Search

BACKGROUND: The 12-lead ECG plays an important role in triaging patients with symptomatic coronary artery disease, making automated ECG interpretation statements of “Acute MI” or “Acute Ischemia” crucial, especially during prehospital transport when access to physician interpretation of the ECG is limited. However, it remains unknown how automated interpretation statements correspond to adjudicated clinical outcomes during hospitalization. We sought to evaluate the diagnostic performance of prehospital automated interpretation statements to four well-defined clinical outcomes of interest: confirmed ST- segment elevation myocardial infarction (STEMI); presence of actionable coronary culprit lesions, myocardial necrosis, or any acute coronary syndrome (ACS). METHODS: An observational cohort study that enrolled consecutive patients with non-traumatic chest pain transported via ambulance. Prehospital ECGs were obtained with the Philips MRX monitor from the medical command center and re-processed using manufacturer-specific diagnostic algorithms to denote the likelihood of >>>Acute MI<<< or >>>Acute Ischemia<<

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain., Diagnosing a heart attack requires excessive testing and prolonged observation, which frequently requires hospital admission. Here the authors report a machine learning-based system based exclusively on ECG data that can help clinicians identify 37% more heart attacks during initial screening.

Background: Breast cancer is one of the most prevalent types of malignant tumors in the world. Bacteria have been linked with cancer through two mechanisms, the first, stimulating chronic inflammation, and the second, production of carcinogenic bacterial metabolites. The most common types of adenovirus in cancer patients are serotypes 7 and 11 of type B, and serotypes 1, 2, and 5 are of type C. Methods: In this study, 98 blood and 50 urine, samples were collected from women with breast cancer at the Center for Oncology and Hematology in Kirkuk city in addition to 30 blood and 20 urine samples collected from healthy persons as control. Pathogenic gram-negative bacteria were isolated, identified in addition to checking their susceptibility to antibiotics. Antibodies (IgM, IgG) of adenovirus type-7 were involved in this study by the ELISA technique. Results: In this study, 22 gram-negative bacterial species were isolated from patients with breast cancer, including (31.81% Klebsiella pneumoniae, 45.45% E. coli, 18.18% Proteus mirabilis and 4.54% Acinetobacter baumannii). Gram-negative bacteria showed high resistance to antibiotics (approximately absolute resistance) amoxicillin/clavulanic acid, ampicillin, ceftazidime, amoxicillin, cephalothin, vancomycin. Adenovirus type-7 antibodies were detected by using the ELISA technique (3.84% IgM and 91.02% IgG). Viral and bacterial co-infection were also included in this study caused by the involvement of Escherichia coli + ADV-7, P. mirabilis + ADV-7, and K. pneumoniae + ADV-7.

Background The Emergency Severity Index (ESI) is a widely used tool to triage patients in emergency departments. The ESI tool is used to assess all complaints and has significant limitation for accurately triaging patients with suspected acute coronary syndrome (ACS). Objective We evaluated the accuracy of ESI in predicting serious outcomes in suspected ACS and aimed to assess the incremental reclassification performance if ESI is supplemented with a clinically validated tool used to risk-stratify suspected ACS. Methods We used existing data from an observational cohort study of patients with chest pain. We extracted ESI scores documented by triage nurses during routine medical care. Two independent reviewers adjudicated the primary outcome, incidence of 30-day major adverse cardiac events. We compared ESI with the well-established modified HEAR/T (patient History, Electrocardiogram, Age, Risk factors, but without Troponin) score. Results Our sample included 750 patients (age, 59 ± 17 years; 43% female; 40% black). A total of 145 patients (19%) experienced major adverse cardiac event. The area under the receiver operating characteristic curve for ESI score for predicting major adverse cardiac event was 0.656, compared with 0.796 for the modified HEAR/T score. Using the modified HEAR/T score, 181 of the 391 false positives (46%) and 16 of the 19 false negatives (84%) assigned by ESI could be reclassified correctly. Conclusion The ESI score is poorly associated with serious outcomes in patients with suspected ACS. Supplementing the ESI tool with input from other validated clinical tools can greatly improve the accuracy of triage in patients with suspected ACS.

Background: Heart failure (HF) and atrial fibrillation (AF) often coexist. The hemodynamic alterations induced by AF in patients with HF are well studied; however we lack reliable and non-invasive means to study these hemodynamic alterations in ambulatory patients. We sought to evaluate the clinical utility of impedance cardiography (ICG) as a novel and non-invasive tool to evaluate cardiac hemodynamics in ambulatory patients with HF and AF. Methods: This was a single-center observational study. A convenient sample of ambulatory patients with chronic HF underwent non-invasive electrocardiogram (ECG) and hemodynamic monitoring using BioZ Dx impedance cardiographer. Hemodynamics was automatically computed and ECG data were interpreted by an independent reviewer. Results: A total of 32 patients (62 ± 14 years of age; 66% male; ejection fraction 33±13%) were enrolled. There were no baseline demographic or clinical differences between those with AF (28%) and those without AF (72%). However, patients with AF exhibited lower stroke volume (60 ± 7 vs. 89 ± 29, P = 0.008), left ventricular work (33 ± 9 vs. 45 ± 13, P = 0.016), cardiac contractility (30 ± 8 vs. 40 ± 13, P = 0.037), and arterial elasticity (13 ± 5 vs. 21 ± 5, P = 0.012), as well as higher cardiac afterload (203 ± 57 vs. 151 ± 49, P = 0.015). Conclusions: Using non-invasive ICG, we have shown that it is feasible to characterize hemodynamics in ambulatory HF patients. We show that AF compromises left ventricular function in patients with HF and is associated with excess afterload and reduced arterial elasticity. Cardiol Res. 2020;11(6):370-375 doi: https://doi.org/10.14740/cr1110

Healthcare disparities in the initial management of patients with acute coronary syndrome (ACS) exist. Yet, the complexity of interactions between demographic, social, economic, and geospatial determinants of health hinders incorporating such predictors in existing risk stratification models. We sought to explore a machine-learning-based approach to study the complex interactions between the geospatial and social determinants of health to explain disparities in ACS likelihood in an urban community. This study identified consecutive patients transported by Pittsburgh emergency medical service for a chief complaint of chest pain or ACS-equivalent symptoms. We extracted demographics, clinical data, and location coordinates from electronic health records. Median income was based on US census data by zip code. A random forest (RF) classifier and a regularized logistic regression model were used to identify the most important predictors of ACS likelihood. Our final sample included 2400 patients (age 59 ± 17 years, 47% Females, 41% Blacks, 15.8% adjudicated ACS). In our RF model (area under the receiver operating characteristic curve of 0.71 ± 0.03) age, prior revascularization, income, distance from hospital, and residential neighborhood were the most important predictors of ACS likelihood. In regularized regression (akaike information criterion = 1843, bayesian information criterion = 1912, χ2 = 193, df = 10, p

Machine learning uses historical data to make predictions about new data. It has been frequently applied in healthcare to optimise diagnostic classification through discovery of hidden patterns in data that may not be obvious to clinicians. Congenital Heart Defect (CHD) machine learning research entails one of the most promising clinical applications, in which timely and accurate diagnosis is essential. The objective of this scoping review is to summarise the application and clinical utility of machine learning techniques used in paediatric cardiology research, specifically focusing on approaches aiming to optimise diagnosis and assessment of underlying CHD. Out of 50 full-text articles identified between 2015 and 2021, 40% focused on optimising the diagnosis and assessment of CHD. Deep learning and support vector machine were the most commonly used algorithms, accounting for an overall diagnostic accuracy > 0.80. Clinical applications primarily focused on the classification of auscultatory heart sounds, transthoracic echocardiograms, and cardiac MRIs. The range of these applications and directions of future research are discussed in this scoping review.

Diabetic Retinopathy (DR) is a disease of the eye for diabetics, and it can lead to a lack of vision if leaved untreated. The proposed approach in this paper is used to help for detecting and classifying the DR. It is applied to detect non-proliferative DR by identifying micro-aneurysms and hemorrhages. Firstly, the pre-processing step is applied. It consists of extracting the green channel, removing the optic disc (OD) and normalizing the background. Then, h-maxima transformation is performed. After that, threshold segmentation is applied to detect the hemorrhages and micro-aneurysms, accurately. Finally, an area classifier is used for the classification process to discriminate dark spot lesions.

Background Delay in seeking medical treatment for suspected acute coronary syndrome can lead to negative patient outcomes. Objective Our aim was to evaluate the prevalence and predictors of delay in seeking care in high-risk chest pain patients with or without acute coronary syndrome (ACS). Methods This was a secondary analysis of an observational cohort study of patients transported by Emergency Medical Services for a chief complaint of chest pain. Important demographic and clinical characteristics were extracted from electronic health records. Two independent reviewers adjudicated the presence of ACS. Logistic regression was used to model the predictors of delay in seeking care. Results The final sample included 743 patients (99% non-Hispanic). Overall, 24% presented > 12 h from onset of symptoms. Among those with ACS (n = 115), 14% presented > 12 h after onset of symptoms. Race, smoking, diabetes, and related symptoms were associated with delayed seeking behavior. In multivariate analysis, non-Caucasian race (black or others) was the only independent predictor of > 12 h delay in seeking care (odds ratio 1.4; 95% confidence interval 1.0–1.9). Conclusions One in four patients with chest pain, including 14% of those with ACS, wait more than 12 h before seeking care. Compared to non-blacks, black patients are 40% more likely to delay seeking care > 12 h.

BACKGROUND: Nonspecific ST-T repolarization (NST) abnormalities alter the ST-segment for reasons often unrelated to acute myocardial ischemia, which could contribute to misdiagnosis or inappropriate treatment. We sought to define the prevalence of NST patterns in patients with chest pain and evaluate how such patterns correlate with the eventual etiology of chest pain and course of hospitalization. METHODS: This was a prospective observational study that included consecutive prehospital chest pain patients from three tertiary care hospitals in the U.S. Two independent reviewers blinded from clinical data audited the prehospital 12-lead ECG for the presence or absence of NST patterns (i.e., right or left bundle branch block, left ventricular hypertrophy with strain pattern, ventricular pacing, ventricular rhythm, or coarse atrial fibrillation). The primary outcome was 30-day major adverse cardiac events (MACE) defined as cardiac arrest, acute heart failure, post-discharge infarction, or all-cause death. RESULTS: The final sample included 750 patients (age 59±17, 58% males). A total of 40 patients (5.3%) experienced 30-MACE and 131 (17.5%) had NST patterns. The presence of NST patterns was an independent multivariate predictor of 30-day MACE (9.9% vs. 4.4%, OR = 2.2 [95% CI = 1.1–4.5]. Patients with NST patterns had increased median length of stay (1.0 [IQR 0.5–3] vs. 2.0 [IQR 1–4] days, p

Introduction Appropriate prehospital (PH) triage of patients with chest pain can significantly improve outcomes in acute myocardial infarction (MI). We sought to explore how PH providers triage chest pain as high versus low risk and to evaluate the accuracy and predictors of their triage decision. Methods This was a prospective, observational cohort study that enrolled consecutive patients with chest pain transported by emergency medical services (EMS) to 3 tertiary care hospitals in the US. EMS triage decision (high risk versus low-risk) was defined based on the transmission of PH electrocardiogram (ECG) to a command center for medical consultation with or without catheter laboratory activation. Two independent reviewers examined in-hospital medical records to adjudicate the presence of acute MI and to audit the findings on the presenting ECG. Results We enrolled 2,065 patients (aged 56 ± 17, 53% male) of whom 768 (37%) were triaged as high risk. Those triaged as high risk were older, were more likely to be men or have significant cardiac history, and had a higher rate of acute MI events (14.2% versus 3.5%). The sensitivity and specificity for triaging MI events as high risk were 70% and 97%, respectively. A total of 46/155 (30%) MI events were misclassified as low risk. No previous coronary revascularization and ECG misinterpretation were strong independent predictors of such undertriage. Conclusions PH providers have moderate sensitivity in triaging high-risk patients; 1 in 3 MI events are undertriaged. Emergency nurses need to pay special attention to patients with benign past histories during transition of care and should always reinterpret ECGs for subtle ischemic changes.

Background The volume of regional denervated myocardium (D-M) on positron emission tomography has been recently suggested as a strong independent predictor of cause-specific mortality from sudden cardiac arrest (SCA) in chronic heart failure. We sought to evaluate whether ECG indices of global autonomic function predict risk of SCA to a similar degree as regional D-M. Methods Subjects enrolled in the Prediction of Arrhythmic Events using Positron Emission Tomography (PAREPET) study were included in this study. Patients completed a 24-hour Holter ECG at enrollment and were followed up at 3-month intervals. SCA events were adjudicated by two board-certified cardiologists. Other cardiovascular death events were classified as nonsudden cardiac death (NSCD). Eight measures of heart rate variability were analyzed: SDNN, RMSSD, low-frequency (LF) and high-frequency (HF) power, heart rate turbulence onset and slope, and acceleration and deceleration capacity. We used competing risk regression to delineate cause-specific mortality from SCA versus NSCD. Results Our sample included 127 patients (age 67 ± 12, 92% male). After a median follow-up of 4.1 years, there were 22 (17%) adjudicated SCA and 18 (14%) adjudicated NSCD events. In multivariate Cox-regression, LF power was the only HRV parameter to predict time-to-SCA. However, in competing risk analysis, reduced LF power was preferentially associated with NSCD rather than SCA (HR = 0.92 [0.85–0.98], p = 0.019). Conclusion Depressed LF power might indicate impaired vagal reflex, which suggests that increasing vagal tone in these patients would have a protective effect against NSCD beyond that achieved by the mere slowing of heart rate using β-blockers.

Background Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion. Methods This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm). Results Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance. Conclusions Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.

Background Classical ST‐T waveform changes on standard 12‐lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal‐spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology‐driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data‐driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data‐ and physiology‐driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov ; Unique Identifier: NCT04237688.

Background: Non-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. Methods: We performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. The parent study recruited consecutive patients evaluated at the emergency department of three UPMC-affiliated tertiary care hospitals. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. Baseline ECGs were preprocessed using manufacturer specific algorithms. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. Results: Our final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%-65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R 2 = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation (VAT) time in left ventricular apex were the most important determinants of reduced LVEF (Figure), while global QRS duration was of less importance. Conclusions: Poor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

Objectives Cancer survivorship status among patients evaluated for chest pain at the emergency department (ED) warrants high degree of suspicion. However, it remains unclear whether cancer survivorship is associated with different risk of major adverse cardiac events (MACE) compared to those with no history of cancer. Furthermore, while HEART score is widely used in ED evaluation, it is unclear whether it can adequately triage chest pain events in cancer survivors. We sought to compare the rate of MACE in patients with a recent history of cancer in remission evaluated for acute chest pain at the ED to those with no history of cancer, and compare the performance of a common chest pain risk stratification score (HEART) between the two groups. Methods We performed a secondary analysis of a prospective observational cohort study of chest pain patients presenting to the EDs of three tertiary care hospitals in the USA. Cancer survivorship status, HEART scores, and the presence of MACE within 30 days of admission were retrospectively adjudicated from the charts. We defined patients with recent history of cancer in remission as those with a past history of cancer of less than 10 years, and currently cured or in remission. Results The sample included 750 patients (age: 59 ± 17; 42% females, 40% Black), while 69 patients (9.1%) had recent history of cancer in remission. A cancer in remission status was associated with a higher comorbidity burden, older age, and female sex. There was no difference in risk of MACE between those with a cancer in remission and their counterparts in both univariate [17.4 vs. 19.5%, odds ratio (OR) = 0.87 (95% confidence interval (CI), 0.45-1.66], P = 0.67] and multivariable analysis adjusting for demographics and comorbidities [OR = 0.62 (95% CI, 0.31-1.25), P = 0.18]. Patients with cancer in remission had higher HEART score (4.6 ± 1.8 vs. 3.9 ± 2.0, P = 0.006), and a higher proportion triaged as intermediate risk [68 vs. 56%, OR = 1.67 (95% CI, 1.00-2.84), P = 0.05]; however, no difference in the performance of HEART score existed between the groups (area under the curve = 0.86 vs. 0.84, P = 0.76). Conclusions There was no difference in rate of MACE between those with recent history of cancer in remission compared to their counterparts. A higher proportion of patients with cancer in remission was triaged as intermediate risk by the HEART score, but we found no difference in the performance of the HEART score between the groups.

Introduction HEART score is widely used to stratify patients with chest pain in the emergency department but has never been validated for cocaine-associated chest pain (CACP). We sought to evaluate the performance of HEART score in risk stratifying patients with CACP compared to an age- and sex-matched cohort with non-CACP. Methods The parent study was an observational cohort study that enrolled consecutive patients with chest pain. We identified patients with CACP and age/sex matched them to patients with non-CACP in 1:2 fashion. HEART score was calculated retrospectively from charts. The primary outcome was major adverse cardiac events (MACE) within 30 days of indexed encounter. Results We included 156 patients with CACP and 312 age-and sex-matched patients with non-CACP (n = 468, mean age 51 ± 9, 22% females). There was no difference in rate of MACE between the groups (17.9% vs. 15.7%, p = 0.54). Compared to the non-CACP group, the HEART score had lower classification performance in those with CACP (AUC = 0.68 [0.56–0.80] vs. 0.84 [0.78–0.90], p = 0.022). In CACP group, Troponin score had the highest discriminatory value (AUC = 0.72 [0.60–0.85]) and Risk factors score had the lowest (AUC = 0.47 [0.34–0.59]). In patients deemed low-risk by the HEART score, those with CACP were more likely to experience MACE (14% vs. 4%, OR = 3.7 [1.3–10.7], p = 0.016). Conclusion In patients with CACP, HEART score performs poorly in stratifying risk and is not recommended as a rule out tool to identify those at low risk of MACE.

Emergency department (ED) nurses need to identify patients with potential acute coronary syndrome (ACS) rapidly because treatment delay could impact patient outcomes. Aims of this secondary analysis were to identify key patient factors that could be available at initial ED nurse triage that predict ACS. Consecutive patients with chest pain who called 9-1-1, received a 12-lead electrocardiogram in the prehospital setting, and were transported via emergency medical service were included in the study. A total of 750 patients were recruited. The sample had an average age of 59 years old, was 57% male, and 40% Black. One hundred and fifteen patients were diagnosed with ACS. Older age, non-Caucasian race, and faster respiratory rate were independent predictors of ACS. There was an interaction between heart rate by Type II diabetes receiving insulin in the context of ACS. Type II diabetics requiring insulin for better glycemic control manifested a faster heart rate. By identifying patient factors at ED nurse triage that could be predictive of ACS, accuracy rates of triage may improve, thus impacting patient outcomes.

Contains fulltext : 219845.pdf (Publisher’s version ) (Closed access) Epidemiological and biological plausibility studies support a cause-and-effect relationship between increased levels of physical activity or cardiorespiratory fitness and reduced coronary heart disease events. These data, plus the well-documented anti-aging effects of exercise, have likely contributed to the escalating numbers of adults who have embraced the notion that "more exercise is better." As a result, worldwide participation in endurance training, competitive long distance endurance events, and high-intensity interval training has increased markedly since the previous American Heart Association statement on exercise risk. On the other hand, vigorous physical activity, particularly when performed by unfit individuals, can acutely increase the risk of sudden cardiac death and acute myocardial infarction in susceptible people. Recent studies have also shown that large exercise volumes and vigorous intensities are both associated with potential cardiac maladaptations, including accelerated coronary artery calcification, exercise-induced cardiac biomarker release, myocardial fibrosis, and atrial fibrillation. The relationship between these maladaptive responses and physical activity often forms a U- or reverse J-shaped dose-response curve. This scientific statement discusses the cardiovascular and health implications for moderate to vigorous physical activity, as well as high-volume, high-intensity exercise regimens, based on current understanding of the associated risks and benefits. The goal is to provide healthcare professionals with updated information to advise patients on appropriate preparticipation screening and the benefits and risks of physical activity or physical exertion in varied environments and during competitive events.

BACKGROUND: Non-invasive screening tools of cardiac function can play a significant role in the initial triage of patients with suspected acute coronary syndrome. Numerous ECG features have been previously linked with cardiac contractility in the general population. We sought to identify ECG features that are most predictive for real-time screening of reduced left ventricular ejection fraction (LVEF) in the acute care setting. METHODS: We performed a secondary analysis of a prospective, observational cohort study of patients evaluated for suspected acute coronary syndrome. We included consecutive patients in whom an echocardiogram was performed during indexed encounter. We evaluated 554 automated 12-lead ECG features in multivariate linear regression for predicting LVEF. We then used regression trees to identify the most important predictive ECG features. RESULTS: Our final sample included 297 patients (aged 63 ± 15, 45% females). The mean LVEF was 57% ± 13 (IQR 50%–65%). In multivariate analysis, depolarization dispersion in the horizontal plane; global repolarization dispersion; and abnormal temporal indices in inferolateral leads were all independent predictors of LVEF (R(2) = 0.452, F = 6.679, p < 0.001). Horizontal QRS axis deviation and prolonged ventricular activation time in left ventricular apex were the most important determinants of reduced LVEF, while global QRS duration was of less importance. CONCLUSIONS: Poor R wave progression in precordial leads with dominant QS pattern in V3 is the most predictive feature of reduced LVEF in suspected ACS. This feature constitutes a simple visual marker to aid clinicians in identifying those with impaired cardiac function.

Highly pathogenic avian influenza A(H5N8) viruses have been detected in several continents. However, limited viral sequence data are available from countries in the Middle East. We report full-genome analyses of highly pathogenic H5N8 viruses recently detected in different provinces in Saudi Arabia.

Background: A prolonged corrected QT (QTc) interval is a known risk factor for adverse cardiac events. Understanding the determinants and physiologic correlates of QTc is necessary for selecting proper strategies to reduce the risk of adverse events in high-risk patients. We sought to evaluate the role of arterial stiffness in heart failure as a determinant of QTc prolongation. Method: This was an observational study that recruited ambulatory heart failure patients (New York Heart Association Classes I–II) from an outpatient heart failure clinic. In the supine resting position, consented patients underwent noninvasive 12-lead electrocardiograph (ECG) and hemodynamic monitoring using BioZ Dx impedance cardiography. ECGs were evaluated by a reviewer blinded to clinical data, and QTc interval was automatically computed. Patients with pacing or bundle branch block (BBB) were analyzed separately. Strengths of associations were evaluated using Pearson’s r coefficients and multivariate linear regression. Results: The final sample ( N = 44) was 62 ± 13 years of age and 64% male with ejection fraction of 34% ± 12%. At univariate level, QTc interval moderately ( r > .50) correlated with cardiac output, left cardiac work index, systemic vascular resistance, and total arterial compliance in patients with intrinsically narrow QRS complexes. At the multivariate level, increasing systemic vascular resistance and decreasing total arterial compliance remained independent predictors of widening QTc interval in this group ( R2 = .54). No significant correlations were seen in patients with pacing or BBB. Conclusions: In the absence of conduction abnormalities, magnitude of arterial stiffness, an indirect measure of endothelial dysfunction, is associated with QTc interval prolongation.

Mild-to-moderate ischemia does not result in ST segment elevation on the electrocardiogram (ECG), but rather non-specific changes in the T wave, which are frequently labeled as non-diagnostic for ischemia. Robust methods to quantify such T wave heterogeneity can have immediate clinical applications. We sought to evaluate the effects of spontaneous ischemia on the evolution of spatial T wave changes, based on the eigenvalues of the spatial correlation matrix of the ECG, in patients undergoing nuclear cardiac imaging for evaluating intermittent chest pain. We computed T wave complexity (TWC), the ratio of the second to the first eigenvalue of repolarization, from 5-min baseline and 5-min peak-stress Holter ECG recordings. Our sample included 30 males and 20 females aged 63 ± 11 years. Compared to baseline, significant changes in TWC were only seen in patients with ischemia (n = 10) during stress testing, but not among others. The absolute changes in TWC were significantly larger in the ischemia group compared to others, with a pattern that seemed to depend on the severity or anatomic distribution of ischemia. Our results demonstrate that ischemia-induced changes in T wave morphology can be meaningfully quantified from the surface 12-lead ECG, suggesting an important opportunity for improving diagnostics in patients with chest pain.