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

Predicting peri-operative troponin elevation by advanced electrocardiography

BACKGROUND

Peri-operative mortality remains a global problem and an improved pre-operative risk assessment identifying those at highest risk for peri-operative myocardial injury might improve postsurgical outcomes.

AIMS

To determine whether pre-operative measures of advanced electrocardiography (A-ECG) could predict elevated serum troponin T (TnT) in patients undergoing elective, major non-cardiac surgery.

MATERIAL AND METHODS

This observational cohort study included 257 surgical patients who underwent elective major non-cardiac surgery between the years 2012-2013 and 2015-2016 at Karolinska University Hospital. All selected patients were ≥ 18 years of age [median age 70 (63-75) years], had a pre-operative digital 12‑lead ECG < 6 months prior to the procedure and a postoperative high-sensitivity cardiac TnT (hs-cTnT) sample. A-ECG confounders including atrial fibrillation or flutter, abundant premature atrial or ventricular contractions, bundle branch blocks, QRS duration >110 ms, heart rate > 100 beats/min and paced rhythms were excluded. Previously validated A-ECG diagnostic scores that detect cardiovascular pathologies were calculated and compared in patients with and without peri-operative myocardial injury, defined as hs-cTnT >14 ng l^-1.

RESULTS

Pre-operative left ventricular systolic dysfunction by A-ECG was more probable in patients with than without peri-operative myocardial injury (p = 0.03).

CONCLUSIONS

While a pre-operative A-ECG score for LVSD was able to differentiate between patients with versus without elevated peri-operative TnT levels, it did not add any further utility to standard clinical parameters for predicting troponin-related events in the studied population.

QRS-T angle in patients with Hypertrophic Cardiomyopathy - A comparison with Cardiac Magnetic Resonance Imaging

Objective: We sought to investigate the possible association of a wide QRS-T angle on the surface EKG and myocardial fibrosis on contrast-enhanced cardiovascular magnetic (CMR) imaging in patients with hypertrophic cardiomyopathy (HCM). Background: Risk stratification in HCM patients is challenging. Late gadolinium enhancement (LGE) visualizes myocardial fibrosis with unique spatial resolution and is a strong and independent prognosticator in these patients. The QRS-T angle from the surface EKG is a promising prognostic marker in various cardiac pathologies. Methods: 70 patients with HCM obtained a standardized digital 12-lead EKG for the calculation of the QRS-T angle and underwent comprehensive CMR imaging for visualization of fibrosis by LGE. Patients were divided into groups according to the absence or presence of fibrosis on CMR. Results: 43 of 70 patients with HCM showed LGE on CMR following contrast administration. HCM patients with LGE (fibrosis) had wider QRS-T angles as compared to the patient group without LGE (100±54 vs. 46±31; <0.001). A QRS-T angle of 90 degrees or more was a strong predictor (OR 32.84, CI 4.08-264.47; p <0.001) of HCM with LGE. Conclusion: There is a strong association of a wide QRS-T angle and myocardial fibrosis in patients with HCM.

A 12-Lead ECG-Based System With Physiological Parameters and Machine Learning to Identify Right Ventricular Hypertrophy in Young Adults

OBJECTIVE

The presence of right ventricular hypertrophy (RVH) accounts for approximately 5-10% in young adults. The sensitivity estimated by commonly used 12-lead electrocardiographic (ECG) criteria for identifying the presence of RVH is under 20% in the general population. The aim of this study is to develop a 12-lead ECG system with the related information of age, body height and body weight via machine learning to increase the sensitivity and the precision for detecting RVH.

METHOD

In a sample of 1,701 males, aged 17-45 years, support vector machine is used for the training of 31 parameters including age, body height and body weight in addition to 28 ECG data such as axes, intervals and wave voltages as the inputs to link the output RVH. The RVH is defined on the echocardiographic finding for young males as right ventricular anterior wall thickness > 5.5 mm.

RESULTS

On the system goal for increasing sensitivity, the specificity is controlled around 70-75% and all data tested in the proposed method show competent sensitivity up to 70.3%. The values of area under curve of receiver operating characteristic curve and precision-recall curve using the proposed method are 0.780 and 0.285, respectively, which are better than 0.518 and 0.112 using the Sokolow-Lyon voltage criterion, respectively, for detecting unspecific RVH.

CONCLUSION

We present a method using simple physiological parameters with ECG data to effectively identify more than 70% of the RVH among young adults. Clinical Impact: This system provides a fast, precise and feasible diagnosis tool to screen RVH.

An Electrocardiographic System With Anthropometrics via Machine Learning to Screen Left Ventricular Hypertrophy among Young Adults

The prevalence of physiological and pathological left ventricular hypertrophy (LVH) among young adults is about 5%. A use of electrocardiographic (ECG) voltage criteria and machine learning for the ECG parameters to identify the presence of LVH is estimated only 20-30% in the general population. The aim of this study is to develop an ECG system with anthropometric data using machine learning to increase the accuracy and sensitivity for a screen of LVH. In a large sample of 2,196 males, aged 17–45 years, the support vector machine (SVM) classifier is used as the machine learning method for 31 characteristics including age, body height and body weight in addition to 28 ECG parameters such as axes, intervals and voltages to link the output of LVH. The diagnosis of LVH is based on the echocardiographic criteria for young males to be 116 gram/meter² (left ventricular mass (LVM)/body surface area) or 49 gram/meter^2.7 (LVM/body height^2.7). On the purpose of increasing sensitivity, the specificity is adjusted around 70-75% and all data tested in proposed model reveal high sensitivity to 86.7%. The area under curve (AUC) of the Precision-Recall (PR) curve is 0.308 in the proposed model which is better than 0.109 and 0.077 using Cornell and Sokolow-Lyon voltage criteria for LVH, respectively. Our system provides a novel screening tool using age, body height, body weight and ECG data to identify most of the LVH among young adults. It provides a fast, accurate and practical diagnosis tool to identify LVH.

The transition from hypertension to hypertensive heart disease and heart failure: the PREFERS Hypertension study

Aims

Despite evidence‐based therapeutic approaches, target blood pressure is obtained by less than half of patients with hypertension. Hypertension is associated with a significant risk for heart failure, in particular heart failure with preserved left ventricular (LV) ejection fraction (HFpEF). Although treatment is suggested to be given early after hypertension diagnosis, there is still no evidence‐based medical treatment for HFpEF. We aim to study the underlying mechanisms behind the transition from uncomplicated hypertension to hypertensive heart disease (HHD) and HFpEF. To this end, we will combine cardiac imaging techniques and measurements of circulating fibrosis markers to longitudinally monitor fibrosis development in patients with hypertension.

Methods and results

In a prospective cohort study, 250 patients with primary hypertension and 60 healthy controls will be characterized at inclusion and after 1 and 6 years. Doppler echocardiography, cardiac magnetic resonance imaging, and electrocardiogram will be used for measures of cardiac structure and function over time. Blood biomarkers reflecting myocardial fibrosis, inflammation, and endothelial dysfunction will be analysed. As a proxy for HFpEF development, the primary endpoint is to measure echocardiographic changes in LV function and structure (E/e′ and LAVI) and to relate these measures of LV filling to blood pressure, biomarkers, electrocardiogram, and cardiac magnetic resonance.

Conclusions

We aim to study the timeline and transition from uncomplicated hypertension to HHD and HFpEF. In order to identify subjects prone to develop HHD and HFpEF, we want to find biomarkers and cardiac imaging variables to explain disease progression. Ultimately, we aim at finding new pathways to prevent HFpEF.

Quantitative T-wave morphology assessment from surface ECG is linked with cardiac events risk in genotype-positive KCNH2 mutation carriers with normal QTc values

INTRODUCTION

Long QT syndrome (LQTS) mutation carriers have elevated the risk of cardiac events even in the absence of QTc prolongation; however, mutation penetrance in patients with normal QTc may be reflected in abnormal T-wave shape, particularly in KCNH2 mutation carriers. We aimed to assess whether the magnitude of a three-dimensional T-wave vector (TwVM) will identify KCNH2-mutation carriers with normal QTc at risk for cardiac events.

METHODS

Adult LQT2 patients with QTc < 460 ms in men and <470 ms in women (n = 113, age 42 ± 16 years, 43% male) were compared with genotype-negative family members (n = 1007). The TwVM was calculated using T-wave amplitudes in leads V6, II, and V2 as the square root of (TV6²  + TII²  + (0.5*TV2)² ). Cox regression analysis adjusted for gender and time-dependent beta-blocker use was performed to assess cardiac event (CE) risk, defined as syncope, aborted cardiac arrest, implantable cardioverter-defibrillator therapy, or sudden death.

RESULTS

Dichotomized by median of 0.30 mV, lower TwVM was associated with elevated CE risk compared to those with high TwVM (HR = 2.95, 95% CI, 1.25-6.98, P = .014) and also remained significant after including sex and time-dependent beta-blocker usage in the Cox regression analysis (HR = 2.64, 95% CI, 1.64-4.24, P < .001). However, these associations were found only in women but not in men who had low event rates.

CONCLUSION

T-wave morphology quantified as repolarization vector magnitude using T-wave amplitudes retrieved from standard 12-lead electrocardiogram predicts cardiac events risk in LQT2 women and appears useful for risk stratification of KCNH2-mutation carriers without QTc prolongation.

Right precordial-directed electrocardiographical markers identify arrhythmogenic right ventricular cardiomyopathy in the absence of conventional depolarization or repolarization abnormalities

BACKGROUND

Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) carries a risk of sudden death. We aimed to assess whether vectorcardiographic (VCG) parameters directed toward the right heart and a measured angle of the S-wave would help differentiate ARVD/C with otherwise normal electrocardiograms from controls.

METHODS

Task Force 2010 definite ARVD/C criteria were met for all patients. Those who did not fulfill Task Force depolarization or repolarization criteria (-ECG) were compared with age and gender-matched control subjects. Electrocardiogram measures of a 3-dimentional spatial QRS-T angle, a right-precordial-directed orthogonal QRS-T (RPD) angle, a root mean square of the right sided depolarizing forces (RtRMS-QRS), QRS duration (QRSd) and the corrected QT interval (QTc), and a measured angle including the upslope and downslope of the S-wave (S-wave angle) were assessed.

RESULTS

Definite ARVD/C was present in 155 patients by 2010 Task Force criteria (41.7 ± 17.6 years, 65.2% male). -ECG ARVD/C patients (66 patients) were compared to 66 control patients (41.7 ± 17.6 years, 65.2% male). All parameters tested except the QRSd and QTc significantly differentiated -ECG ARVD/C from control patients (p < 0.004 to p < 0.001). The RPD angle and RtRMS-QRS best differentiated the groups. Combined, the 2 novel criteria gave 81.8% sensitivity, 90.9% specificity and odds ratio of 45.0 (95% confidence interval 15.8 to 128.2).

CONCLUSION

ARVD/C disease process may lead to development of subtle ECG abnormalities that can be distinguishable using right-sided VCG or measured angle markers better than the spatial QRS-T angle, the QRSd or QTc, in the absence of Taskforce ECG criteria.

Clinical Application of the QRS-T Angle for the Prediction of Ventricular Arrhythmias in Patients with the Fontan Palliation

Fontan palliation patients are at risk for ventricular arrhythmias post-operatively. This study aimed to evaluate whether differences in the spatial QRS-T angle can reliably predict ventricular arrhythmias in patients who had undergone Fontan palliation. A total of 117 patients who had the Fontan palliation and post-Fontan catheterization were included. Ventricular arrhythmias were identified in nine patients. Measurements of ECG parameters including QRS vector magnitude, QRS duration, corrected QT interval, and spatial peaks QRS-T angles were performed, and compared between those with and without ventricular arrhythmias. The only ECG parameter to distinguish those with versus those without VA was the SPQRS-T angle (p < 0.001), which at a cut-off value of 102.9° gave sensitivity, specificity, positive and negative predictive values of 100.0, 57.0, 17.6 and 100.0%, respectively. Only the spatial peaks QRS-T angle differentiated those with and without ventricular arrhythmia development with a univariate HR 1.237 (95% CI 1.021-1.500) and a multivariate HR of 1.032 (1.009-1.056) when catheter measured parameters were taken into account. In Fontan patients, the spatial peaks QRS-T angle is a significant independent predictor of ventricular arrhythmias. Clinical usefulness of this parameter remains to be seen and should be tested prospectively.

Non-invasive electrocardiographic predictors of atrial arrhythmias in patients with the Fontan palliation

BACKGROUND

Fontan palliation patients are at risk for atrial arrhythmias post-operatively. This study aimed to evaluate whether differences in depolarization vector magnitude or spatial P-R angle can reliably predict atrial arrhythmias in patients who had undergone Fontan palliation.

METHODS

A total of 115 patients who had Fontan palliation and post-Fontan catheterization were included. Atrial arrhythmias were identified in 14 patients. Measurements of ECG parameters including QRS vector magnitude, P-R duration, spatial QRS-T angle and spatial P-R angle were performed, and compared between those with and without atrial arrhythmias.

RESULTS

Only the QRSvm independently differentiated those with and without atrial arrhythmias with multivariate HR of 0.743 (95% CI 0.581 to 0.951).

CONCLUSION

In Fontan patients, the QRSvm was the only significant independent predictor of atrial arrhythmias.

Vectorcardiography identifies patients with electrocardiographically concealed long QT syndrome

BACKGROUND

Long QT syndrome (LQTS) and genotypic subtypes are associated with distinctive T-wave patterns, arrhythmogenic triggers, and corrected QT (QTc) interval risk associations. Twenty percent of patients with LQTS have normal QTc values, defined as electrographically concealed LQTS (ecLQTS). Vectorcardiography (VCG) has value for sudden cardiac death risk assessment.

OBJECTIVE

The purpose of this study was to determine the use of VCG to identify patients with ecLQTS.

METHODS

We performed a retrospective analysis in patients with ecLQTS with resting QTc values <440 ms. Computerized derivation of the spatial mean and peak QRS-T angles, QTpeak, Tpeak-Tend (angle between QRS and T-wave peak amplitudes in 3-dimensional space), and T-wave eigenvalues (TwEVs; amplitudes [in microvolts] for each of the first 4 TwEVs were derived from the 12-lead electrocardiogram) was performed. The results were compared with those for healthy controls. Intergenotype differences were analyzed.

RESULTS

Of 610 patients with LQTS, 169 patients (28%) had ecLQTS (86 (51%) men; mean age 22 ± 16 years; mean QTc interval 422 ± 14 ms). There were 519 healthy controls (44% men; mean age 19.8 ± 13.8 years) with a mean QTc interval of 426 ± 28 ms. Among VCG parameters, QTpeak and TwEVs significantly differentiated patients with ecLQTS from controls (P ≤ .01 for each) as well as differentiated KCNQ1-encoded type 1 LQTS (ecLQT1), KCNH2-encoded type 2 LQTS (ecLQT2), and SCN5A-encoded type 3 LQTS (ecLQT3) from controls (P < .01). ecLQT3 was differentiated from controls and ecLQT1 and ecLQT2 by the fourth TwEV (P < .01 for each). The fourth TwEV differentiated symptomatic patients with ecLQTS from asymptomatic patients with ecLQTS (P < .01).

CONCLUSION

ecLQTS can be distinguished from controls using QTpeak. ecLQT3 was best differentiated by the fourth TwEV. VCG may facilitate familial diagnostic anticipation of LQTS status before the completion of mutation-specific genetic testing even with normal resting QTc values.

Lower spatial QRS-T angle rules out sustained ventricular arrhythmias in children with hypertrophic cardiomyopathy

Introduction The spatial peaks QRS-T angle accurately distinguishes children with hypertrophic cardiomyopathy from their healthy counterparts. The spatial peaks QRS-T angle is also useful in risk stratification for ventricular arrhythmias. We hypothesised that the spatial peaks QRS-T angle would be useful for the prediction of ventricular arrhythmias in hypertrophic cardiomyopathy patients under 23 years of age.

METHODS

Corrected QT interval and spatial peaks QRS-T angles were retrospectively assessed in 133 paediatric hypertrophic cardiomyopathy patients (12.4±6.6 years) with versus without ventricular arrhythmias of 30 seconds or longer. Significance, positive/negative predictive values, and odds ratios were calculated based on receiver operating characteristic curve cut-off values.

RESULTS

In total, 10 patients with ventricular arrhythmias were identified. Although the corrected QT interval did not differentiate those with versus without ventricular arrhythmias, the spatial peaks QRS-T angle did (151.4±19.0 versus 116.8±42.6 degrees, respectively, p<0.001). At an optimal cut-off value (124.1 degrees), the positive and negative predictive values of the spatial peaks QRS-T angle were 15.4 and 100.0%, respectively, with an odds ratio of 25.9 (95% CI 1.5-452.2).

CONCLUSION

In children with hypertrophic cardiomyopathy, the spatial peaks QRS-T angle is associated with ventricular arrhythmia burden with high negative predictive value and odds ratio.

ECG-derived spatial QRS-T angle is strongly associated with hypertrophic cardiomyopathy

INTRODUCTION

ECG-derived vectorcardiography (VCG) has diagnostic and prognostic value in various diseases. Hypertrophic cardiomyopathy (HCM), a genetic disease with unexplained left ventricular hypertrophy, is one of the most common causes of sudden cardiac death (SCD) in young persons. Genotype positive status is associated with increased risk of systolic dysfunction, heart failure, and (SCD). Herein, we aimed to determine the diagnostic utility of derived VCG parameters in a large cohort of genotyped HCM patients.

METHODS

Between 1997 and 2007, genetic testing was performed on 1053 unrelated patients with HCM. Of these, 967 had 12-lead ECGs suitable for computerized derivation of VCG parameters, including the spatial mean and peaks QRS-T angles, spatial ventricular gradient (SVG), spatial QRS, QT, and Tpeak-Tend (TpTe) intervals. ECGs were also evaluated using Seattle ECG criteria. Differences between HCM patients and healthy controls as well as between genotype positive versus genotype negative HCM patients were assessed.

RESULTS

Spatial peaks (129.3±26.4 vs.30.5±24.2 degrees) and spatial mean QRS-T angles (121.8±38.6 vs. 47.3±27.6 degrees) were significantly higher in patients with HCM than in controls (P<0.001). The spatial peaks and mean QRS-T angles identified 94% and 84% of HCM patients, respectively, while Seattle criteria identified 70.7% of patients (P<0.001). Genotype positive patients had higher spatial mean QRS-T angles, spatial TpTe (P<0.001 respectively), spatial peaks QRS-T angles (P=0.017) and lower SVG (P<0.001) than genotype negative patients.

CONCLUSIONS

ECG-derived spatial QRS-T angles can differentiate patients with HCM from controls and could provide a better tool than traditional Seattle criteria. Clinical usefulness of VCG to differentiate genotype-negative from genotype-positive patients has yet to be established.

More Than Just Accuracy: A Novel Method to Incorporate Multiple Test Attributes in Evaluating Diagnostic Tests Including Point of Care Tests

Current frameworks for evaluating diagnostic tests are constrained by a focus on diagnostic accuracy, and assume that all aspects of the testing process and test attributes are discrete and equally important. Determining the balance between the benefits and harms associated with new or existing tests has been overlooked. Yet, this is critically important information for stakeholders involved in developing, testing, and implementing tests. This is particularly important for point of care tests (POCTs) where tradeoffs exist between numerous aspects of the testing process and test attributes. We developed a new model that multiple stakeholders (e.g., clinicians, patients, researchers, test developers, industry, regulators, and health care funders) can use to visualize the multiple attributes of tests, the interactions that occur between these attributes, and their impacts on health outcomes. We use multiple examples to illustrate interactions between test attributes (test availability, test experience, and test results) and outcomes, including several POCTs. The model could be used to prioritize research and development efforts, and inform regulatory submissions for new diagnostics. It could potentially provide a way to incorporate the relative weights that various subgroups or clinical settings might place on different test attributes. Our model provides a novel way that multiple stakeholders can use to visualize test attributes, their interactions, and impacts on individual and population outcomes. We anticipate that this will facilitate more informed decision making around diagnostic tests.

QT interval variability in body surface ECG: measurement, physiological basis, and clinical value: position statement and consensus guidance endorsed by the European Heart Rhythm Association jointly with the ESC Working Group on Cardiac Cellular Electrophysiology

This consensus guideline discusses the electrocardiographic phenomenon of beat-to-beat QT interval variability (QTV) on surface electrocardiograms. The text covers measurement principles, physiological basis, and clinical value of QTV. Technical considerations include QT interval measurement and the relation between QTV and heart rate variability. Research frontiers of QTV include understanding of QTV physiology, systematic evaluation of the link between QTV and direct measures of neural activity, modelling of the QTV dependence on the variability of other physiological variables, distinction between QTV and general T wave shape variability, and assessing of the QTV utility for guiding therapy. Increased QTV appears to be a risk marker of arrhythmic and cardiovascular death. It remains to be established whether it can guide therapy alone or in combination with other risk factors. QT interval variability has a possible role in non-invasive assessment of tonic sympathetic activity.

In Hypertrophic Cardiomyopathy, the Spatial Peaks QRS-T Angle Identifies Those With Sustained Ventricular Arrhythmias

BACKGROUND

The spatial peaks QRS-T (SPQRS-T) angle differentiates hypertrophic cardiomyopathy (HCM) patients from controls. Increased angle confers arrhythmia risk in other populations.

HYPOTHESIS

We predict that the SPQRS-T angle will identify HCM patients with sustained ventricular arrhythmias (VAs) and those with New York Heart Association class III/IV heart failure.

METHODS

Corrected QT interval, QRS duration, and SPQRS-T angle were assessed in HCM patients with VAs (>30 seconds) and those without VAs.

RESULTS

One hundred HCM patients (mean age, 32.7 ± 17.2 years) were assessed. Twenty patients had VAs. The corrected QT interval identified VA (P = 0.018) and at 460 ms gave positive and negative predictive values of 28.6% and 83.3%, respectively, and an odds ratio of 2.0 (95% confidence interval: 0.7-5.6). The SPQRS-T angle differentiated VA from no VA (P < 0.001) and at 124.1 degrees gave positive and negative predictive values and an odds ratio of 36.7%, 96.1%, and 14.2 (95% confidence interval: 3.1-65.6), respectively.

CONCLUSIONS

The SPQRS-T angle best differentiated patients with VAs.

Differentiating hypertrophic cardiomyopathy from athlete's heart: An electrocardiographic and echocardiographic approach

Differential diagnosis of hypertrophic cardiomyopathy (HCM) vs athlete's heart is challenging in individuals with mild-moderate left-ventricular hypertrophy. This study aimed to assess ECG and echocardiographic parameters proposed for the differential diagnosis of HCM. The study included 75 men in three groups: control (n=30), "gray zone" athletes with interventricular septum (IVS) measuring 13-15mm (n=25) and HCM patients with IVS of 13-18mm (n=20). The most significant differences were found in relative septal thickness (RST), calculated as the ratio of 2 x IVS to left ventricle end-diastolic diameter (LV-EDD) (0.37, 0.51, 0.71, respectively; p<0.01) and in spatial QRS-T angle as visually estimated (9.8, 33.6, 66.2, respectively; p<0.01). The capacity for differential HCM diagnosis of each of the 5 criteria was assessed using the area under the curve (AUC), as follows: LV-EDD<54 (0.60), family history (0.61), T-wave inversion (TWI) (0.67), spatial QRS-T angle>45 (0.75) and RST>0.54 (0.92). Pearson correlation between spatial QRS-T angle>45 and TWI was 0.76 (p 0.01). The combination of spatial QRS-T angle>45 and RST>0.54 for diagnosis of HCM had an AUC of 0.79. The best diagnostic criteria for HCM was RST>0.54. The spatial QRS-T angle>45 did not add sensitivity if TWI was present. No additional improvement in differential diagnosis was obtained by combining parameters.

A review of beat-to-beat vectorcardiographic (VCG) parameters for analyzing repolarization variability in ECG signals

Elevated ventricular repolarization lability is believed to be linked to the risk of ventricular tachycardia/ventricular fibrillation. However, ventricular repolarization is a complex electrical phenomenon, and abnormalities in ventricular repolarization are not completely understood. To evaluate repolarization lability, vectorcardiography (VCG) is an alternative approach where the electrocardiographic (ECG) signal can be considered as possessing both magnitude and direction. Recent research has shown that VCG is advantageous over ECG signal analysis for identification of repolarization abnormality. One of the key reasons is that the VCG approach does not rely on exact identification of the T-wave offset, which improves the reproducibility of the VCG technique. However, beat-to-beat variability in VCG is an emerging area for the investigation of repolarization abnormality though not yet fully realized. Therefore, the purpose of this review is to explore the techniques, findings, and efficacy of beat-to-beat VCG parameters for analyzing repolarization lability, which may have potential utility for further study.

Changes of Virtual Planar QRS and T Vectors Derived from Holter in the Populations with and without Diabetes Mellitus

Aims

Research related to type 2 diabetes mellitus (DM) and parameters of electrocardiography (ECG) was limited. Patients with and without DM (NDM) were randomly enrolled in a study to exploit the influence of DM on planar QRS and T vectors derived from the Virtual Holter process.

Methods

A total of 216 (NDM) and 127 DM patients were consecutively and randomly recruited. We selected a 1‐minute length of ECG, which was scheduled for analysis at 4 AM. After a series of calculating algorisms, we received the virtual planar vector parameters.

Results

Patients with DM were elderly (65.61 ± 12.08 vs 59.41 ± 16.86 years, P < 0.001); higher morbidity of hypertension (76.38% vs 58.14%, P < 0.001) and coronary artery disease (44.09% vs 32.41%, P = 0.03); thicker interventricular septum (10.92 ± 1.77 vs 10.08 ± 1.96 mm, P < 0.001) and left ventricular posterior wall (9.84 ± 1.38 vs 9.39 ± 1.66 mm, P = 0.03); higher lipid levels and average heart rate (66.67 ± 12.04 vs 61.87 ± 13.36 bpm, P < 0.01); higher angle of horizontal QRS vector (HQRSA, –2.87 ± 48.48 vs –19.00 ± 40.18 degrees, P < 0.01); lower maximal magnitude of horizontal T vector (HTV, 2.33 ± 1.47 vs 2.88 ± 1.89 mm, P = 0.01) and maximal magnitude of right side T vector (2.77 ± 1.55 vs 3.27 ± 1.92 mm, P = 0.03), and no difference in angle of frontal QRS‐T vector (FQRSTA, 32.77 ± 54.20 vs 28.39 ± 52.87 degrees, P = 0.74) compared with patients having NDM. After adjusting for confounding factors, DM was significantly effective on FQRSTA (regression coefficient –40.0, 95%CI –66.4 to –13.6, P < 0.01), HQRSA (regression coefficient 22.6, 95%CI 2.5 to 42.8, P = 0.03), and HTV (regression coefficient 0.9, 95%CI 0.2 to 1.7, P = 0.01). Confounding factors included: sex, 2‐hour postprandial blood glucose, smoking, triglyceride, apolipoprotein A, creatinine, left ventricular ejection fraction, and average heart rate.

Conclusions

The risk factors of DM and lipid metabolism abnormality particularly apolipoprotein A significantly modified parameters of virtual planar QRS and T vector, including frontal QRS‐T angle.

Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification

Hypertrophic cardiomyopathy (HCM) is a cardiovascular disease where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. A test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of HCM patients. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-second, 12-lead ECG signals. Patients are classified as having HCM if the majority of their recorded heartbeats are recognized as characteristic of HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features—both commonly used and newly-developed ones—from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. We also compared the performance of these two classifiers to that obtained by a logistic regression classifier, and the first two methods performed better than logistic regression. The patient-classification precision of random forests and of support vector machine classifiers is close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 264 highly informative features can achieve performance measures comparable to those achieved by using the complete set of features.

Identifying Hypertrophic Cardiomyopathy Patients by Classifying Individual Heartbeats from 12-lead ECG Signals

Test based on electrocardiograms (ECG) that record the heart electrical activity can help in early detection of patients with hypertrophic cardiomyopathy (HCM) where the heart muscle is partially thickened and blood flow is (potentially fatally) obstructed. This paper presents a cardiovascular-patient classifier we developed to identify HCM patients using standard 10-seconds, 12-lead ECG signals. Patients are classified as having HCM if the majority of the heartbeats are recognized as HCM. Thus, the classifier's underlying task is to recognize individual heartbeats segmented from 12-lead ECG signals as HCM beats, where heartbeats from non-HCM cardiovascular patients are used as controls. We extracted 504 morphological and temporal features - both commonly used and newly-developed ones - from ECG signals for heartbeat classification. To assess classification performance, we trained and tested a random forest classifier and a support vector machine classifier using 5-fold cross validation. The patient-classification precision and F-measure of both classifiers are close to 0.85. Recall (sensitivity) and specificity are approximately 0.90. We also conducted feature selection experiments by gradually removing the least informative features; the results show that a relatively small subset of 304 highly informative features can achieve performance measures comparable to that achieved by using the complete set of features.

Deployment of remote advanced electrocardiography for improved cardiovascular risk assessment in career firefighters

INTRODUCTION

Firefighters perform strenuous muscular work while wearing heavy, encapsulating personal protective equipment in high temperature environments, under chaotic and emotionally stressful conditions. These factors can precipitate sudden cardiac events in firefighters with underlying cardiovascular disease. The purpose of this pilot study was to deploy and explore the feasibility of the resting "advanced" 12-lead electrocardiogram (A-ECG) as a remote firefighter risk assessment tool for improved assessment of cardiac risk.

MATERIALS AND METHODS

Conventional 12-lead resting electrocardiograms (ECGs) were collected for 5 min by using high-fidelity PC-based ECG hardware and software while subjects (n=21) rested comfortably. Raw data from the ECG system were securely transported via a secure network to a server where they were archived and processed. Authorized personnel performed both conventional ECG and A-ECG analyses from each digital recording, generating A-ECG "scores" in a blinded fashion. A separate cohort of firefighters (n=6) was trained to administer the A-ECG and rated the system's usability and frequency of technical problems.

RESULTS

Of the 21 uniformed personnel who completed testing, only 1 had a positive A-ECG score for coronary artery disease, which was subsequently confirmed by a cardiologist. All other subjects were classified as healthy by A-ECG. Firefighters trained to administer the A-ECG responded favorably in rating the usability of the system.

CONCLUSIONS

We have demonstrated that a new technology, A-ECG, can be deployed for remote firefighter risk assessment being performed by firefighters themselves and interpreted centrally. This simple, time- and cost-effective approach can help identify individuals potentially at increased risk for line-of-duty death due to underlying cardiovascular disease.

Predicting "heart age" using electrocardiography

Knowledge of a patient's cardiac age, or "heart age", could prove useful to both patients and physicians for better encouraging lifestyle changes potentially beneficial for cardiovascular health. This may be particularly true for patients who exhibit symptoms but who test negative for cardiac pathology. We developed a statistical model, using a Bayesian approach, that predicts an individual's heart age based on his/her electrocardiogram (ECG). The model is tailored to healthy individuals, with no known risk factors, who are at least 20 years old and for whom a resting ~5 min 12-lead ECG has been obtained. We evaluated the model using a database of ECGs from 776 such individuals. Secondarily, we also applied the model to other groups of individuals who had received 5-min ECGs, including 221 with risk factors for cardiac disease, 441 with overt cardiac disease diagnosed by clinical imaging tests, and a smaller group of highly endurance-trained athletes. Model-related heart age predictions in healthy non-athletes tended to center around body age, whereas about three-fourths of the subjects with risk factors and nearly all patients with proven heart diseases had higher predicted heart ages than true body ages. The model also predicted somewhat higher heart ages than body ages in a majority of highly endurance-trained athletes, potentially consistent with possible fibrotic or other anomalies recently noted in such individuals.

Open access integrated therapeutic and diagnostic platforms for personalized cardiovascular medicine

It is undeniable that the increasing costs in healthcare are a concern. Although technological advancements have been made in healthcare systems, the return on investment made by governments and payers has been poor. The current model of care is unsustainable and is due for an upgrade. In developed nations, a law of diminishing returns has been noted in population health standards, whilst in the developing world, westernized chronic illnesses, such as diabetes and cardiovascular disease have become emerging problems. The reasons for these trends are complex, multifactorial and not easily reversed. Personalized medicine has the potential to have a significant impact on these issues, but for it to be truly successful, interdisciplinary mass collaboration is required. We propose here a vision for open-access advanced analytics for personalized cardiac diagnostics using imaging, electrocardiography and genomics.

Conventional QT variability measurement vs. template matching techniques: comparison of performance using simulated and real ECG

Increased beat-to-beat variability in the QT interval (QTV) of ECG has been associated with increased risk for sudden cardiac death, but its measurement is technically challenging and currently not standardized. The aim of this study was to investigate the performance of commonly used beat-to-beat QT interval measurement algorithms. Three different methods (conventional, template stretching and template time shifting) were subjected to simulated data featuring typical ECG recording issues (broadband noise, baseline wander, amplitude modulation) and real short-term ECG of patients before and after infusion of sotalol, a QT interval prolonging drug. Among the three algorithms, the conventional algorithm was most susceptible to noise whereas the template time shifting algorithm showed superior overall performance on simulated and real ECG. None of the algorithms was able to detect increased beat-to-beat QT interval variability after sotalol infusion despite marked prolongation of the average QT interval. The QTV estimates of all three algorithms were inversely correlated with the amplitude of the T wave. In conclusion, template matching algorithms, in particular the time shifting algorithm, are recommended for beat-to-beat variability measurement of QT interval in body surface ECG. Recording noise, T wave amplitude and the beat-rejection strategy are important factors of QTV measurement and require further investigation.

Electrocardiogram screening for disorders that cause sudden cardiac death in asymptomatic children: a meta-analysis

BACKGROUND AND OBJECTIVES

Pediatric sudden cardiac death (SCD) occurs in an estimated 0.8 to 6.2 per 100 000 children annually. Screening for cardiac disorders causing SCD in asymptomatic children has public appeal because of its apparent potential to avert tragedy; however, performance of the electrocardiogram (ECG) as a screening tool is unknown. We estimated (1) phenotypic (ECG- or echocardiogram [ECHO]-based) prevalence of selected pediatric disorders associated with SCD, and (2) sensitivity, specificity, and predictive value of ECG, alone or with ECHO.

METHODS

We systematically reviewed literature on hypertrophic cardiomyopathy (HCM), long QT syndrome (LQTS), and Wolff-Parkinson-White syndrome, the 3 most common disorders associated with SCD and detectable by ECG.

RESULTS

We identified and screened 6954 abstracts, yielding 396 articles, and extracted data from 30. Summary phenotypic prevalences per 100 000 asymptomatic children were 45 (95% confidence interval [CI]: 10-79) for HCM, 7 (95% CI: 0-14) for LQTS, and 136 (95% CI: 55-218) for Wolff-Parkinson-White. The areas under the receiver operating characteristic curves for ECG were 0.91 for detecting HCM and 0.92 for LQTS. The negative predictive value of detecting either HCM or LQTS by using ECG was high; however, the positive predictive value varied by different sensitivity and specificity cut-points and the true prevalence of the conditions.

CONCLUSIONS

Results provide an evidence base for evaluating pediatric screening for these disorders. ECG, alone or with ECHO, was a sensitive test for mass screening and negative predictive value was high, but positive predictive value and false-positive rates varied.

Advanced electrocardiographic parameters change with severity of mitral regurgitation in Cavalier King Charles Spaniels in sinus rhythm

BACKGROUND

Multiple advanced resting ECG (A-ECG) techniques have improved the diagnostic or prognostic value of ECG in detecting human cardiac diseases even before onset of clinical signs or changes in conventional ECG.

OBJECTIVE

To determine which A-ECG parameters, derived from 12-lead A-ECG recordings, change with severity of mitral regurgitation (MR) caused by myxomatous mitral valve disease (MMVD) in Cavalier King Charles Spaniels (CKCSs) in sinus rhythm.

ANIMALS

Seventy-six privately owned CKCSs.

METHODS

Dogs were prospectively divided into 5 groups according to the degree of MR (estimated by color Doppler mapping as the percentage of the left atrial area affected by the MR jet) and presence of clinical signs. High fidelity approximately 5-minute 12-lead ECG recordings were evaluated using custom software to calculate multiple conventional and A-ECG parameters.

RESULTS

Nineteen of 76 ECG parameters were significantly different (P < .05) across the 5 dog groups. A 4-parameter model that incorporated results from 1 parameter of heart rate variability, 2 parameters of QT variability, and 1 parameter of QRS amplitude was identified that explained 82.4% of the variance with a correlation coefficient (R) of 0.60 (P < .01). When age or murmur grade was included in the statistical model the prediction value further increased the R to 0.74 and 0.85 (P < .01), respectively.

CONCLUSION

In CKCSs with sinus rhythm, 4 selected A-ECG parameters further improve prediction of MR jet severity beyond age and murmur grade, although the predictive increment in this study probably is not sufficient to warrant utilization in clinical veterinary practice.

Diagnostic utility of the spatial versus individual planar QRS-T angles in cardiac disease detection

INTRODUCTION

We compared the diagnostic utility of various planar QRS-T angles to that of the spatial QRS-T angle in detecting various cardiac diseases.

MATERIALS AND METHODS

Electrocardiographic (ECG) and derived vectorcardiographic (VCG) data were analyzed from 370 patients with imaging-proven cardiac disease (coronary artery disease, hypertrophic cardiomyopathy, or left ventricular systolic dysfunction) and 210 apparently healthy controls. The areas under the curve (AUC) of the Receiver Operating Characteristic (ROC) for distinguishing cardiac health from disease for each disease condition were statistically compared for the spatial mean QRS-T angle versus the ECG-derived frontal and VCG-derived frontal, left sagittal and horizontal planar QRS-T angles.

RESULTS

The AUC ROC of the spatial mean QRS-T angle, which ranged from 0.801 ± 0.035 to 0.987 ± 0.007 depending on the specific comparison, was always significantly greater than that of the ECG frontal planar QRS-T angle (range from 0.680 ± 0.043 to 0.796 ± 0.045) and usually significantly greater than that of all other QRS-T angles for the diseases studied.

DISCUSSION

The spatial mean QRS-T angle is statistically significantly more diagnostically powerful than the ECG-derived frontal planar QRS-T angle and also generally more diagnostically powerful than all VCG-derived planar QRS-T angles in detecting cardiac disease. The ECG frontal planar QRS-T angle should not be considered an adequate diagnostic substitute for the spatial QRS-T angle.