06 |
| Body Mass Index (SD), (kg/m2) | 24.4 (4.1) | 24.4 (4.7) | 24.8 (4.0) | 24.3 (3.7) | 23.6 (3.9) | 0.41 |
| Hypertension (%) | 290 (69.7) | 72 (64.3) | 106 (66.3) | 77 (81.9) | 35 (70.0) | 0.023 |
| Diabetes (%) | 131 (31.5) | 22 (19.6) | 42 (26.3) | 42 (44.7) | 25 (50.0) | < 0.001 |
| Dyslipidemia (%) | 151 (36.3) | 40 (35.7) | 57 (35.6) | 40 (42.6) | 14 (28.0) | 0.38 |
| Coronary artery disease (%) | 115 (27.6) | 21 (18.8) | 36 (22.5) | 34 (36.2) | 24 (48.0) | < 0.001 |
| Congestive heart failure (%) | 100 (24.0) | 29 (25.9) | 28 (17.5) | 21 (22.3) | 22 (44.0) | 0.003 |
| Atrial fibrillation (%) | 70 (16.8) | 15 (13.4) | 25 (15.6) | 19 (20.2) | 11 (22.0) | 0.40 |
| Biomarker results | ||||||
| eGFR (SD), (mL/min/1.73 m2) | 71.9 (36.4) | 112.8 (33.8) | 75.3 (8.4) | 47.1 (9.0) | 16.3 (8.6) | < 0.001 |
| Na (mmol/L) | 137.9 (4.5) | 138.1 (4.2) | 138.2 (4.1) | 137.3 (5.2) | 137.8 (4.9) | 0.6 |
| K (mmol/L) | 4.3 (0.5) | 4.2 (0.5) | 4.2 (0.5) | 4.6 (0.6) | 4.4 (0.6) | < 0.001 |
| Medications | ||||||
| Antiplatelet agents (%) | 170 (40.9) | 35 (31.3) | 74 (46.3) | 37 (39.4) | 24 (48.0) | 0.06 |
| Anticoagulant (%) | 47 (11.3) | 15 (13.4) | 16 (10.0) | 11 (11.7) | 5 (10.0) | 0.84 |
| Alpha-blocker (%) | 17 (4.1) | 3 (2.7) | 7 (4.4) | 5 (5.3) | 2 (4.0) | 0.76 |
| Beta-blocker (%) | 161 (38.8) | 47 (42.3) | 54 (33.8) | 46 (48.9) | 14 (28.0) | 0.033 |
| Calcium channel blocker (%) | 133 (32.0) | 32 (28.6) | 50 (31.3) | 37 (39.4) | 14 (28.0) | 0.35 |
| ACEI/ARB (%) | 213 (51.2) | 54 (48.2) | 88 (55.0) | 55 (58.5) | 16 (32.0) | 0.013 |
| Electrocardiographic parameters | ||||||
| Heart rate (SD), min | 78.7 (18.7) | 79.8 (18.8) | 75.2 (16.8) | 79.3 (20.1) | 86.3 (19.8) | < 0.001 |
| PR interval (SD), ms | 181.2 (38.3) | 181.9 (38.1) | 178.6 (32.7) | 184.6 (46.7) | 181.5 (39.7) | 0.85 |
| QRS duration (SD), ms | 149.1 (14.6) | 149.4 (15.6) | 148.4 (14.9) | 150.3 (13.6) | 148.4 (13.2) | 0.73 |
| QT interval (SD), ms | 438.4 (52.4) | 430.7 (54.3) | 443.0 (41.3) | 446.2 (53.0) | 426.6 (72.5) | 0.027 |
| QTc, (SD), ms | 495.1 (35.8) | 491.1 (35.9) | 490.1 (33.6) | 502.1 (32.1) | 506.9 (45.3) | 0.002 |
| CKD stages | All patients (N = 416) |
CKD stage 1 (N = 112) |
CKD stage 2 (N = 160) |
CKD stage 3 (N = 94) |
CKD stage 4–5 (N = 50) |
p-value |
|---|---|---|---|---|---|---|
| Echocardiographic parameters | ||||||
| N = 321 | N = 86 | N = 113 | N = 80 | N = 42 | ||
| LV end-systolic diameter (SD), mm | 38.1 (11.4) | 38.5 (12.2) | 37.3 (11.6) | 37.5 (10.5) | 40.8 (10.8) | 0.26 |
| Valve calcification (%) | 77 (22.8) | 19 (20.9) | 15 (12.4) | 22 (26.8) | 21 (47.7) | < 0.001 |
| LV ejection fraction (%) | 51.6 (17.4) | 53.0 (18.1) | 52.8 (16.5) | 51.2 (17.7) | 44.5 (16.7) | 0.04 |
ACEI angiotension-converting enzyme inhibitor, ARB angiotensin receptor blocker, cLBBB complete left bundle branch block, CKD chronic kidney disease, eGFR estimated glomerular filtration rate, LV left ventricular.
Using an eGFR of 60 mL/min as a cut-off value, patients in the advanced CKD group were significantly older in age (75.3 vs. 68.5; p = 0.001), and more of them had a history of hypertension (77.8% vs. 65.4%; p = 0.01), diabetes mellitus (46.5% vs. 23.5%; p = 0.001) and CAD (40.3% vs. 21.1%; p = 0.001) than those in the non-advanced CKD group. In addition, a higher heart rate (HR) (81.7 vs. 77.1; p = 0.011), larger QTc (500.9 vs. 488.6; p < 0.001), greater degree of valvular calcification (20.9% vs. 47.7%; p < 0.001), and lower LVEF (51.6% vs. 44.5%; p < 0.001) were significantly associated with cLBBB patients with advanced CKD (Table 2).
Table 2.
Clinical characteristics, electrocardiographic parameters, and echocardiographic variables for cLBBB patients with or without advanced chronic kidney disease.
| eGFR ≥ 60 mL/min (N = 272) |
eGFR < 60 mL/min (N = 144) |
p-value | |
|---|---|---|---|
| Age (SD), (years) | 68.5 (13.2) | 75.3 (10.5) | 0.001 |
| Male sex (%) | 136 (50.0) | 63 (43.8) | 0.26 |
| Body Mass Index (SD), (kg/m2) | 24.6 (4.3) | 24.0 (3.7) | 0.38 |
| Hypertension (%) | 178 (65.4) | 112 (77.8) | 0.01 |
| Diabetes mellitus (%) | 64 (23.5) | 67 (46.5) | 0.001 |
| Dyslipidemia (%) | 93 (34.2) | 50 (34.7) | 0.91 |
| Coronary artery disease (%) | 57 (21.0) | 58 (40.3) | 0.001 |
| Congestive heart failure (%) | 57 (21.0) | 43 (29.9) | 0.05 |
| Atrial fibrillation (%) | 40 (14.7) | 30 (20.8) | 0.13 |
| Biomarker results | |||
| eGFR (SD), (mL/min/1.73 m2) | 90.8 (29.2) | 36.4 (17.1) | 0.001 |
| Na (mmol/L) | 138.2 (4.1) | 137.5 (5.1) | 0.19 |
| K (mmol/L) | 4.2 (0.5) | 4.5 (0.6) | < 0.001 |
| Medications | |||
| Antiplatelet agents (%) | 109 (40.1) | 61 (42.4) | 0.68 |
| Anticoagulant (%) | 31 (11.4) | 16 (11.1) | 1.00 |
| Alpha-blocker (%) | 10 (3.7) | 7 (4.9) | 0.61 |
| Beta-blocker (%) | 101 (37.3) | 60 (41.7) | 0.40 |
| Calcium channel blocker (%) | 82 (30.1) | 51 (35.4) | 0.32 |
| ACEI/ARB (%) | 142 (52.2) | 71 (49.3) | 0.61 |
| ECG parameters | |||
| Heart rate (SD), (beats/min) | 77.1 (17.7) | 81.7 (20.2) | 0.011 |
| PR interval (SD), (ms) | 179.9 (34.9) | 183.6 (44.4) | 0.64 |
| QRS duration (SD), (ms) | 148.8 (15.2) | 149.6 (13.5) | 0.46 |
| QT interval (SD), (ms) | 437.9 (47.3) | 439.4 (60.9) | 0.72 |
| QTc (SD), (ms) | 488.6 (44.4) | 500.9 (53.4) | < 0.001 |
| Echocardiographic parameters | |||
| LV end diastolic diameter (SD), (mm) | 52.3 (10.0) | 52.2 (8.5) | 0.64 |
| LV end systolic diameter (SD), (mm) | 37.8 (11.9) | 38.7 (10.7) | 0.30 |
| Valve calcification (%) | 34 (16.0) | 43 (34.1) | 0.001 |
| LV ejection fraction (%) | 53.2 (17.1) | 48.9 (17.6) | 0.039 |
ACEI angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, cLBBB complete left bundle branch block, eGFR estimated glomerular filtration rate, LV left ventricular.
Long-term survival in cLBBB patients with/without advanced CKD stage
After a mean follow-up of 3.6 years, 14 patients implanted pacemaker for progressed AV block, 18 patients implanted cardiac resynchronization therapy for heart failure, 66 (15.9%) patients died from cardiovascular causes, and 65 (15.6%) patients experienced non-cardiovascular death. The Kaplan–Meier analysis showed that survival was significantly affected by the stage of CKD. Patients with CKD stages 4–5 had the worst survival outcome (p < 0.001) (Fig. 1).
Figure 1.
Kaplan–Meier survival curves for survival (in years) in cLBBB patients with different stages of CKD.
Multivariate Cox hazard regression analysis revealed that older age (hazard ratio (HR) = 1.03, 95% CI = 1.01–1.05, p = 0.002), history of HF (HR = 2.39, 95% CI = 1.63–3.49, p < 0.001), CKD (HR = 2.48, 95% CI = 1.71–3.59, p < 0.001), higher HR (HR = 1.02, 95% CI = 1.01–1.03, p < 0.001) and without use of ACEi/ARB (HR = 0.59, 95% CI = 0.41–0.85, p = 0.005) were independent predictors of the total mortality after adjusting for significant covariates, such as age, sex, history of diabetes mellitus, dyslipidemia, CAD, CHF, AF, and CKD; medications use such as ACEI/ARB, and electrocardiographic parameters including heart rate and QRS duration, and QTc. (Table 3). After adjusting for significant clinical characteristics and ECG parameters used in primary outcome analysis, CV mortality was significantly associated with a history of CHF (HR = 3.92; 95% CI = 2.34–6.59; p < 0.001), advanced CKD (HR = 2.41; 95% CI = 1.42–4.10; p = 0.001),higher heart rate (HR = 1.02; 95% CI = 1.00–1.03; p = 0.016), and larger QRS duration (HR = 1.03; 95% CI = 1.01–1.04; p = 0.006) (Table 4). We also used Cockcroft-Gault equations to estimate GFR and defined advanced CKD as eGFR < 60 mL/min. Univariate and multivariate Cox proportional hazard ratios, adjusting for significant parameters from univariate analysis, showed advanced CKD as an independent predictor of total mortality (HR = 3.23, 95% CI = 2.29–5.52, p < 0.001) and cardiovascular mortality (HR = 1.92, 95% CI = 1.02–3.63, p = 0.03, after adjusting significant covariates. (data not shown in Table 3 and 4).
Table 3.
Univariate and multivariate cox proportional hazard ratio of total mortality after adjusting for clinical characteristics, medication use, and parameters of electrocardiogram.
| Univariate | Multivariate | |||
|---|---|---|---|---|
| HR (95% confidence interval) | p value | Adjusted HR (95% confidence interval) | P value | |
| Age, year | 1.04 (1.02–1.06) | p < 0.001 | 1.03 (1.01–1.05) | 0.002 |
| Male sex | 1.35 (0.96–1.90) | 0.09 | ||
| Hypertension | 0.88 (0.61–1.27) | 0.49 | ||
| Diabetes mellitus | 1.49 (1.05–2.12) | 0.026 | 1.10 (0.76–1.60) | 0.61 |
| Dyslipidemia | 0.55 (0.37–0.82) | 0.003 | 0.68 (0.48–1.01) | 0.05 |
| Coronary artery disease | 1.78 (1.25–2.53) | 0.0014 | 1.38 (0.95–2.02) | 0.09 |
| Congestive heart failure | 3.29 (2.33–4.66) | p < 0.001 | 2.39 (1.63–3.49) | p < 0.001 |
| Atrial fibrillation | 1.69 (1.13–2.53) | 0.011 | 0.86 (0.55–1.35) | 0.51 |
| CKD | ||||
| CKD-EPI equation | ||||
| eGFR < 60 | 3.15 (2.23–4.45) | p < 0.001 | 2.48 (1.71–3.59) | p < 0.001 |
| eGFR > = 60 | 1.00 (reference) | 1.00 (reference) | ||
| Cockcroft-Gault equations | ||||
| eGFR < 60 | 3.31 (2.33–4.69) | p < 0.001 | ||
| eGFR > = 60 | 1.00 (reference) | |||
| Medication | ||||
| Antiplatelet agents | 1.28 (0.91–1.81) | 0.16 | ||
| Anticoagulant | 0.88 (0.51–1.54) | 0.66 | ||
| Alpha-blocker | 0.91 (0.37–2.21) | 0.83 | ||
| CCB | 0.71 (0.48–1.04) | 0.07 | ||
| ACEI/ARB | 0.58 (0.41–0.82) | 0.002 | 0.59 (0.41–0.85) | 0.005 |
| β-blockers | 0.75 (0.53–1.08) | 0.12 | ||
| Electrocardiographic parameters | ||||
| Heart rate, beats/min | 1.02 (1.02–1.03) | p < 0.001 | 1.02 (1.01–1.03) | p < 0.001 |
| QRS duration, ms | 1.01 (1.00–1.02) | 0.036 | 1.01 (0.99–1.02) | 0.49 |
| QT interval, ms | 1.00 (0.99–1.00) | 0.003 | ||
| QTc, ms | 1.00 (1.00–1.01) | 0.12 | 1.00 (0.99–1.00) | 0.79 |
| LVEF,% | 0.98 (0.97–0.99) | p < 0.001 | NA | |
| Valve calcification | 2.67 (1.82–3.92) | p < 0.001 | NA | |
ACEI angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, CCB calcium channel blocker, cLBBB complete left bundle branch block, eGFR estimated glomerular filtration rate, LVEF LV ejection fraction.
Significant values are in bold.
Table 4.
Univariate and multivariate cox proportional hazard ratio of cardiovascular mortality after adjusting for clinical characteristics, medication use, and parameters of electrocardiogram.
| Univariate | Multivariate | |||
|---|---|---|---|---|
| HR (95% confidence interval) | p value | Adjusted HR (95% confidence interval) | P value | |
| Age, year | 1.03 (1.00–1.05) | 0.022 | ||
| Male sex | 1.42 (0.88–2.31) | 0.16 | ||
| Hypertension | 0.95 (0.56–1.61) | 0.85 | ||
| Diabetes mellitus | 1.85 (1.14–3.01) | 0.013 | ||
| Dyslipidemia | 0.59 (0.34–1.02) | 0.057 | ||
| Coronary artery disease | 2.41 (1.48–3.91) | p < 0.001 | 1.52 (0.90–2.54) | 0.16 |
| Congestive heart failure | 6.53 (3.97–10.73) | p < 0.001 | 3.92 (2.34–6.59) | p < 0.001 |
| Atrial fibrillation | 2.37 (1.40–4.00) | 0.0013 | 1.23 (0.70–2.18) | 0.47 |
| CKD-EPI equation | ||||
| eGFR < 60 | 3.09 (1.90–5.03) | p < 0.001 | 2.41 (1.42–4.10) | 0.001 |
| eGFR > = 60 | 1.00 (reference) | 1.00 (reference) | ||
| Cockcroft-Gault equations | ||||
| eGFR < 60 | 2.52 (1.55–4.11) | p < 0.001 | ||
| eGFR > = 60 | 1.00 (reference) | |||
| Medication | ||||
| Antiplatelet agents | 1.34 (0.83–2.17) | 0.24 | ||
| Anticoagulant | 1.62 (0.87–3.04) | 0.13 | ||
| Alpha-blocker | 1.47 (0.53–4.04) | 0.46 | ||
| Calcium channel blocker | 0.56 (0.32–1.00) | 0.049 | ||
| ACEI/ARB | 0.67 (0.41–1.09) | 0.11 | ||
| β-blockers | 1.20 (0.74–1.94) | 0.47 | ||
| Electrocardiographic parameters | ||||
| Heart rate | 1.03 (1.02–1.04) | p < 0.001 | 1.02 (1.00–1.03) | 0.016 |
| QRS duration | 1.03 (1.02–1.05) | p < 0.001 | 1.03 (1.01–1.04) | 0.006 |
| QT interval | 1.00 (0.99–1.00) | 0.041 | ||
| QTc | 1.01 (1.00–1.02) | 0.004 | 1.00 (0.99–1.01) | 0.87 |
| LVEF | 0.96 (0.95–0.98) | p < 0.001 | NA | |
| Valve calcification | 2.30 (1.36–3.90) | 0.002 | NA | |
Adjusted for age, gender, hypertension, diabetes mellitus, dyslipidemia, coronary artery disease, congestive heart failure, Af, chronic kidney disease, medication use such as aspirin, ACEI/ARB and β-blockers and ECG parameters.
ACEI angiotensin-converting enzyme inhibitors, ARB angiotensin receptor blocker, cLBBB complete left bundle branch block, CKD chronic kidney disease, eGFR estimated glomerular filtration rate, HR hazard ratio, LVEF LV ejection fraction.
Significant values are in bold.
Risk stratification by advanced CKD for total mortality in cLBBB
The presence of advanced CKD could further stratify the total mortality risk associated with a QRS duration > 150 ms or history of CHF in cLBBB patients (Fig. 2A,B). The cLBBB patients without a QRS duration > 150 ms but with advanced CKD carried more risk of mortality than those without advanced CKD but with a QRS duration > 150 ms. Also, patients with advanced CKD had the worst prognosis (p < 0.001) in patients with a history of CHF. (Fig. 2A,B).
Figure 2.
CKD could further stratify the risk for cLBBB patients. (A) Patients with QRS duration > 150 ms and advanced CKD had the worst prognosis (p < 0.001). (B) Patients with CHF and advanced CKD had the worst prognosis (p < 0.001).
For contrasting the CKD’s prognostic significance in patients with cLBBB, 416 control patients without cLBBB were included. by1:1ratio random matching according to age, gender, and date of baseline electrocardiogram (Supplementary Table 1). Compared to the non-LBBB patients, patients with cLBBB had a greater prevalence of HTN (69.7% vs. 56.5%; p = 0.001), diabetes (36.3% vs. 26.9%; p = 0.045), hyperlipidemia (36.6% vs. 26.9%; p = 0.005). Also, patients with cLBBB had a larger PR interval, QRS duration, QT interval and QTc (all p = 0.001). Multivariate Cox hazard regression analysis revealed that comparing to patients without cLBBB, the presence of CKD (eGFR < 60 mL/min/1.73 m2) in patients with LBBB had more significant risk of total mortality (HR ratio 5.01 vs. 2.40) and CV death (HR ration 61.78 vs. 14.41) after adjusting clinical covariates, such as age, sex, history of diabetes mellitus, dyslipidmia, CAD, CHF, AF, and ECG parameters like HR, PR interval, QRS duration, QT interval and QTc (Table 5).
Table 5.
Multivariate cox proportional hazard ratio of total mortality and cardiovascular mortality among the presence of cLBBB and advanced CKD after adjusting for clinical characteristics, and parameters of electrocardiogram.
| Total Mortality | CV mortality | |||
|---|---|---|---|---|
| Adjusted HR 95% CI | p value | Adjusted HR 95% CI | p value | |
| cLBBB = 1 CKD(+) | 5.01 (3.01–8.35) | < .001 | 61.78 (8.04–474.86) | < .001 |
| cLBBB = 0 CKD(+) | 2.40 (1.37–4.22) | 0.002 | 14.41 (1.66–125.33) | 0.016 |
| cLBBB = 1 CKD(−) | 2.11 (1.26–3.54) | 0.005 | 24.55 (3.20–188.45) | 0.002 |
| cLBBB = 0 CKD(−) | 1 (reference) | 1 (reference) | ||
Adjusted by age, sex, hypertension, diabetes, dyslipidemia, coronary artery disease, congestive heart failure, atrial fibrillation, heart rate, QRS duration, and QT interval.
cLBBB complete left bundle branch block, CKD, chronic kidney disease, CV cardiovascular, HR hazard ratio.
Discussion
CKD is proven to be a risk factor for all-cause and CV related mortality. The risk increased as the renal function deteriorated18–20. In the present study, we found that age, a history of CHF, advanced CKD, which was determined by the eGFR, no use of ACEI/ARB, were significant prognostic factors in all-cause mortality in patients with cLBBB. Furthermore, we observed a notably greater impact of CKD on both total mortality and cardiovascular mortality in patients with cLBBB compared to those without cLBBB.
Several studies have mentioned that the presence of CKD is associated with conduction abnormalities. Wong et al.5 found that in 50 CKD patients who had an implantable cardiac device while undergoing hemodialysis, the risk of sudden cardiac death was attributable to severe bradycardia and asystole but not ventricular tachyarrhythmia during a long interdialytic period. In a cross-sectional study of nearly 4000 hypertensive patients without overt cardiovascular disease, Sciarretta et al.3 reported that CKD was significantly associated with ECG abnormalities such as intraventricular conduction defects, ventricular repolarization alterations, and left-axis deviation, independent of traditional cardiovascular risk factors. Our data further demonstrated the prognostic influence of CKD in patients with cLBBB.
The possible mechanism of the increased risk associated with advanced CKD in patients with cLBBB may be multifactorial. First, valvular calcification has been reported to be associated with conduction block21, and patients with chronic renal failure had a greater likelihood of developing valvular calcification, which is likely the result of an altered calcium and phosphate metabolism with or without secondary hyperparathyroidism22. As the conduction block deteriorated in patients with cLBBB, it might have caused a serious heart block. Our data not only supported an increase in valvular calcification in CKD patients, but it also demonstrated that CKD and valvular calcification were associated with a higher mortality in patients with cLBBB. Second, CKD is well-documented to induce left ventricular (LV) hypertrophy and cardiac remodeling through multifactorial and interconnected mechanisms. CKD may lead to disruptions in fluid and electrolyte balance, activation of the renin–angiotensin–aldosterone system (RAAS), and accumulation of uremic toxins. These factors contribute to LVH by promoting hypertension, volume overload, increased vascular resistance, and direct myocardial damage23. Moreover, cLBBB has a negative impact on LV mechanical synchrony and may have deleterious effects on LV systolic function. As heart failure develops, CKD tends to accompany it, and they appear to act together in a vicious circle in which each condition affects or exacerbates the other, and a disease to one organ can produce a progressive dysfunction through neurohormonal, hemodynamic, and other modulating processes24. Third, the kidneys play a pivotal role in maintaining and regulating electrolyte homeostasis. Electrolyte disorders, such as abnormalities in potassium, calcium, and magnesium levels, are prevalent in CKD. These disturbances can significantly impact cardiac conduction, potentially resulting in cardiac arrhythmias25.
Given that CKD and cLBBB are recognized as notable risk factors for the onset of HF and ischemic heart disease, the association between β-blocker utilization and decreased morbidity and mortality in these populations is well-established. As a result, the prevalence of β-blocker usage is notable, reaching up to 38% among our patient group (with an average age of 70.9) presenting with cLBBB.
A longer QRS duration was an important prognostic factor, especially for cardiovascular mortality in patients with heart failure and cLBBB26. Our data demonstrated similar findings that QRS duration > 150 ms and history of CHF are strong prognostic factors for CV mortality in patients with cLBBB. Moreover, the presence of advanced CKD could further stratify the total mortality risk associated with CHF and a QRS duration > 150 ms in cLBBB patients.
This study showed that ACEI or ARB therapy is associated with lower risks of total mortality and CV mortality in patients with cLBBB. The pathogenesis is complex and multifactorial. First, Dewland et al.27 found that ACEI reduced the incident of conduction system disease from a secondary analysis of a randomized clinical trial. Use of ACEI may slow the progression of conduction abnormality. Seconds, the negative impact on mechanical synchrony caused by cLBBB may lead to HF10. ACEI/ARB therapy has shown to decrease the mortality of HF through attenuating ventricular remodeling and renin‐angiotensin‐aldosterone system inhibition28. CKD provides significant risk stratification in patients with cLBBB and use of ACEIs or ARBs has been shown to delay the progression of CKD29.
There are some limitations to this study. The quantitative assessment of proteinuria to evaluate the severity of kidney damage was incomplete because of the retrospective design. Previous studies proved that both the eGFR and proteinuria are pivotal for the assessment of cardiovascular risks, including mortality and cardiovascular events30. Additionally, the number of cases included in this study was relatively small, probably due to the low prevalence of cLBBB in Taiwan; this could lead to recruitment bias for the enrolled patients12. Despite these potential limitations, the study may be regarded as generating a hypothesis that can be verified in future large-scale studies.
Conclusion
In patients with cLBBB, CKD provides higher significant risk for all-cause mortality and the use of ACEI or ARB was associated with lower risk of total mortality.
Supplementary Information
Acknowledgements
The authors also thank Wiley’s Editing Service for English language editing.
Author contributions
H.C.H., Y.B.L. and K.L.C. contributed to study conception and design. H.C.H. and C.K.C. contributed to the acquisition of the data. H.C.H., C.K.C., C.H.H. and K.L.C. contributed to the analysis and/or interpretation of data. All authors contributed to the drafting or critical revision of the article for important intellectual content. All authors gave final approval of the version of the article to be published.
Data availability
The dataset used and analyzed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
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Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-68826-5.
References
- 1.Mills, K. T. et al. A systematic analysis of worldwide population-based data on the global burden of chronic kidney disease in 2010. Kidney Int.88, 950–957 (2015). 10.1038/ki.2015.230 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Tsai, M. H. et al. Incidence, prevalence, and duration of chronic kidney disease in Taiwan: Results from a community-based screening program of 106,094 individuals. Nephron140, 175–184 (2018). 10.1159/000491708 [DOI] [PubMed] [Google Scholar]
- 3.Sciarretta, S. 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.27, 410–417 (2009). 10.1097/HJH.0b013e32831bc764 [DOI] [PubMed] [Google Scholar]
- 4.Mantovani, A. et al. Association between decreasing estimated glomerular filtration rate and risk of cardiac conduction defects in patients with type 2 diabetes. Diabetes Metab.44, 473–481 (2018). 10.1016/j.diabet.2018.08.007 [DOI] [PubMed] [Google Scholar]
- 5.Wong, M. G. et al. Temporal distribution of arrhythmic events in chronic kidney disease: Highest incidence in the long interdialytic period. Heart Rhythm12, 2047–2055 (2015). 10.1016/j.hrthm.2015.06.033 [DOI] [PubMed] [Google Scholar]
- 6.Kottgen, A. et al. Reduced kidney function as a risk factor for incident heart failure: The atherosclerosis risk in communities (ARIC) study. J. Am. Soc. Nephrol.18, 1307–1315 (2007). 10.1681/ASN.2006101159 [DOI] [PubMed] [Google Scholar]
- 7.Imanishi, R. et al. Prognostic significance of incident complete left bundle branch block observed over a 40-year period. Am. J. Cardiol.98, 644–648 (2006). 10.1016/j.amjcard.2006.03.044 [DOI] [PubMed] [Google Scholar]
- 8.Sumner, G. et al. The prognostic significance of bundle branch block in high-risk chronic stable vascular disease patients: A report from the HOPE trial. J. Cardiovasc. Electrophysiol.20, 781–787 (2009). 10.1111/j.1540-8167.2009.01440.x [DOI] [PubMed] [Google Scholar]
- 9.Nakamura, Y. et al. Prognostic values of bundle branch blocks for cardiovascular mortality in Japanese (24 year follow-up of NIPPON DATA80). J. Electrocardiol.46, 360–365 (2013). 10.1016/j.jelectrocard.2013.03.009 [DOI] [PubMed] [Google Scholar]
- 10.Supariwala, A. A. et al. Prevalence and long-term prognosis of patients with complete bundle branch block (right or left bundle branch) with normal left ventricular ejection fraction referred for stress echocardiography. Echocardiography32, 483–489 (2015). 10.1111/echo.12680 [DOI] [PubMed] [Google Scholar]
- 11.Witt, C. M. et al. Outcomes with left bundle branch block and mildly to moderately reduced left ventricular function. JACC Heart Fail.4, 897–903 (2016). 10.1016/j.jchf.2016.07.002 [DOI] [PubMed] [Google Scholar]
- 12.Huang, H. C., Chien, K. L., Lin, H. J. & Liu, Y. B. The prevalence and association of patients with impaired left ventricular ejection fraction and complete left bundle-branch block in Taiwan. J. Formos. Med. Assoc.118, 686–691 (2019). 10.1016/j.jfma.2018.08.009 [DOI] [PubMed] [Google Scholar]
- 13.Surawicz, B, et al. AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part III: Intraventricular conduction disturbances: A scientific statement from the American Heart Association Electrocardiography and Arrhythmias Committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society. Endorsed by the International Society for Computerized Electrocardiology. J. Am. Coll. Cardiol.53, 976–81 (2009). [DOI] [PubMed]
- 14.Mitchell, C. et al. Guidelines for performing a comprehensive transthoracic echocardiographic examination in adults: Recommendations from the American Society of Echocardiography. J. Am. Soc. Echocardiogr.32, 1–64. 10.1016/j.echo.2018.06.004 (2019). 10.1016/j.echo.2018.06.004 [DOI] [PubMed] [Google Scholar]
- 15.Levey, A. S. et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med.150, 604–612 (2009). 10.7326/0003-4819-150-9-200905050-00006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Levey, A. S. et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of diet in renal disease study group. Ann. Intern. Med.130, 461–470 (1999). 10.7326/0003-4819-130-6-199903160-00002 [DOI] [PubMed] [Google Scholar]
- 17.National Kidney Foundation. K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am. J. Kidney Dis39(2 Supp 1), S1-266 (2002). [PubMed] [Google Scholar]
- 18.Shamim, W. et al. Intraventricular conduction delay: A prognostic marker in chronic heart failure. Int. J. Cardiol.70, 171–178 (1999). 10.1016/S0167-5273(99)00077-7 [DOI] [PubMed] [Google Scholar]
- 19.Lin, C. M., Yang, M. C., Hwang, S. J. & Sung, J. M. Progression of stages 3b–5 chronic kidney diseased—preliminary results of Taiwan national pre-ESRD disease management program in southern Taiwan. J. Formos. Med. Assoc.112, 773–782 (2013). 10.1016/j.jfma.2013.10.021 [DOI] [PubMed] [Google Scholar]
- 20.Roderick, P. J. et al. CKD and mortality risk in older people: A community-based population study in the United Kingdom. Am. J. Kidney Dis.53(6), 950–960 (2009). 10.1053/j.ajkd.2008.12.036 [DOI] [PubMed] [Google Scholar]
- 21.Shurmur, S. W. et al. Cardiac conduction defects associated with aortic and mitral valve calcification in dialysis patients. Ren. Fail.12, 103–107 (1990). 10.3109/08860229009087126 [DOI] [PubMed] [Google Scholar]
- 22.Umana, E., Ahmed, W. & Alpert, M. A. Valvular and perivalvular abnormalities in end-stage renal disease. Am. J. Med. Sci.325, 237–242 (2003). 10.1097/00000441-200304000-00010 [DOI] [PubMed] [Google Scholar]
- 23.Izumaru, K. et al. Reduced estimated GFR and cardiac remodeling: A population-based autopsy study. Am. J. Kidney Dis.74, 373–381 (2019). 10.1053/j.ajkd.2019.02.013 [DOI] [PubMed] [Google Scholar]
- 24.Szymanski, M. K., de Boer, R. A., Navis, G. J., van Gilst, W. H. & Hillege, H. L. Animal models of cardiorenal syndrome: A review. Heart Fail. Rev.17, 411–420 (2012). 10.1007/s10741-011-9279-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Zaza, A. Serum potassium and arrhythmias. Europace11(4), 421–422. 10.1093/europace/eup005 (2009). 10.1093/europace/eup005 [DOI] [PubMed] [Google Scholar]
- 26.Cazeau, S. et al. Effects of multisite biventricular pacing in patients with heart failure and intraventricular conduction delay. N. Engl. J. Med.344, 873–880 (2001). 10.1056/NEJM200103223441202 [DOI] [PubMed] [Google Scholar]
- 27.Dewland, T. A. et al. Effect of the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT) on conduction system disease. JAMA Intern. Med.176, 1085–1092 (2016). 10.1001/jamainternmed.2016.2502 [DOI] [PubMed] [Google Scholar]
- 28.Pfeffer, M. A. et al. Valsartan, captopril, or both in myocardial infarction complicated by heart failure, left ventricular dysfunction, or both. N. Engl. J. Med.349, 1893–1906 (2003). 10.1056/NEJMoa032292 [DOI] [PubMed] [Google Scholar]
- 29.Ku, E., McCulloch, C. E., Vittinghoff, E., Lin, F. & Johansen, K. L. Use of antihypertensive agents and association with risk of adverse outcomes in chronic kidney disease: Focus on angiotensin-converting enzyme inhibitors and angiotensin receptor blockers. J. Am. Heart Assoc.19, e009992 (2018). 10.1161/JAHA.118.009992 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Polkinghorne, K. R. Estimated glomerular filtration rate versus albuminuria in the assessment of kidney function: What’s more important?. Clin. Biochem. Rev.35, 67–73i (2014). [PMC free article] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
The dataset used and analyzed during the current study are available from the corresponding author on reasonable request.