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 (VO2peak) which has previously been shown to be closely related to total heart volume (THV). Thus, the aim of this study was to derive a VO2peak/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 VO2peak/THV ratio was then derived and tested. RESULTS: Peak oxygen uptake was strongly correlated to THV (r2 = 0.74, p < 0.001) in the control subjects, but not for the patients (r2 = 0.0002, p = 0.95). The VO2peak/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 VO2peak/THV ratio was the only independent predictor of presence of HF (p < 0.001). CONCLUSIONS: The VO2peak/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.

Detection of hypertrophic cardiomyopathy is improved when using advanced rather than strictly conventional 12-lead electrocardiogram.

INTRODUCTION: Twelve-lead electrocardiogram (ECG) is used to screen for hypertrophic cardiomyopathy (HCM), but up to 25% of HCM patients do not have distinctly abnormal ECGs, whereas up to 5% to 15% of healthy athletes do. We hypothesized that an approximately 5-minute resting advanced 12-lead ECG test ("A-ECG score") could detect HCM with greater sensitivity than pooled conventional ECG criteria and distinguish healthy athletes from HCM with greater specificity. MATERIALS AND METHODS: Five-minute 12-lead ECGs were obtained from 56 HCM patients, 56 age/sex-matched healthy controls, and 69 younger endurance-trained athletes. Electrocardiograms were analyzed using recently suggested pooled conventional ECG criteria and also A-ECG scoring techniques that considered results from multiple advanced and conventional ECG parameters. RESULTS: Compared with pooled criteria from the strictly conventional ECG, an A-ECG logistic score incorporating results from just 3 advanced ECG parameters (spatial QRS-T angle, unexplained portion of QT variability, and T-wave principal component analysis ratio) increased the sensitivity of ECG for identifying HCM from 89% (78%-96%) to 98% (89%-100%; P = .025), while increasing specificity from 90% (83%-94%) to 95% (92%-99%; P = .020). CONCLUSIONS: Resting 12-lead A-ECG scores that are simultaneously more sensitive than pooled conventional ECG criteria for detecting HCM and more specific for distinguishing healthy athletes and other healthy controls from HCM can be constructed. Pending further prospective validation, such scores may lead to improved ECG-based screening for HCM.

Aspects of left ventricular morphology outperform left ventricular mass for prediction of QRS duration.

BACKGROUND: The knowledge of the case-specific normal QRS duration in each individual is needed when determining the onset, severity and progression of the heart disease. However, large interindividual variability even of the normal QRS duration exists. The aims of the study were to develop a model for prediction of normal QRS complex duration and to test it on healthy individuals. METHODS: The study population of healthy adult volunteers was divided into a sample for development of a prediction model (n = 63) and a testing sample (n = 30). Magnetic resonance imaging data were used to assess anatomical characteristics of the left ventricle: the angle between papillary muscles (PM(A)), the length of the left ventricle (LV(L)) and left ventricular mass (LV(M)). Twelve-lead electrocardiogram (ECG) was used for measurement of the QRS duration. Multiple linear regression analysis was used to develop a prediction model to estimate the QRS duration. The accuracy of the prediction model was assessed by comparing predicted with measured QRS duration in the test set. RESULTS: The angle between PM(A) and the length of the LV(L) were statistically significant predictors of QRS duration. Correlation between QRS duration and PM(A) and LV(L) was r = 0.57, P = 0.0001 and r = 0.45, P = 0.0002, respectively. The final model for prediction of the QRS was: QRS(Predicted)= 97 + (0.35 x LV(L)) - (0.45 x PM(A)). The predicted and real QRS duration differed with median 1 ms. CONCLUSIONS: The model for prediction of QRS duration opens the ability to predict case-specific normal QRS duration. This knowledge can have clinical importance, when determining the normality on case-specific basis.