Dynamic tracking of ischemia in the surface electrocardiogram

Personalized ECG monitoring and adaptive machine learning

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected. We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's "fingerprint" basis patterns and to detect anomalies in those patterns.

Evaluation of ECG algorithms designed to improve detect of transient myocardial ischemia to minimize false alarms in patients with suspected acute coronary syndrome

BACKGROUND

Patients hospitalized for suspected acute coronary syndrome (ACS) are at risk for transient myocardial ischemia. During the "rule-out" phase, continuous ECG ST-segment monitoring can identify transient myocardial ischemia, even when asymptomatic. However, current ST-segment monitoring software is vastly underutilized due to false positive alarms, with resultant alarm fatigue. Current ST algorithms may contribute to alarm fatigue because; (1) they are not designed with a delay (minutes), rather alarm to brief spikes (i.e., turning, heart rate changes), and (2) alarm to changes in a single ECG lead, rather than contiguous leads.

PURPOSE

This study was designed to determine sensitivity, and specificity, of ST algorithms when accounting for; ST magnitude (100μV vs 200μV), duration, and changes in contiguous ECG leads (i.e., aVL, I, - aVR, II, aVF, III; V1, V2, V3, V4, V5, V6, V6, I).

METHODS

This was a secondary analysis from the COMPARE Study, which assessed occurrence rates for transient myocardial ischemia in hospitalized patients with suspected ACS using 12-lead Holter. Transient myocardial ischemia was identified from Holter using >100μV ST-segment ↑ or ↓, in >1 ECG lead, >1min. Algorithms tested against Holter transient myocardial ischemia were done using the University of California San Francisco (UCSF) ECG algorithm and included: (1)100μV vs 200μV any lead during a 5-min ST average; (2)100μV vs 200μV any lead >5min, (3) 100μV vs 200μV any lead during a 5-min ST average in contiguous leads, and (4) 100μV vs 200μV>5min in contiguous leads (Table below).

RESULTS

In 361 patients; mean age 63+12years, 63% male, 56% prior CAD, 43 (11%) had transient myocardial ischemia. Of the 43 patients with transient myocardial ischemia, 17 (40%) had ST-segment elevation events, and 26 (60%) ST-segment depression events. A higher proportion of patients with ST segment depression has missed ischemic events. Table shows sensitivity and specificity for the four algorithms tested.

CONCLUSIONS

Sensitivity was highly variable, due to the ST threshold selected, with the 100μV measurement point being superior to the 200μV amplitude threshold. Of all the algorithms tested, there was moderate sensitivity and specificity (70% and 68%) using the 100μV ST-segment threshold, integrated ST-segment changes in contiguous leads during a 5-min average.

Intelligent use of advanced capabilities of diagnostic ECG algorithms in a monitoring environment

A large number of ST-elevation notifications are generated by cardiac monitoring systems, but only a fraction of them is related to the critical condition known as ST-segment elevation myocardial infarction (STEMI) in which the blockage of coronary artery causes ST-segment elevation. Confounders such as acute pericarditis and benign early repolarization create electrocardiographic patterns mimicking STEMI but usually do not benefit from a real-time notification. A STEMI screening algorithm able to recognize those confounders utilizing capabilities of diagnostic ECG algorithms in variation analysis of ST segments helps to avoid triggering a non-actionable ST-elevation notification. However, diagnostic algorithms are generally designed to analyze short ECG snapshots collected in low-noise resting position and hence are susceptible to high levels of noise common in a monitoring environment. We developed a STEMI screening algorithm which performs a real-time signal quality evaluation on the ECG waveform to select the segments with quality high enough for subsequent analysis by a diagnostic ECG algorithm. The STEMI notifications generated by this multi-stage STEMI screening algorithm are significantly fewer than ST-elevation notifications generated by a continuous ST monitoring strategy.

Heart rate variability measured early in patients with evolving acute coronary syndrome and 1-year outcomes of rehospitalization and mortality

OBJECTIVE

This study sought to examine the prognostic value of heart rate variability (HRV) measurement initiated immediately after emergency department presentation for patients with acute coronary syndrome (ACS).

BACKGROUND

Altered HRV has been associated with adverse outcomes in heart disease, but the value of HRV measured during the earliest phases of ACS related to risk of 1-year rehospitalization and death has not been established.

METHODS

Twenty-four-hour Holter recordings of 279 patients with ACS were initiated within 45 minutes of emergency department arrival; recordings with ≥18 hours of sinus rhythm were selected for HRV analysis (number [N] =193). Time domain, frequency domain, and nonlinear HRV were examined. Survival analysis was performed.

RESULTS

During the 1-year follow-up, 94 patients were event-free, 82 were readmitted, and 17 died. HRV was altered in relation to outcomes. Predictors of rehospitalization included increased normalized high frequency power, decreased normalized low frequency power, and decreased low/high frequency ratio. Normalized high frequency >42 ms(2) predicted rehospitalization while controlling for clinical variables (hazard ratio [HR] =2.3; 95% confidence interval [CI] =1.4-3.8, P=0.001). Variables significantly associated with death included natural logs of total power and ultra low frequency power. A model with ultra low frequency power <8 ms(2) (HR =3.8; 95% CI =1.5-10.1; P=0.007) and troponin >0.3 ng/mL (HR =4.0; 95% CI =1.3-12.1; P=0.016) revealed that each contributed independently in predicting mortality. Nonlinear HRV variables were significant predictors of both outcomes.

CONCLUSION

HRV measured close to the ACS onset may assist in risk stratification. HRV cut-points may provide additional, incremental prognostic information to established assessment guidelines, and may be worthy of additional study.

Acute coronary ischemia identified by EMS providers in a standing middle-aged male with atypical symptoms

Acute coronary syndrome and myocardial infarction have been described to present with atypical symptoms in certain subsets of patients. However, these subsets commonly do not include middle-aged males with a paucity of underlying medical conditions. We present a very unique case of acute coronary syndrome in a 53-year-old male, with no previously identified medical conditions other than chronic back pain. The patient was encountered by rural emergency medical service providers presenting with syncope followed by intermittent episodes of lightheadedness. Further, electrocardiographic changes consistent with acute ischemia could only be demonstrated with the patient in a standing position, prior to the development of an occurrence of ventricular tachycardia, which degenerated into ventricular fibrillation. To our knowledge, this is a very rare case of electrocardiographic changes consistent with occult, acute cardiac ischemia with a proven coronary artery lesion seen initially only with the patient in a standing position.

Cardiac position sensitivity study in the electrocardiographic forward problem using stochastic collocation and boundary element methods

The electrocardiogram (ECG) is ubiquitously employed as a diagnostic and monitoring tool for patients experiencing cardiac distress and/or disease. It is widely known that changes in heart position resulting from, for example, posture of the patient (sitting, standing, lying) and respiration significantly affect the body-surface potentials; however, few studies have quantitatively and systematically evaluated the effects of heart displacement on the ECG. The goal of this study was to evaluate the impact of positional changes of the heart on the ECG in the specific clinical setting of myocardial ischemia. To carry out the necessary comprehensive sensitivity analysis, we applied a relatively novel and highly efficient statistical approach, the generalized polynomial chaos-stochastic collocation method, to a boundary element formulation of the electrocardiographic forward problem, and we drove these simulations with measured epicardial potentials from whole-heart experiments. Results of the analysis identified regions on the body-surface where the potentials were especially sensitive to realistic heart motion. The standard deviation (STD) of ST-segment voltage changes caused by the apex of a normal heart, swinging forward and backward or side-to-side was approximately 0.2 mV. Variations were even larger, 0.3 mV, for a heart exhibiting elevated ischemic potentials. These variations could be large enough to mask or to mimic signs of ischemia in the ECG. Our results suggest possible modifications to ECG protocols that could reduce the diagnostic error related to postural changes in patients possibly suffering from myocardial ischemia.

The new universal definition of myocardial infarction criteria improve electrocardiographic diagnosis of acute coronary syndrome

INTRODUCTION/METHODS

To assess whether revised electrocardiographic (ECG) criteria improve emergency department identification of patients with acute myocardial infarction (MI) or unstable angina (UA) and predict outcome, we studied 120 patients with a nondiagnostic initial ECG by prior criteria. Electrocardiograms were read in a blinded fashion months apart with standard and then revised criteria, and analyzed by χ(2) and logistic regression analysis.

RESULTS

In 12 subjects (10%), the initial ECG was now interpreted as diagnostic of ischemia. Eleven (92%) had an MI, 1 had UA (8%), and none had a noncardiac diagnosis. Ischemic ECG changes were strongly associated with MI or UA (P = .003). At 1-year follow-up, ECG changes diagnostic of ischemia were associated with a trend toward higher mortality (25% vs 7%, P = .07), but after adjustment for clinical factors, ECG changes were not an independent predictor of 1-year mortality.

CONCLUSIONS

Revision of the ECG criteria for ischemia was associated with enhanced diagnostic performance and identified a subset of patients at higher risk.

Emergency physician high pretest probability for acute coronary syndrome correlates with adverse cardiovascular outcomes

OBJECTIVES

The value of unstructured physician estimate of risk for disease processes, other than acute coronary syndrome (ACS), has been demonstrated. The authors sought to evaluate the predictive value of unstructured physician estimate of risk for ACS in emergency department (ED) patients without obvious initial evidence of a cardiac event.

METHODS

This was a post hoc secondary analysis of the Internet Tracking Registry for Acute Coronary Syndromes (i*trACS), a prospectively collected multicenter data registry of patients over the age of 18 years presenting to the ED with symptoms of ACS between 1999 and 2001. In this registry, following patient history, physical exam, and electrocardiogram (ECG), the unstructured treating physician estimate of risk was recorded. A 30-day follow-up and a medical record review were used to determine rates of adverse cardiac events, death, myocardial infarction (MI), or revascularization procedure. The analysis included all patients with nondiagnostic ECG changes, normal initial biomarkers, and a non-MI initial impression from the registry and excluded those without complete data or who were lost to follow-up. Data were stratified by unstructured physician risk estimate: noncardiac, low risk, high risk, or unstable angina.

RESULTS

Of 15,608 unique patients in the registry, 10,145 met inclusion/exclusion criteria. Patients were defined as having unstable angina in 6.0% of cases; high risk, 23.5% of cases; low risk, 44.2%; and noncardiac, 26.3% of cases. Adverse cardiac event rates had an inverse relationship, decreasing from 22.0% (95% confidence interval [CI] = 18.8% to 25.6%) for unstable angina, 10.2% (95% CI = 9.0% to 11.5%) for those stratified as high risk, 2.2% (95% CI = 1.8% to 2.6%) for low risk, and to 1.8% (95% CI = 1.4% to 2.4%) for noncardiac. The relative risk (RR) of an adverse cardiac event for those with an initial label of unstable angina compared to those with a low-risk designation was 10.2 (95% CI = 8.0 to 13.0). The RR of an event for those with a high-risk initial impression compared to those with a low-risk initial impression was 4.7 (95% CI = 3.8 to 5.9). The risk of an event among those with a low-risk initial impression was the same as for those with a noncardiac initial impression (RR = 0.83, 95% CI = 0.6 to 1.2).

CONCLUSIONS

In ED patients without obvious initial evidence of a cardiac event, unstructured emergency physician (EP) estimate of risk correlates with adverse cardiac outcomes.