Abstract
Background
Right ventricular (RV) dysfunction is a major cause of death in patients undergoing maintenance hemodialysis (MHD). We used 3‐dimensional speckle‐tracking echocardiography (3DSTE) to evaluate long‐term impacts of MHD on RV function.
Hypothesis
In this study, RV dysfunction in MHD patients will be revealed and studied in depth by 3DSTE.
Methods
Echocardiography was performed on 110 consecutively enrolled individuals: 30 controls and 80 patients with MHD. Conventional echocardiographic parameters and 3DSTE parameters were obtained and compared between groups. Univariate and multivariate logistic regression analysis identified independent predictors of intradialytic hypotension (IDH).
Results
Compared with the control group, RV end‐diastolic volume (RVEDV) was markedly enlarged (46.1 ± 11.8 mL/m2 vs 42.3 ± 8.6 mL/m2; P = 0.047), whereas RV ejection fraction (RVEF) was significantly lower in the MHD group (50.6% ± 5.8% vs 55.2% ± 3.7%; P < 0.001). RV global, septal, and lateral wall longitudinal strains were also decreased in the MHD group (−18.2 ± 3.6 vs −22.6 ± 4.3%; −13.1 ± 3.8 vs −17.5 ± 5.5%; and −23.4 ± 4.7 vs −27.7 ± 4.0%, respectively; all P < 0.001). RVEF (odds ratio [OR]: 0.72, 95% confidence interval [CI]: 0.51 to 1.01, P = 0.038) and history of diabetes (OR: 11.14, 95% CI: 1.16 to 106.71, P = 0.036) were 2 independent predictors of IDH. Ultrafiltration rate was an independent factor associated with RVEF (β = −0.01, 95% CI: −0.019 to 0.001, P = 0.039).
Conclusions
RVEF by 3DSTE could be an important predictor of IDH in MHD patients, and lower ultrafiltration rate was protective for RVEF. 3DSTE may have potential in RV evaluation and risk stratification in MHD patients.
Keywords: Intradialytic Hypotension, Maintenance Hemodialysis, Right Ventricular Function, Speckle Tracking, Three‐Dimensional Echocardiography
1. INTRODUCTION
Cardiovascular (CV) diseases are the main causes of death in patients with maintenance hemodialysis (MHD).1, 2 Previous studies mostly focused on left ventricular (LV) function, but researchers have also shown that right ventricular (RV) dysfunction (RVD) was associated with increased morbidity and mortality in many CV diseases.3, 4 More and more, nephrologists began to realize the importance of RV functional assessment. However, the RV quantification remains difficult due to its complicated geometry. Three‐dimensional speckle‐tracking echocardiography (3DSTE) is a new echocardiographic technology that is useful in rebuilding the complex 3‐dimensional anatomy of the RV. The accuracy and reproducibility of 3DSTE in evaluating RV volume and systolic function have been previously verified,5, 6, 7, 8 as well as assessing RV strain by 3DSTE being gradually validated and applied in clinical practices.9, 10, 11 Intradialytic hypotension (IDH), an important complication of MHD that occurs in up to 25% of dialysis sessions, leads to multiorgan ischemia and also contributes to high risk for CV events and patient death.12, 13 Previous studies have revealed some relationship between LV dysfunction and IDH.14 However, there are few data focusing on the association between the RV and IDH.
The present study aimed to investigate RV function via conventional echocardiography and 3DSTE to reveal (1) the long‐term impacts of hemodialysis on the RV in MHD patients and (2) the relationship between RV dysfunction and IDH.
2. METHODS
2.1. Patients
From July 2015 to June 2016, 80 patients on MHD were enrolled (42 males; age 30–74 years): 60 diagnosed with chronic glomerulonephritis, 10 with IgA nephropathy, 6 with polycystic kidney disease, and 4 with diabetic nephropathy. The diagnosis was made according to the National Kidney Foundation (NKF) Kidney Disease Outcomes Quality Initiative (KDOQI) clinical practice guidelines.15 Exclusion criteria included history of myocardial infarction, cardiomyopathy, congenital heart disease, severe valvular stenosis or regurgitation, abnormal wall motion, heart failure (left ventricular ejection fraction [LVEF] <50%), arrhythmia, pulmonary hypertension caused by lung disease, moderate to large pericardial effusion, or inadequate echocardiographic images. Thirty age‐ and sex‐matched healthy subjects (14 males; age 30–70 years) were included as controls. They met the following inclusion criteria: no history of cardiac symptoms, hypertension, or diabetes mellitus (DM); normal physical examination, electrocardiogram, and echocardiography results; and no use of medication. IDH was defined as a decrease in systolic blood pressure (SBP) by ≥20 mmHg during dialysis accompanied by symptoms caused by hypotension, such as dizziness and nausea.16 The inclusion criteria of the IDH group were based on retrospective investigation of medical record with IDH occurring in at least two‐thirds of the dialysis sessions during the 3 months prior to our study. Among the MHD cohort, 14 patients (23.4%) were included into IDH group and the others into non‐IDH group.
The current study was approved by the Ethics Committee of Zhongshan Hospital affiliated to Fudan University.
2.2. Clinical data
Clinical data were collected on the morning of the interdialytic day. Each patient was physically examined for height, weight, heart rate, SBP, and diastolic blood pressure (DBP). Type of vascular access (distal radiocephalic arteriovenous fistula or permanent central venous catheter); history of hypertension, DM, hypercholesterolemia, and coronary heart diseases; and current smoking and antihypertensive drug therapy were recorded. Body surface area (BSA; in m2) was calculated as 0.0061 × height (cm) + 0.0124 × weight (kg) − 0.0099. The amount of ultrafiltration volume was individually based on interdialysis weight gain (IDWG).17 All HD sessions were programmed as 4 hours. Ultrafiltration rate (UFR; mL/h) was calculated as ultrafiltration volume per hour. Blood urea nitrogen, serum creatinine, albumin, cholesterol, triglycerides, hemoglobin, fasting blood glucose, cardiac troponin T (cTnT), and N‐terminal probrain natriuretic peptide were measured using routine methods.
2.3. Conventional echocardiography
Echocardiography was also performed on the interdialytic day. Conventional echocardiographic images were obtained using a M5S probe (Vivid E9; GE Vingmed Ultrasound, Horten, Norway). In the parasternal long‐axis view, the LV end‐diastolic diameter (LVEDD), LV end‐systolic diameter, interventricular septal thickness (IVST), and posterior wall thickness (PWT) were measured. LV mass (LVM) was calculated by the Devereux formula: LVM (g) = 1.04 × [(IVST + LVEDD + PWT)3 − LVEDD3] − 13.6. Left ventricular mass index (LVMI) was the LVM indexed to BSA.
In the apical 4‐chamber view, the RV end‐diastolic and end‐systolic areas (RVEDA and RVESA) were measured, and RV fractional area change (FAC‐2D) was then calculated as (RVEDA − RVESA) / RVEDA × 100%. Tricuspid annulus plane systolic excursion (TAPSE‐2D) was measured as the distance between the highest and lowest point of tricuspid annulus plane excursion with M‐mode ultrasound. RV early (E‐t) and late (A‐t) inflow velocities were measured by pulsed‐wave Doppler, placing the sample volume in between the tips of the tricuspid valve. Pulsed‐wave tissue Doppler imaging of the tricuspid annulus was used to measure myocardial velocities in peak systole (S‐t) and in early (E′‐t) and late diastole (A′‐t) from the apical 4‐chamber view when the sample volume was placed in the tricuspid annulus of the RV lateral wall. The time intervals from the end to the onset of the tricuspid annular velocity pattern during diastole (a) and the duration of the S‐t (b) were measured and used to calculate the myocardial performance index (MPI) as (a − b) / b. Pulmonary artery systolic pressure (PASP) was calculated from the peak continuous‐wave Doppler velocity of the tricuspid regurgitation jet plus right atrial pressure, as assessed by the inspiratory collapse of the inferior vena cava. All these measurements and calculations were performed according to guidelines for the Echocardiographic Assessment of the Right Heart in Adults.18
2.4. Three‐dimensional speckle‐tracking echocardiography
Images of 3DSTE were obtained in all the subjects with a 4 V fully sampled matrix array transducer (Vivid E9). Four small real‐time subvolumes acquired from alternate cardiac cycles were obtained and then combined to form a full‐volume dataset. The acquisition was performed during breathhold and required a relatively stable R‐R interval to minimize translation artifacts between the 4 acquired subvolumes. The transducer was positioned at a modified apical position, which was more lateral than the standard one for full coverage of the RV. Meanwhile, on the premise of whole coverage of RV, the sector angle and depth were decreased to ensure the frame rates within the range of 40% to ~60% of the heart rate. Images were then stored digitally for offline analysis on a TomTec workstation (4D‐RV Function; TomTec Imaging Systems, Munich, Germany). Several reference points, including mitral/tricuspid valve, apex of LV and RV, aortic valve annulus, and anterior and posterior junction with LV, were successively set by the users in 6 planes. After the detection of endocardial border at the end‐diastolic and end‐systolic reference frames, the user could correct the curves at the starting image. Then the software automatically traced the RV endocardial borders through the entire cardiac cycle. Manual adjustments were made at the end‐diastolic and end‐systolic reference frames to optimize the border tracking. Parameters including RV end‐diastolic volume (RVEDV), RV end‐systolic volume (RVESV), stroke volume (RVSV), ejection fraction (RVEF), and RV septal and lateral wall longitudinal strains (RVLS‐sep and RVLS‐lat) were obtained, and global RV longitudinal strain (RVLS) was calculated as the average of RVLS‐sep and RVLS‐lat. In addition, TAPSE‐3D and FAC‐3D were also obtained automatically (Figure 1).
Figure 1.
Demonstration of 3DSTE analysis on a TomTec workstation. (A) Reference points were set in 6 views. Then the workstation made the detection of endocardial border at the (B) end‐diastolic and (C) end‐systolic reference frames. (D) Endocardial tracking is performed automatically through the entire cardiac cycle by 3DSTE. Abbreviations: 3D, 3‐dimensional; 3DSTE, three‐dimensional speckle tracking echocardiography
Meanwhile, 3D datasets with full coverage of the LV were also acquired from all subjects. LV end‐diastolic volume (LVEDV) and ejection fraction (LVEF) were then analyzed offline (4D‐LV Function; TomTec Imaging Systems). The image acquisition and tracing proposals were performed in the same way as described in a previous publication.19
All the volume variables were standardized by BSA.
2.5. Reproducibility
Ten randomly selected 3DSTE images of RV were analyzed by 2 observers and by 1 observer twice with a time interval of 2 weeks for the measurements of 3DSTE data. Reproducibility variables were differences between repeated measurements expressed as an absolute percentage of the mean.
2.6. Statistical analysis
Continuous variables were expressed as mean ± SD, and the variables with normal distribution were compared between MHD and control groups, and between IDH and non‐IDH groups, using the unpaired Student t test. The Wilcoxon rank‐sum test was performed for continuous variables with skewed distributions. The χ2 test or Fisher exact test was performed for the rates of the 2 groups. Inter‐ and intraobserver reproducibility of 3DSTE data were assessed using intraclass correlation coefficients and coefficients of variation. P < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 16.0 software (SPSS Inc., Chicago, IL).
3. RESULTS
3.1. Clinical data
Three MHD patients were excluded because of poor quality of images (defined as >2 nonvisualized segments). A total of 77 patients and 30 controls were finally included in the statistical analysis. Between‐group comparisons of the clinical and laboratory characteristics are shown in Table 1. There were significant differences between the MHD and control groups in SBP, DBP, heart rate, blood urea nitrogen, serum creatinine, hemoglobin, and fasting blood glucose. The IDH group had a tendency toward higher UFR (673 ± 250 mL/h) than did the non‐IDH group (564 ± 200 mL/h; P = 0.085).
Table 1.
Comparison of demographic and laboratory characteristics in control, MHD, IDH, and non‐IDH groups
| Control, n = 30 | MHD, n = 77 | P Value | Non‐IDH, n = 63 | IDH, n = 14 | P Value | |
|---|---|---|---|---|---|---|
| Demographic and clinical characteristics | ||||||
| Age, y | 54 ± 13 | 57 ± 12 | 0.276 | 57 ± 13 | 56 ± 10 | 0.839 |
| Male sex | 14 (47) | 40 (52) | 0.671 | 34 (54) | 6 (43) | 0.559 |
| Body weight, kg | 60.4 ± 11.7 | 60.9 ± 9.2 | 0.832 | 59.0 ± 11.9 | 60.3 ± 9.1 | 0.346 |
| BSA | 1.8 ± 0.2 | 1.8 ± 0.2 | 0.879 | 1.8 ± 0.2 | 1.8 ± 0.1 | 0.762 |
| Type of vascular access, AVF:CVC | — | 42:35 | — | 35:28 | 7:7 | 0.772 |
| SBP, mm Hg | 120 ± 9 | 148 ± 20 | <0.001a | 146 ± 18 | 154 ± 27 | 0.215 |
| DBP, mm Hg | 76 ± 5 | 97 ± 15 | <0.001a | 97 ± 14 | 96 ± 20 | 0.865 |
| Heart rate, bpm | 69 ± 10 | 77 ± 11 | 0.003a | 76 ± 11 | 81 ± 12 | 0.119 |
| UFR, mL/h | 577 ± 220 | — | 564 ± 200 | 673 ± 250 | 0.085 | |
| History of DM | 17 (22) | — | 11 (18) | 9 (43) | 0.069 | |
| History of HTN | 70 (91) | — | 58 (92) | 12 (86) | 0.604 | |
| Hypercholesterolemia | 16 (21) | — | 13 (21) | 3 (21) | NS | |
| Current smoker | 3 (10) | 10 (13) | NS | 8 (13) | 2 (14) | NS |
| History of CHD | 11 (14) | — | 7 (11) | 4 (29) | 0.107 | |
| Medications | ||||||
| α/β‐Blocker | 40 (52) | — | 33 (52) | 7 (50) | NS | |
| ACEI/ARB | 28 (36) | — | 22 (35) | 6 (43) | 0.760 | |
| CCB | 56 (72) | — | 47 (75) | 9 (64) | 0.511 | |
| Laboratory characteristics | ||||||
| cTnT, pg/mL | 22.0 ± 23.1 | — | 22.4 ± 22.9 | 19.9 ± 27.1 | 0.734 | |
| Log(NT‐proBNP), pg/mL | 3.5 ± 0.5 | — | 3.5 ± 0.4 | 3.6 ± 0.5 | 0.839 | |
| BUN, mmol/L | 4.8 ± 1.4 | 25.1 ± 10.6 | <0.001a | 27.9 ± 5.7 | 30.2 ± 4.6 | 0.207 |
| sCr, μmol/L | 40 ± 12 | 1117 ± 262 | <0.001a | 1108 ± 272 | 1155 ± 221 | 0.586 |
| Albumin, g/L | 38.1 ± 7.2 | 35.0 ± 5.8 | 0.322 | 38.9 ± 2.9 | 39.8 ± 2.6 | 0.344 |
| Cholesterol, mmol/L | 4.0 ± 1.5 | 4.3 ± 0.9 | 0.122 | 4.2 ± 1.8 | 4.5 ± 1.7 | 0.271 |
| TG, mmol/L | 1.4 ± 0.7 | 1.6 ± 1.2 | 0.429 | 1.5 ± 1.3 | 1.8 ± 1.1 | 0.346 |
| Hb, g/L | 124 ± 19 | 100 ± 20 | <0.001a | 111 ± 17 | 116 ± 14 | 0.335 |
| FBG, mmol/L | 4.7 ± 0.6 | 6.8 ± 2.4 | <0.001a | 6.9 ± 2.3 | 6.0 ± 2.6 | 0.354 |
Abbreviations: ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; AVF, arteriovenous fistula; BSA, body surface area; BUN, blood urea nitrogen; CCB, calcium channel blocker; CHD, coronary heart disease; cTnT, cardiac troponin T; CVC, central venous catheter; DBP, diastolic blood pressure; DM, diabetes mellitus; FBG, fasting blood glucose; Hb, hemoglobin; HTN, hypertension; IDH, intradialytic hypotension; MHD, maintenance hemodialysis; NS, not significant; NT‐proBNP, N‐terminal probrain natriuretic peptide; SBP, systolic blood pressure; sCr, serum creatinine; SD, standard deviation; TG, triglycerides; UFR, ultrafiltration rate.
Values are presented as n (%) or mean ± SD.
P < 0.05 compared with control group.
3.2. Conventional echocardiography
PASP notably increased in the MHD group compared with the control group, whereas decreased S‐t was also noted (14.2 ± 2.1 vs 15.1 ± 1.7 cm/s; P = 0.045). However, no significant between‐group difference was found in other RV systolic parameters, including FAC‐2D, TAPSE‐2D, or MPI. Concerning the RV diastolic function, patients with MHD had lower E/A‐t and E′/A′‐t ratios than controls, indicating impaired RV relaxation (Table 2). Patients with IDH had higher PASP (41.3 ± 12.5 vs 33.6 ± 6.0 mmHg; P = 0.002). However, no more differences were detected in the other conventional parameters between the IDH and non‐IDH groups (Table 2).
Table 2.
Comparison of 3DSTE and conventional echocardiography parameters in control, MHD, IDH, and non‐IDH groups
| Control, n = 30 | MHD, n = 77 | P Value | Non‐IDH, n = 63 | IDH, n = 14 | P Value | |
|---|---|---|---|---|---|---|
| 3DSTE parameters | ||||||
| RVEDV, mL/m2 | 42.3 ± 8.6 | 46.1 ± 11.8 | 0.047a | 45.1 ± 11.2 | 46.2 ± 14.8 | 0.723 |
| RVSV, mL/m2 | 23.4 ± 5.3 | 23.7 ± 6.1 | 0.820 | 24.5 ± 5.7 | 20.3 ± 6.6 | 0.021 |
| RVEF, % | 55.2 ± 3.7 | 50.6 ± 5.8 | 0.000a | 52.0 ± 5.2 | 44.4 ± 3.5 | 0.000b |
| RVLS‐sep, % | −17.5 ± 5.5 | −13.1 ± 3.8 | 0.000a | −13.4 ± 3.8 | −11.1 ± 3.5 | 0.044b |
| RVLS‐lat, % | −27.7 ± 4.0 | −23.4 ± 4.7 | 0.000a | −23.6 ± 4.8 | −21.3 ± 3.6 | 0.094 |
| RVLS, % | −22.6 ± 4.3 | −18.2 ± 3.6 | 0.000a | −18.5 ± 3.6 | −16.2 ± 3.0 | 0.030b |
| TAPSE‐3D, mm | 16.6 ± 4.0 | 14.1 ± 3.8 | 0.004a | 14.5 ± 4.4 | 14.0 ± 3.7 | 0.667 |
| FAC‐3D, % | 50.1 ± 4.9 | 45.2 ± 7.3 | 0.002a | 46.2 ± 7.3 | 40.6 ± 5.6 | 0.008b |
| LVEDV, mL/m2 | 42.6 ± 8.3 | 51.8 ± 15.3 | 0.000a | 50.0 ± 13.7 | 55.7 ± 18.2 | 0.064 |
| LVEF, % | 62.2 ± 4.4 | 58.3 ± 4.8 | 0.001a | 60.3 ± 5.7 | 55.1 ± 7.8 | 0.001b |
| Conventional echocardiography parameters | ||||||
| FAC‐2D, % | 52.5 ± 6.1 | 51.3 ± 6.3 | 0.309 | 51.8 ± 5.8 | 49.6 ± 8.2 | 0.230 |
| TAPSE‐2D, mm | 26.4 ± 1.7 | 25.9 ± 3.8 | 0.551 | 26.0 ± 4.2 | 25.8 ± 3.7 | 0.912 |
| MPI | 0.45 ± 0.11 | 0.47 ± 0.16 | 0.466 | 0.47 ± 0.16 | 0.49 ± 0.15 | 0.499 |
| E/A‐t | 1.4 ± 0.3 | 0.9 ± 0.3 | 0.000a | 1.0 ± 0.3 | 0.9 ± 0.2 | 0.581 |
| S‐t, cm/s | 15.1 ± 1.7 | 14.2 ± 2.1 | 0.045a | 14.4 ± 2.4 | 14.3 ± 2.2 | 0.891 |
| E′/A′‐t | 1.0 ± 0.5 | 0.7 ± 0.2 | 0.000a | 0.7 ± 0.2 | 0.6 ± 0.2 | 0.052 |
| E/E′‐t | 3.5 ± 2.1 | 4.0 ± 1.8 | 0.200 | 4.0 ± 1.7 | 3.9 ± 2.2 | 0.744 |
| PASP, mm Hg | 29.6 ± 2.9 | 35.1 ± 8.1 | 0.029a | 33.6 ± 6.0 | 41.3 ± 12.5 | 0.002b |
| LVEDD, mm | 45.7 ± 4.2 | 49.3 ± 5.4 | 0.002a | 48.6 ± 4.4 | 50.6 ± 7.8 | 0.086 |
| LVMI, g/m2 | 83.7 ± 15.7 | 134.2 ± 38.8 | 0.000a | 131.1 ± 34.3 | 148.1 ± 54.0 | 0.138 |
Abbreviations: A′‐t, right ventricular late inflow velocity; E′‐t, right ventricular early inflow velocity; FAC, right ventricular fractional area change; IDH, intradialytic hypotension; LVEDD, left ventricular end‐diastolic diameter; LVEDV, left ventricular end‐diastolic volume; LVEF, left ventricular ejection fraction; LVESV, left ventricular end‐systolic volume; LVMI, left ventricular mass index; MHD, maintenance hemodialysis; MPI, right ventricular myocardial performance index; PASP, pulmonary artery systolic pressure; 3DSTE, three‐dimensional speckle tracking echocardiography; RVEDV, right ventricular end‐diastolic volume; RVEF, right ventricular ejection fraction; RVESV, right ventricular end‐systolic volume; RVLS, global right ventricular longitudinal strain; RVLS‐sep, right ventricular septal wall longitudinal strain; RVLS‐lat, right ventricular lateral wall longitudinal strain; S‐t, myocardial velocity in peak systole; TAPSE, tricuspid annulus plane systolic excursion.
P < 0.05 compared with control group.
P < 0.05 compared with IDH group.
3.3. Three‐dimensional speckle‐tracking echocardiography
3DSTE images of RV were successfully analyzed in 30 (100%) control subjects and 77 (96%) patients with MHD. The frame rates of the 3D data were 45 ± 6 frames/s (range, 38–52 frames/s). Variables are shown in Table 2. Compared with the controls, MHD patients had significantly lower RV longitudinal strains, RVEF, and LVEF (RVLS‐sep: −13.1% ± 3.8% vs −17.5% ± 5.5%; RVLS‐lat: −23.4% ± 4.7% vs −27.7% ± 4.0%; RVLS: −18.2% ± 3.6% vs −22.6% ± 4.3%; RVEF: 50.6% ± 5.8% vs 55.2% ± 3.7%; LVEF: 58.3% ± 4.8% vs 62.2% ± 4.4%; all P < 0.01), as well as enlarged RVEDV and LVEDV (46.1 ± 11.8 vs 42.3 ± 8.6 mL/m2, P = 0.047; 51.8 ± 15.3 vs 42.6 ± 8.3 mL/m2, P < 0.001). Moreover, TAPSE‐3D and FAC‐3D were also decreased in MHD patients (Table 2).
Comparisons between the IDH and non‐IDH groups showed that the former had notably decreased RVEF, RV longitudinal strains, FAC‐3D, and LVEF (RVEF: 44.4% ± 3.5% vs 52.0% ± 5.2%, P < 0.001; RVLS‐sep: −11.1% ± 3.5% vs −13.4% ± 3.8%, P = 0.044; RVLS: −16.2% ± 3.0% vs −18.5% ± 3.6%, P = 0.030; FAC‐3D: 40.6% ± 5.6% vs 46.2% ± 7.3%, P = 0.008; and LVEF: 55.1% ± 7.8% vs 60.3% ± 5.7%, P = 0.001).
3.4. Relationships between conventional and 3DSTE parameters
Correlations between 3DSTE parameters of RV and LVEF, LVMI, and conventional RV parameters are shown in the Supporting Information, Appendix 1, in the online version of this article. It is highlighted that RVEF was notably correlated with LVMI (r = −0.27, P = 0.006), LVEF (r = 0.500, P = 0.007), and conventional RV indices (TAPSE‐2D: r = 0.276, P = 0.006; FAC‐2D: r = 0.373, P = 0.002; S‐t: r = 0.206, P = 0.036; and PASP: r = −0.366, P = 0.001).
3.5. Predictors of IDH
Univariate and multivariable logistic regression analysis showed that history of DM (odds ratio: 11.14, 95% confidence interval [CI]: 1.16 to 106.71, P = 0.036) and RVEF (odds ratio: 0.72, 95% CI: 0.51 to 1.01, P = 0.038) were 2 independent predictors of IDH (Table 3). The diagnostic accuracies of 3DSTE and conventional parameters to predict IDH were shown by receiver operating characteristic curves (see Supporting Information, Appendix 2, in the online version of this article). RVEF derived from 3DSTE had the largest area under the curve (AUC: 0.913, P = 0.006) among the selected indices. The optimal threshold of RVEF for the prediction of IDH was <47.6%, with 85.7% sensitivity and 84.1% specificity (Figure 2).
Table 3.
Univariate and multivariate logistic regression analysis for predictors of IDH
| Univariate | Model: Demographic + Laboratory | |||
|---|---|---|---|---|
| Coefficient | P Value | OR Coefficient (95% CI) | P Value | |
| Age, y | 1.00 | 0.863 | ||
| Sex | 0.99 | 0.982 | ||
| Type of vascular access | 1.24 | 0.554 | ||
| SBP | 1.02 | 0.147 | ||
| DBP | 1.00 | 0.805 | ||
| UFR | 1.00 | 0.031 | 0.89 (0.65 to 1.22) | 0.469 |
| History of DM | 2.64 | 0.006 | 11.14 (1.16 to 106.71) | 0.036 |
| History of HTN | 0.57 | 0.523 | ||
| Hypercholesterolemia | 0.93 | 0.942 | ||
| Current smoking | 0.62 | 0.558 | ||
| History of CHD | 2.86 | 0.138 | ||
| BUN | 1.12 | 0.066 | 1.08 (0.78 to 1.49) | 0.632 |
| sCr | 1.00 | 0.727 | ||
| Albumin | 1.06 | 0.628 | ||
| Cholesterol | 0.08 | 0.818 | ||
| TG | 0.13 | 0.696 | ||
| Hb | 1.03 | 0.213 | ||
| FBG | 0.77 | 0.184 | ||
| cTnT | 0.99 | 0.567 | ||
| Log(NT‐proBNP) | 1.89 | 0.142 | ||
| α/β‐Blocker | 1.06 | 0.297 | ||
| ACEI/ARB | 2.47 | 0.162 | ||
| CCB | 0.92 | 0.921 | ||
| E′/A′‐t | 0.02 | 0.077 | 0.35 (0.00 to 596.13) | 0.787 |
| PASP | 1.11 | 0.009 | 1.11 (0.95 to 1.31) | 0.189 |
| RVEF | 0.83 | 0.001 | 0.72 (0.51 to 1.01) | 0.038 |
| RVLS | 1.27 | 0.040 | 1.09 (0.95 to 1.31) | 0.632 |
| LVEF | 0.83 | 0.005 | 0.93 (0.72 to 1.19) | 0.552 |
| LVMI | 1.01 | 0.125 | ||
Abbreviations: ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin II receptor blocker; A′‐t, right ventricular late inflow velocity; BUN, blood urea nitrogen; CCB, calcium channel blocker; CHD, coronary heart disease; CI, confidence interval; cTnT, cardiac troponin T; DBP, diastolic blood pressure; DM, diabetes mellitus; FBG, fasting blood glucose; Hb, hemoglobin; HTN, hypertension; IDH, intradialytic hypotension; LVEF, left ventricular ejection fraction; LVMI, left ventricular mass index; NT‐proBNP, N‐terminal probrain natriuretic peptide; OR, odds ratio; PASP, pulmonary artery systolic pressure; RVEF, right ventricular ejection fraction; RVLS, global right ventricular longitudinal strain; SBP, systolic blood pressure; sCr, serum creatinine; TG, triglycerides; UFR, ultrafiltration rate.
Figure 2.
The ROC curves of the ability of 3DSTE RV parameters to predict IDH. RVEF had the largest AUC (0.913, P = 0.006) among the selected indices. The optimal threshold of RVEF for the prediction of IDH was <47.6%, with 85.7% sensitivity and 84.1% specificity (the absolute values of RVLS and RVLS‐sep were adopted to integrate those indices into 1 figure). Abbreviations: 3DSTE, 3‐dimensional speckle‐tracking echocardiography; AUC, area under the curve; FAC‐3D, RV fractional area change derived from 3DSTE; IDH, intradialytic hypotension; ROC, receiver operating characteristic; RV, right ventricular; RVEF, right ventricular ejection fraction; RVLS, global right ventricular longitudinal strain; RVLS‐sep, right ventricular septal wall longitudinal strain
3.6. Linear regression for RVEF in MHD patients
Univariate associations with RVEF are summarized the Supporting Information, Appendix 3, in the online version of this article. RVEF was correlated with type of vascular access, UFR, history of coronary heart diseases, cTnT, use of angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker medications, PASP, and LVEF (all P < 0.1). When using stepwise regression, UFR was the only independent factor associated with RVEF among the collected data (β = −0.01, 95% CI: −0.019 to −0.001, P = 0.039; see Supporting Information, Appendix 3, in the online version of this article).
3.7. Reproducibility
Interobserver measurements showed intraclass correlation of 0.82 for RVEDV, 0.80 for RVEF, 0.85 for RVLS‐sep, 0.88 for RVLS‐lat, 0.85 for TAPSE‐3D, and 0.85 for FAC‐3D. Similarly, intraobserver measurements showed intraclass correlation of 0.88 for RVEDV, 0.82 for RVEF, 0.90 for RVLS‐sep, 0.92 for RVLS‐lat, 0.88 for TAPSE‐3D, and 0.89 for FAC‐3D. However, the interobserver coefficient of variation of TAPSE‐3D was 18.1% ± 7.9%, and intraobserver coefficient of variation was 16.2% ± 6.1%, indicating the unsatisfying reliability of this index. For interobserver and intraobserver agreements, see Supporting Information, Appendices 4 and 5, in the online version of this article.
4. DISCUSSION
RVD is common in MHD patients.20, 21, 22 Moreover, RVD could also affect LV filling via interventricular interaction.23 The detection of RVD will be helpful to identify patients with a higher CV risk. Therefore, an accurate and effective method was required for the RV assessment. Previous research focusing on RV function mainly used the conventional echocardiographic variables, such as TAPSE and S‐t, which could only demonstrate deformation in the longitudinal direction of RV free wall. The newly developed 3DSTE, whose accuracy and reproducibility have been validated, is available to display the complex 3‐dimensional anatomy of the RV irrespective of its shape. To our knowledge, this study was the first to evaluate RV systolic function and to explore the long‐term impacts of hemodialysis on MHD patients via 3DSTE.
With respect to the impacts of hemodialysis on cardiac function, the conclusions of previous literature were inconsistent. Researchers generally hold that hemodialysis is detrimental for cardiac structure and function because the LV and RV dilated and both systolic and diastolic function deteriorated during long‐term hemodialysis.24, 25, 26 However, others found that LV strains improved immediately after hemodialysis.27 Our study demonstrated RV morphological enlargement and dysfunction in MHD patients, which was consistent with previous studies. The internal environment changes in MHD patients are quite complicated, including body‐fluid fluctuation, electrolyte disorder, and uremic toxins. These unfavorable factors lead to prolonged active contraction and increased oxygen consumption of cardiomyocytes, eventually causing LV and RV dysfunction.
Hemodialysis has proved to be double‐edged. Redundant fluid and most of the uremic toxins can be removed by hemodialysis. On the other hand, excessive and rapid fluid removal leads to hemodynamic instability with the absence of a proper and timely compensatory mechanism. Moreover, hemodialysis‐induced myocardial stunning driven by ischemia has also been widely recognized.28, 29, 30 IDH is an important complication as a result of an inadequate CV response to large and rapid removal of volume. It is associated with myocardial stunning and multiorgan ischemia owing to decreased perfusion, which contributes to the long‐term risk for CV events and increased mortality.13, 31 Therefore, IDH was adopted as a grouping criterion. The comparisons of echocardiographic data showed the IDH group had notably worse RV systolic function than did the non‐IDH group, detected by 3DSTE. In healthy individuals, compensatory enhancement of myocardial contraction occurred after reduction of preload to maintain adequate cardiac output and blood pressure.32 Patients in non‐IDH group usually hold better cardiac function and compensatory mechanism. Nevertheless, in patients with IDH, the absence of a compensatory mechanism caused the slump of blood pressure and subsequent cardiac ischemia, leading to further myocardial dysfunction and the loss of contraction enhancement. All these factors create a vicious circle and result in eventual deterioration of cardiac function.
The predictive values of RVEF and history of DM for IDH were elucidated by univariate and multivariate logistic regression analysis. Further analysis also showed that 7 (9%) patients in our MHD cohort with both impaired RVEF (<47.6%) and history of DM had significantly increased incidence of IDH (87.5%). DM has been reported by previous researchers as is usually implicated with a poor CV autonomic regulation during hemodialysis.33, 34 RVEF could be helpful for a better risk stratification for IDH in MHD patients. This finding also highlighted the necessity of more protective measures to avoid IDH in patients with impaired RVEF and/or history of DM. Further analysis displayed that RVEF was correlated with type of vascular access, UFR, history of coronary heart disease, cTnT, use of angiotensin‐converting enzyme inhibitor/angiotensin II receptor blocker medications, PASP, and LVEF, revealing the close relationship between RV systolic function and load changes and ischemia. Owing to the deletion of mid myocardium, the RV is more susceptible to the change of load and ischemia than is the LV.35 However, multivariate regression analysis indicated that UFR was the only independent factor associated with RVEF. UFR was mostly determined by IDWG. Previous studies have verified that both large IDWG36 and high UFR37 were independent predictors for CV and all‐cause mortality in MHD cohorts. Every dialysis session brings grave challenges to the heart, as overload volume accumulated during interdialytic days has to be removed within 4 hours. Smaller IDWG could be related to lower UFR and a more moderate dialysis session. Hence, strict weight control during the interdialytic period should be observed to protect RV systolic function. Another option to minimize UFR is performing HD more frequently, such as daily HD or nocturnal dialysis, which also brings better clearance of uremic toxins.38 However, the extra cost and inconvenience limit its feasibility.
RV diastolic dysfunction was also detected in the MHD group, which was consistent with previous studies.25, 26, 39 Impaired diastolic function leads to a concomitant rise in RV end‐diastolic pressure. An elevation in RV diastolic pressure will subsequently cause a shift of the interventricular septum toward LV, which leads to notable depression in the LVEF and, subsequently, a reduced overall cardiac output.23 Therefore, RV diastolic abnormality should also be highlighted and closely monitored by incorporating E/A‐t and E′/A′‐t in the clinic echocardiography report when assessing MHD patients.
To our knowledge, this study was the first to use 3DSTE in evaluating RV impairment in MHD patients. This noninvasive and convenient method could contribute to better CV risk stratification for MHD patients.
4.1. Study limitations
First, our study was limited by the small number of samples and the cross‐sectional design. Second is the lack of gold standards, such as cardiac MRI. Third, with regard to the 3DSTE technique, the 3D dataset acquisition was limited by the acoustic windows and required experienced operators. Furthermore, the RV endocardial border identification in 3DSTE was challenging due to dropouts and translation artifacts. Some manual adjustments were made during the analysis procedure, which would consequently reduce its reproducibility. Hence, improvements in image quality are desired for its tracking reliability of 3DSTE analysis. A better method to assess the reliability of 3DSTE was to use 2 different 3D datasets acquired by 2 independent operators. Thus, we interpreted our findings in a cautious manner and will improve these limitations through a better design and larger samples in future studies.
5. CONCLUSION
MHD patients endure the deterioration of RV function. RVEF by 3DSTE could be an important predictor of IDH in MHD patients, whereas lower UFR could be protective for RVEF. 3DSTE may have potential in RV evaluation and risk stratification in MHD patients.
Conflicts of interest
The authors declare no potential conflicts of interest.
Supporting information
Appendix S1. Correlation matrix between 3DSTE and conventional parameters
Appendix S2. Results of Receiver‐Operating Characteristic Comparing 3DSTE and convention 2D parameters in the MHD cohorts to predict IDH
Appendix S3. Univariate and multivariate regression analysis for factors associated with RVEF
Appendix S4. Bland–Altman analysis for inter‐(triangle) and intra‐(circle) observer reliability.
Appendix S5. intra‐and inter‐observer reproducibility of 3DSTE indices
Sun M, Cao X, Guo Y, et al. Long‐term impacts of hemodialysis on the right ventricle: Assessment via 3‐dimensional speckle‐tracking echocardiography. Clin Cardiol. 2018;41:87–95. 10.1002/clc.22857
Funding information This study was supported by grants from the National Natural Science Foundation of China (81371576) and Youth Foundation of Shanghai Municipal Commission of Health and Family Planning (20174Y0054).
REFERENCES
- 1. Foley RN, Parfrey PS, Sarnak MJ. Epidemiology of cardiovascular disease in chronic renal disease. J Am Soc Nephrol. 1998;9(12 suppl):S16–S23. [PubMed] [Google Scholar]
- 2. Santoro A, Mandreoli M. Chronic renal disease and risk of cardiovascular morbidity‐mortality. Kidney Blood Press Res. 2014;39:142–146. [DOI] [PubMed] [Google Scholar]
- 3. de Groote P, Millaire A, Foucher‐Hossein C, et al. Right ventricular ejection fraction is an independent predictor of survival in patients with moderate heart failure. J Am Coll Cardiol. 1998;32:948–954. [DOI] [PubMed] [Google Scholar]
- 4. Voelkel NF, Quaife RA, Leinwand LA, et al. Right ventricular function and failure: report of a National Heart, Lung, and Blood Institute Working Group on cellular and molecular mechanisms of right heart failure. Circulation. 2006;114:1883–1891. [DOI] [PubMed] [Google Scholar]
- 5. Shimada YJ, Shiota M, Siegel RJ, et al. Accuracy of right ventricular volumes and function determined by three‐dimensional echocardiography in comparison with magnetic resonance imaging: a meta‐analysis study. J Am Soc Echocardiogr. 2010;23:943–953. [DOI] [PubMed] [Google Scholar]
- 6. Chua S, Levine RA, Yosefy C, et al. Assessment of right ventricular function by real‐time three‐dimensional echocardiography improves accuracy and decreases interobserver variability compared with conventional two‐dimensional views. Eur J Echocardiogr. 2009;10:619–624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7. Tamborini G, Brusoni D, Torres Molina JE, et al. Feasibility of a new generation three‐dimensional echocardiography for right ventricular volumetric and functional measurements. Am J Cardiol. 2008;102:499–505. [DOI] [PubMed] [Google Scholar]
- 8. van der Zwaan HB, Geleijnse ML, Soliman OI, et al. Test‐retest variability of volumetric right ventricular measurements using real‐time three‐dimensional echocardiography. J Am Soc Echocardiogr. 2011;24:671–679. [DOI] [PubMed] [Google Scholar]
- 9. Vitarelli A, Mangieri E, Terzano C, et al. Three‐dimensional echocardiography and 2D‐3D speckle‐tracking imaging in chronic pulmonary hypertension: diagnostic accuracy in detecting hemodynamic signs of right ventricular (RV) failure. J Am Heart Assoc. 2015;4:e001584. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Smith BC, Dobson G, Dawson D, et al. Three‐dimensional speckle tracking of the right ventricle: toward optimal quantification of right ventricular dysfunction in pulmonary hypertension. J Am Coll Cardiol. 2014;64:41–51. [DOI] [PubMed] [Google Scholar]
- 11. Atsumi A, Ishizu T, Kameda Y, et al. Application of 3‐dimensional speckle tracking imaging to the assessment of right ventricular regional deformation. Circ J. 2013;77:1760–1768. [DOI] [PubMed] [Google Scholar]
- 12. Shoji T, Tsubakihara Y, Fujii M, et al. Hemodialysis‐associated hypotension as an independent risk factor for two‐year mortality in hemodialysis patients. Kidney Int. 2004;66:1212–1220. [DOI] [PubMed] [Google Scholar]
- 13. Stéfansson BV, Brunelli SM, Cabrera C, et al. Intradialytic hypotension and risk of cardiovascular disease. Clin J Am Soc Nephrol. 2014;9:2124–2132. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Yang NI, Wang CH, Hung MJ, et al. Real‐time three‐dimensional echocardiography provides advanced haemodynamic information associated with intra‐dialytic hypotension in patients with autonomic dysfunction. Nephrol Dial Transplant. 2010;25:249–254. [DOI] [PubMed] [Google Scholar]
- 15. National Kidney Foundation . K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 suppl 1):S1–S266. [PubMed] [Google Scholar]
- 16. K/DOQI Workgroup . K/DOQI clinical practice guidelines for cardiovascular disease in dialysis patients. Am J Kidney Dis. 2005;45(4 suppl 3):S1–S153. [PubMed] [Google Scholar]
- 17. Pecoits‐Filho R, Gonçalves S, Barberato SH, et al. Impact of residual renal function on volume status in chronic renal failure. Blood Purif. 2004;22:285–292. [DOI] [PubMed] [Google Scholar]
- 18. Rudski LG, Lai WW, Afilalo J, et al. Guidelines for the echocardiographic assessment of the right heart in adults: a report from the American Society of Echocardiography endorsed by the European Association of Echocardiography, a registered branch of the European Society of Cardiology, and the Canadian Society of Echocardiography. J Am Soc Echocardiogr. 2010;23:685–713. [DOI] [PubMed] [Google Scholar]
- 19. Kang Y, Sun MM, Cui J, et al. Three‐dimensional speckle tracking echocardiography for the assessment of left ventricular function and mechanical dyssynchrony. Acta Cardiol. 2012;67:423–430. [DOI] [PubMed] [Google Scholar]
- 20. Beigi AA, Sadeghi AM, Khosravi AR, et al. Effects of the arteriovenous fistula on pulmonary artery pressure and cardiac output in patients with chronic renal failure. J Vasc Access. 2009;10:160–166. [DOI] [PubMed] [Google Scholar]
- 21. Alhamad EH, Al‐Ghonaim M, Alfaleh HF, et al. Pulmonary hypertension in end‐stage renal disease and post renal transplantation patients. J Thorac Dis. 2014;6:606–616. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Yigla M, Banderski R, Azzam ZS, et al. Arterio‐venous access in end‐stage renal disease patients and pulmonary hypertension. Ther Adv Respir Dis. 2008;2:49–53. [DOI] [PubMed] [Google Scholar]
- 23. Piazza G, Goldhaber SZ. The acutely decompensated right ventricle: pathways for diagnosis and management. Chest. 2005;128:1836–1852. [DOI] [PubMed] [Google Scholar]
- 24. Floccari F, Granata A, Rivera R, et al. Echocardiography and right ventricular function in NKF stage III chronic kidney disease: ultrasound nephrologists' role. J Ultrasound. 2012;15:252–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Arinc H, Gunduz H, Tamer A, et al. Use of tissue Doppler to assess right ventricle function in hemodialysis patients. Am J Nephrol. 2005;25:256–261. [DOI] [PubMed] [Google Scholar]
- 26. Paneni F, Gregori M, Ciavarella GM, et al. Right ventricular dysfunction in patients with end‐stage renal disease. Am J Nephrol. 2010;32:432–438. [DOI] [PubMed] [Google Scholar]
- 27. Kovács A, Tapolyai M, Celeng C, et al. Impact of hemodialysis, left ventricular mass and FGF‐23 on myocardial mechanics in end‐stage renal disease: a three‐dimensional speckle tracking study. Int J Cardiovasc Imaging. 2014;30:1331–1337. [DOI] [PubMed] [Google Scholar]
- 28. Burton JO, Jefferies HJ, Selby NM, et al. Hemodialysis‐induced cardiac injury: determinants and associated outcomes. Clin J Am Soc Nephrol. 2009;4:914–920. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Ingec M, Oguz EG, Yildirim T, et al. The effect of hemodialysis on cardiac enzyme levels and echocardiographic parameters. Int J Artif Organs. 2014;37:513–520. [DOI] [PubMed] [Google Scholar]
- 30. Nie Y, Zhang Z, Zou J, et al. Hemodialysis‐induced regional left ventricular systolic dysfunction. Hemodial Int. 2016;20:564–572. [DOI] [PubMed] [Google Scholar]
- 31. Zager PG, Nikolic J, Brown RH, et al. “U” curve association of blood pressure and mortality in hemodialysis patients. Medical Directors of Dialysis Clinic, Inc. [published correction appears in Kidney Int. 1998;54:1417]. Kidney Int. 1998;54:561–569. [DOI] [PubMed] [Google Scholar]
- 32. Yadid M, Sela G, Amiad Pavlov D, et al. Adaptive control of cardiac contraction to changes in loading: from theory of sarcomere dynamics to whole‐heart function. Pflugers Arch. 2011;462:49–60. [DOI] [PubMed] [Google Scholar]
- 33. Locatelli F, Pozzoni P, Del VL. Renal replacement therapy in patients with diabetes and end‐stage renal disease. J Am Soc Nephrol. 2004;15(15 suppl 1):S25–S29. [DOI] [PubMed] [Google Scholar]
- 34. Rocha A, Sousa C, Teles P, et al. Frequency of intradialytic hypotensive episodes: old problem, new insights. J Am Soc Hypertens. 2015;9:763–768. [DOI] [PubMed] [Google Scholar]
- 35. Pettersen E, Helle‐Valle T, Edvardsen T, et al. Contraction pattern of the systemic right ventricle shift from longitudinal to circumferential shortening and absent global ventricular torsion. J Am Coll Cardiol. 2007;49:2450–2456. [DOI] [PubMed] [Google Scholar]
- 36. Lee MJ, Doh FM, Kim CH, et al. Interdialytic weight gain and cardiovascular outcome in incident hemodialysis patients. Am J Nephrol. 2014;39:427–435. [DOI] [PubMed] [Google Scholar]
- 37. Movilli E, Gaggia P, Zubani R, et al. Association between high ultrafiltration rates and mortality in uraemic patients on regular haemodialysis: a 5‐year prospective observational multicentre study. Nephrol Dial Transplant. 2007;22:3547–3552. [DOI] [PubMed] [Google Scholar]
- 38. Jin X, Rong S, Mei C, et al. Effects of in‐center nocturnal versus conventional hemodialysis on endothelial dysfunction. Ther Apher Dial. 2012;16:334–340. [DOI] [PubMed] [Google Scholar]
- 39. Said K, Hassan M, Baligh E, et al. Ventricular function in patients with end‐stage renal disease starting dialysis therapy: a tissue Doppler imaging study. Echocardiography. 2012;29:1054–1059. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Appendix S1. Correlation matrix between 3DSTE and conventional parameters
Appendix S2. Results of Receiver‐Operating Characteristic Comparing 3DSTE and convention 2D parameters in the MHD cohorts to predict IDH
Appendix S3. Univariate and multivariate regression analysis for factors associated with RVEF
Appendix S4. Bland–Altman analysis for inter‐(triangle) and intra‐(circle) observer reliability.
Appendix S5. intra‐and inter‐observer reproducibility of 3DSTE indices