Peak oxygen uptake in relation to total heart volume discriminates heart failure patients from healthy volunteers and athletes

Background: An early sign of heart failure (HF) is a decreased cardiac reserve or inability to adequately increase cardiac output during exercise. Under normal circumstances maximal cardiac output is closely related to peak oxygen uptake (VO(2)peak) which has previously been shown to be closely related to total heart volume (THV). Thus, the aim of this study was to derive a VO(2)peak/THV ratio and to test the hypothesis that this ratio can be used to distinguish patients with HF from healthy volunteers and endurance athletes. Thirty-one patients with HF of different etiologies were retrospectively included and 131 control subjects (60 healthy volunteers and 71 athletes) were prospectively enrolled. Peak oxygen uptake was determined by maximal exercise test and THV was determined by cardiovascular magnetic resonance. The VO(2)peak/THV ratio was then derived and tested. Results: Peak oxygen uptake was strongly correlated to THV (r(2) = 0.74, p < 0.001) in the control subjects, but not for the patients (r(2) = 0.0002, p = 0.95). The VO(2)peak/THV ratio differed significantly between control subjects and patients, even in patients with normal ejection fraction and after normalizing for hemoglobin levels (p < 0.001). In a multivariate analysis the VO(2)peak/THV ratio was the only independent predictor of presence of HF (p < 0.001). Conclusions: The VO(2)peak/THV ratio can be used to distinguish patients with clinically diagnosed HF from healthy volunteers and athletes, even in patients with preserved systolic left ventricular function and after normalizing for hemoglobin levels

Disappearance of myocardial perfusion defects on prone SPECT imaging: Comparison with cardiac magnetic resonance imaging in patients without established coronary artery disease

<p>Abstract</p> <p>Background</p> <p>It is of great clinical importance to exclude myocardial infarction in patients with suspected coronary artery disease who do not have stress-induced ischemia. The diagnostic use of myocardial perfusion single-photon emission computed tomography (SPECT) in this situation is sometimes complicated by attenuation artifacts that mimic myocardial infarction. Imaging in the prone position has been suggested as a method to overcome this problem.</p> <p>Methods</p> <p>In this study, 52 patients without known prior infarction and no stress-induced ischemia on SPECT imaging were examined in both supine and prone position. The results were compared with cardiac magnetic resonance imaging (CMR) with delayed-enhancement technique to confirm or exclude myocardial infarction.</p> <p>Results</p> <p>There were 63 defects in supine-position images, 37 of which disappeared in the prone position. None of the 37 defects were associated with myocardial infarction by CMR, indicating that all of them represented attenuation artifacts. Of the remaining 26 defects that did not disappear on prone imaging, myocardial infarction was confirmed by CMR in 2; the remaining 24 had no sign of ischemic infarction but 2 had other kinds of myocardial injuries. In 3 patients, SPECT failed to detect small scars identified by CMR.</p> <p>Conclusion</p> <p>Perfusion defects in the supine position that disappeared in the prone position were caused by attenuation, not myocardial infarction. Hence, imaging in the prone position can help to rule out ischemic heart disease for some patients admitted for SPECT with suspected but not documented ischemic heart disease. This would indicate a better prognosis and prevent unnecessary further investigations and treatment.</p

Analysis of Electrocardiograms Using Artificial Neural Networks.

Most conventional ECG interpretation programs use decision tree logic for interpretation of the ECG. The performance is generally good but can be improved. Artificial neural networks represent a new computer method, which has proved to be of value in pattern recognition and classification tasks. The purpose of the studies in this thesis was to improve the analysis/interpretation of the 12-lead ECG by using artificial neural networks. The input values to the networks are extracted from the measurement section of a commercially available interpretation program. No special recording technique or devices have to be used. The results show that artificial neural networks improve computerized ECG interpretation for the diagnosis of acute and healed myocardial infarction. They also perform well in quality control of the ECG recordings by detecting lead reversals with high sensitivity and specificity. The output values from an accurately trained neural network can, under certain conditions, be regarded as a posteriori probabilities for a diagnosis. The output values can also be transformed to verbal statements concerning different probability levels for healed myocardial infarction. The agreement between these probability estimates and those of an experienced electrocardiographer was high. The results indicate that artificial neural networks, if properly trained and validated, will be a useful aid in the attempt to improve the diagnostic yield of the 12-lead ECG

A longitudinal study on cardiac effects of deconditioning and physical reconditioning using the anterior cruciate ligament injury as a model.

BACKGROUND: Studies of cardiovascular deconditioning are primarily carried out after experimental bed rest. No previous study has followed the cardiovascular effects of decreased and resumed physical activity in athletes after acute physical injury and convalescence. Anterior cruciate ligament (ACL) injury causes a significantly decreased activity level over a long period, making it an ideal model for studying effects of deconditioning and reconditioning. Therefore, the aim of this study was to investigate how cardiac dimensions and maximal exercise capacity change after an ACL-injury. METHOD: Seventeen athletes (5 women) were included. Cardiac magnetic resonance (CMR) was performed within 5 days of the injury (CMR1), before endurance training was resumed (CMR2) and 6 months after the second scan (CMR3). Maximal exercise testing was performed on the same day as CMR2 and 3. RESULTS: The deconditioning phase between CMR1 and CMR2 was 59 ± 28 days. Total heart volume (THV) decreased with -3·1 ± 6·7%, P = 0·056. Between CMR2 and 3 (reconditioning), THV increased significantly (2·5 ± 4·6%, P<0·05). Left and right ventricular EDV decreased during deconditioning (-3·0 ± 5·6% and -4·7 ± 6·6%) and increased during reconditioning (1·7 ± 3·9% and 2·6 ± 6·2%) however not statistically significant. Left ventricular mass (LVM) remained unchanged. VO2 peak (mlmin(-1) kg(-1) ) increased significantly during the reconditioning phase (6·1 ± 5·3%, P<0·001). CONCLUSION: Physiological cardiac adaptation to deconditioning and reconditioning caused by severe knee injury with maintained normal daily living during convalescence was smaller than previously shown in bed rest studies. Total heart volume and VO2 peak were significantly affected by reconditioning whilst LVEDV, RVEDV and LVM remained unchanged over the study period

Agreement between artificial neural networks and experienced electrocardiographer on electrocardiographic diagnosis of healed myocardial infarction

Objectives. The purpose of this study was to compare the diagnoses of healed myocardial infarction made from the 12-lead electrocardiogram (ECG) by artificial neural networks and an experienced electrocardiographer. Background. Artificial neural networks have proved of value in pattern recognition tasks. Studies of their utility in ECG interpretation have shown performance exceeding that of conventional ECG interpretation programs. The latter present verbal statements, often with an indication of the likelihood for a certain diagnosis, such as 'possible left ventricular hypertrophy'. A neural network presents its output as a numeric value between 0 and 1; however, these values can be interpreted as Bayesian probabilities. Methods. The study was based on 351 healthy volunteers and 1,313 patients with a history of chest pain who had undergone diagnostic cardiac catheterization. A 12-lead ECG was recorded in each subject. An expert electrocardiographer classified the ECGs in five different groups by estimating the probability of anterior myocardial infarction. Artificial neural networks were trained and tested to diagnose anterior myocardial infarction. The network outputs were divided into five groups by using the output values and four thresholds between 0 and 1. Results. The neural networks diagnosed healed anterior myocardial infarctions at high levels of sensitivity and specificity. The network outputs were transformed to verbal statements, and the agreement between these probability estimates and those of an expert electrocardiographer was high. Conclusions. Artificial neural networks can be of value in automated interpretation of ECGs in the near future