Stephen Lord

Cardiac Testing for Coronary Artery Disease in Potential Kidney Transplant Recipients: A Systematic Review of Test Accuracy Studies

BACKGROUND Cardiovascular disease is the leading cause of death after kidney transplant. Screening for coronary artery disease is integral to pretransplant evaluation, although the relative performance of different tests is uncertain. STUDY DESIGN Systematic review of diagnostic test accuracy studies using hierarchical summary receiver operating characteristic analysis. SETTING & POPULATION Kidney transplant candidates undergoing pretransplant assessment. SELECTION CRITERIA OF STUDIES: Studies evaluating the accuracy of screening tests for detecting coronary artery disease. INDEX TESTS Any non- or minimally invasive test used to diagnose coronary artery disease. REFERENCE TEST Coronary angiography. RESULTS 11 studies (690 participants) evaluated dobutamine stress echocardiography; 7 (317 participants), myocardial perfusion scintigraphy; 2 (129 participants), exercise stress electrocardiography; and 2 (121 participants), other tests. Dobutamine stress echocardiography had pooled sensitivity of 0.80 (95% CI, 0.64-0.90) and specificity of 0.89 (95% CI, 0.79-0.94). Myocardial perfusion scintigraphy had pooled sensitivity of 0.69 (95% CI, 0.48-0.85) and specificity of 0.77 (95% CI, 0.59-0.89). Head-to-head comparison of dobutamine stress echocardiography and myocardial perfusion scintigraphy (2 studies; 116 participants) showed that dobutamine stress echocardiography had higher specificity and at least equivalent or higher sensitivity. Indirect comparison suggested dobutamine stress echocardiography may have improved accuracy over myocardial perfusion scintigraphy (P = 0.07). LIMITATIONS Power to detect differences in accuracy between tests is limited due to sparse data. Absence of significant coronary artery disease may not necessarily correlate with cardiac event-free survival after transplant. CONCLUSIONS Dobutamine stress echocardiography may perform better than myocardial perfusion scintigraphy; however, additional studies directly comparing dobutamine stress echocardiography and myocardial perfusion scintigraphy are needed. Further research should focus on assessing the ability of functional tests to predict postoperative outcome.

Exercise response after cardiac transplantation: correlation with sympathetic reinnervation.

OBJECTIVE--To investigate the relation between sympathetic efferent reinnervation and chronotropic competence during exercise testing after cardiac transplantation. PATIENTS--Twenty five long-term cardiac transplant recipients and 11 normal controls. SETTING--Regional cardiothoracic centre. METHODS--Intracoronary tyramine was given to the transplant recipients and the per cent heart rate change measured. Exercise tests were performed in patients and controls according to the chronotropic assessment exercise protocol, and the per cent heart rate reserve measured at peak exercise and 6 min afterwards to estimate the recovery rate. RESULTS--The mean (SD) percentage heart rate change after intracoronary tyramine was 15.7 (15.4). Heart rate reserve achieved at peak exercise was 68.3 (20.6)% compared with 102.7 (9.3)% in the controls (P < 0.001). Heart rate recovery at 6 min was 41.7 (20.1)% compared with 79.5 (9.0)% in the controls (P < 0.001). Total workload was 69.0 (33.0) METS.min compared with 117.2 (41.9) METS.min in the controls (P < 0.01). There was a positive correlation between heart rate reserve achieved at peak exercise and response to tyramine (r = 0.66, P < 0.01), between heart rate recovery and response to tyramine (r = 0.69, P < 0.001), and between total workload and response to tyramine (r = 0.63, P = 0.04). CONCLUSION--Functional sympathetic efferent reinnervation of the sinus node occurred in some patients after transplantation, and was associated with improved heart rate response during and recovery after exercise, as well as with increased total workload.

Heart rate and blood pressure variability in normal subjects compared with data from beat-to-beat models developed from de Boer's model of the cardiovascular system

The objective of this study was to assess the ability of de Boers model of the cardiovascular system to reproduce the heart rate and blood pressure variability observed in a range of normal subjects, and to make modifications to improve its performance. ECG, blood pressure and chest wall movement were recorded from 12 normal human subjects during controlled breathing. For each beat, systolic pressure, diastolic pressure, arterial time constant and RR interval were extracted. RR interval and systolic pressure spectral power in low and high frequency bands and the baroreflex sensitivity index, alpha, were then determined. For each subject, mean values were input to the model and the beat-to-beat output compared with the actual data for that subject. Finally, the effects of reducing the influence of baroreflex on peripheral vascular resistance and of providing separate sympathetic and vagal baroreflex sensitivities were assessed. Simulations resulted in data which were qualitatively similar to those of each subjects recording. With the modifications, the log ratio of simulated to real data improved from 7.2 to 1.5 (p = 0.003) for low frequency RR, from 0.27 to 0.55 (p = 0.011) for high frequency RR and from 8.5 to 0.9 (p = 0.003) for low frequency systolic pressure. We conclude that de Boers model reproduces many of the characteristics of heart rate and blood pressure variability, and our modifications to baroreflex sensitivities and the feedback effect on peripheral resistance resulted in significant improvements.

Sympathetic reinnervation and heart rate variability after cardiac transplantation.

BACKGROUND: Heart rate variability is thought to measure autonomic modulation, but the relation has never been demonstrated directly in humans. AIM: To test the hypothesis that increased low frequency heart rate variability reflects sympathetic reinnervation after cardiac transplantation. PATIENTS: 24 cardiac transplant recipients at the time of routine surveillance coronary angiography two or more years after cardiac transplantation, and 10 controls with normal coronary arteries undergoing angiography for investigation of chest pain. SETTING: Regional cardiothoracic centre. METHODS: Sympathetic effector function at the sinus node was assessed by measuring the fall in cycle length for two minutes after injection of tyramine to the artery supplying the sinus node. Heart rate variability was measured from three-minute RR interval sequences at rest, during metronomic respiration, and before and after atropine. RESULTS: The logarithm of the low frequency component of heart rate variability during metronomic respiration was linearly related to the logarithm of the change in cycle length after injection of tyramine (R2 = 0.28, P = 0.007). Absolute units more accurately reflected sympathetic effector function than did normalised units or the ratio of low frequency to high frequency. Atropine did not affect high frequency heart rate variability in transplant recipients. CONCLUSIONS: The low frequency component of heart rate variability is directly related to sympathetic reinnervation to the sinus node.

Prognostic value of cardiac tests in potential kidney transplant recipients: a systematic review.

Background Whether abnormal myocardial perfusion scintigraphy (MPS), dobutamine stress echocardiography (DSE) or coronary angiography, performed during preoperative evaluation for potential kidney transplant recipients, predicts future cardiovascular morbidity is unclear. We assessed test performance for predicting all-cause mortality, cardiovascular mortality and major adverse cardiac events (MACE). Methods We searched MEDLINE and EMBASE (to February 2014), appraised studies, and calculated risk differences and relative risk ratios (RRR) with 95% confidence intervals (95% CI) using random effects meta-analysis. Results Fifty-two studies (7401 participants) contributed data to the meta-analysis. Among the different tests, similar numbers of patients experienced MACE after an abnormal test result compared with a normal result (risk difference: MPS 20 per 100 patients tested [95% CI, 0.11–0.29], DSE 24 [95% CI, 0.10–0.38], and coronary angiography 20 [95% CI, 0.08–0.32; P = 0.91]). Although there was some evidence that coronary angiography was better at predicting all-cause mortality than MPS (RRR, 0.69; 95% CI, 0.49–0.96; P = 0.03) and DSE (RRR, 0.72; 95% CI, 0.50–1.02; P = 0.06), noninvasive tests were as good as coronary angiography at predicting cardiovascular mortality (RRR, MPS, 0.89; 95% CI, 0.38–2.10; P = 0.78; DSE, 1.09; 95% CI, 0.12–10.05; P = 0.93), and MACE (RRR: MPS, 1.09; 95% CI, 0.64–1.86; P = 0.74; DSE, 1.56; 95% CI, 0.71–3.45; P = 0.25). Conclusions Noninvasive tests are as good as coronary angiography at predicting future adverse cardiovascular events in advanced chronic kidney disease. However, a substantial number of people with negative test results go on to experience adverse cardiac events.

Comparison of autoregressive and Fourier transform based techniques for estimating RR interval spectra

Two fundamentally different approaches have been used to estimate spectra of RR interval series. The classical approach uses a Fourier transform (FT), and the model based approach uses an autoregressive (AR) model. The aim of this study was to compare estimates of both low frequency (LF) and high frequency (HF) heart rate variability (HRV) obtained using both FT and AR techniques. Five minute RR interval series were obtained from a group of normals (N=9) and a group of heart transplant patients with decreased HRV (N=9). For the normal group, mean LF power was 1164 ms/sup 2/ (SD 817 ms/sup 2/) with the FT and 1358 ms/sup 2/ (SD 906 m/sup 2/s) with the AR method. Mean HF power was 1010 ms/sup 2/ (SD 1321 ms/sup 2/) with the FT and 442 ms/sup 2/ (SD 718 ms/sup 2/) with the AR method. In the transplant group LF HRV was unmeasureable, and mean HF power was 2.39 ms/sup 2/ with the FT and 5.31 m/sup 2/s with the AR method. Overall the two measures were well correlated (r=0.74). Both FT and AR spectra provide a comparable measure of LF and HF HRV.

SCREENING TESTS FOR CORONARY ARTERY DISEASE IN POTENTIAL KIDNEY TRANSPLANT RECIPIENTS: A SYSTEMATIC REVIEW OF DIAGNOSTIC TEST ACCURACY: 849

L.W. Wang1, P. Macaskill2, M.A. Fahim3, J.C. Craig4, R. Mitchell5, A. Hayen6, L. Baines7, S. Lord8, A. Webster9 1Medicine, Prince of Wales Hospital, Randwick/NSW/AUSTRALIA, 2, University of Sydney, Sydney/AUSTRALIA, 3Department Of Nephrology, Princess Alexandria Hospital, Brisbane/NSW/ AUSTRALIA, 4Centre For Kidney Research, The Children’s Hospital at Westmead, Westmead/AUSTRALIA, 5Cochrane Renal Group, The Children’s Hospital at Westmead, Sydney/AUSTRALIA, 6School Of Public Health, University of Sydney, Sydney/AUSTRALIA, 7Renal Services, Newcastle Upon Tyne Hospitals NHS Trust, Newcastle Upon Tyne/UNITED KINGDOM, 8Cardiology, Freeman Hospital, Newcastle Upon Tyne/UNITED KINGDOM, 9School Of Public Health, University of Sydney, Sydney/NSW/AUSTRALIA

Computer modelling of heart rate and blood pressure

A computer model of the cardiovascular system which models beat-to-beat changes in heart period, systolic pressure, diastolic pressure and the arterial time constant has been studied. The original model, produced by de Boer et al. (1987), was developed with limited data. The authors have shown that in some situations the model may be highly inaccurate, but that it is possible to adapt the model to fit a wide range of data move accurately. From 7 normal subjects the authors recorded a single channel ECG, blood pressure non-invasively using a Finapres monitor, and depth of respiration. The mean values of RR interval, systolic and diastolic pressure, arterial time constant, and baroreflex sensitivity were calculated. The model was produced using LabView, a graphical programming environment. The first implementation of the model used the clinical values calculated the constants specified by de Boer, and the respiration signal the amplitude of which was set so that the power in the high frequency (0.15-0.4 Hz) systolic pressure peak from the model matched that of the real data. The model output was compared with the recorded data for the low frequency (0.04-0.15 Hz) and high frequency spectral peaks of RR interval and low frequency spectral peak of systolic pressure. In some subjects the model outputs were within a factor of 2 but a wide range was obtained up to a factor of 60 for a young healthy subject with high baroreflex sensitivity. It was possible to adapt the model to fit the range of all normal data to within a factor of 8 for the RR and systolic pressure spectral peaks. Overall, the model fitted the high frequency peaks best, with the adaptation having the greatest effect on the low frequency peaks.