Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data.

Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.

Ischemic Pre-Conditioning is Present in Patients with Non-ST Elevation Myocardial Infarction and ECG Derived Moderate Obstructive Sleep Apnea.

Background: Obstructive Sleep Apnea (OSA) is associated with an increased risk of cardiovascular events, including Acute Coronary Syndrome (ACS). There is conflicting evidence that suggests OSA has a cardioprotective effect (i.e., lower troponin), via ischemic pre-conditioning, in patients with ACS. Purpose: This study had two aims: (1) compare peak troponin between non-ST elevation (NSTE) ACS patients with and without moderate OSA identified using a Holter derived respiratory disturbance index (HDRDI); and (2) determine the frequency of transient myocardial ischemia (TMI) between NSTE-ACS patients with and without moderate HDRDI. Method: This was a secondary analysis. OSA events were identified from 12-lead ECG Holter recordings using QRSs, R-R intervals, and the myogram. Moderate OSA was defined as an HDRDI ≥15 events per/hour. TMI was defined as ≥1 millimeter of ST-segment ↑ or ↓, in ≥ 1 ECG lead, ≥ 1 minute. Results: In 110 NSTE-ACS patients, 39% (n=43) had moderate HDRDI. Peak troponin was higher in patients with moderate HDRDI (6.8 ng/ml yes vs. 10.2 ng/ml no; p=0.037). There was a trend for fewer TMI events, but there were no differences (16% yes vs. 30% no; p=0.081). Conclusions: NSTE-ACS patients with moderate HDRDI have less cardiac injury than those without moderate HDRDI measured using a novel ECG derived method. These findings corroborate prior studies suggesting a possible cardioprotective effect of OSA in ACS patients via ischemic pre-condition. There was a trend for fewer TMI events in moderate HDRDI patients, but there was no statistical difference. Future research should explore the underlying physiologic mechanisms of this finding.

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.

The J to T-peak interval as a biomarker in drug safety studies: A method of accuracy assessment applied to two algorithms.

OBJECTIVES: To evaluate performance of J-to-T-peak (JTP) measurements of 12-lead ECGs, in a five-arm study using drugs with various levels of electrolyte channel block. METHODS: The novel evaluation method distinguishes between different aspects of measurement. "Random noise" is the variability among repeated measurements made without changing the conditions. "Context noise" is the variability of changes in context of the measurement, e.g. T-wave morphology, autonomic nervous system state. RESULTS: The average random noise of our RR-corrected JTPc measurements in standard deviations was 3.0 ms and not dependent on the drug. The average context noise was 4.0 ms for ranolazine, verapamil, and placebo, and 8.8 ms for dofetilide and quinidine. Measurement consistency is corroborated by linear fit confidence intervals of baseline- and placebo-corrected JTPc versus drug concentration. CONCLUSIONS: Systematic differences were found in JTPc drug response between the Mortara method and published data. Residual signal component in the context noise may influence future study design.

The development and validation of an early warning system to prevent the acquisition of 12-lead resting ECGs with interchanged electrode positions.

BACKGROUND: A fraction of routine resting ECG's are taken with electrode positions interchanged, leading to possible clinical misinterpretation. OBJECTIVE: Develop and test a method to detect and prevent electrode reversals at the electrocardiograph before the ECG is acquired. METHOD: The algorithm is based on QRS axis and P amplitudes for limb electrode reversals, and P-Q-RS amplitude distances to detect chest electrode reversals. The evaluation method involved a large (>18,000) hospital database for which serial ECG's were available and was based on simulated juxtapositions. RESULTS: The 7 most common lead reversals could be detected with a specificity of 99.8% per type and an average sensitivity of 90%, excluding LA-LL reversal (22% sensitivity). DISCUSSION: Results are similar to retrospective studies that used smaller, more homogeneous datasets. CONCLUSION: The early warning system reduces the ECG's recorded with reversal by 80%, at the price of a modest false alert rate of 1.4%.

Automated QT measurement and application to detection of moxifloxacin-induced changes.

BACKGROUND: Concern for drug-induced QT prolongation has caused significant investment in QT measurement to safety-test new compounds. Manual methods are expensive and time-consuming. Reliable automatic methods would be highly desirable. METHODS: Twelve-lead Holter recordings were annotated beat-to-beat by an automatic algorithm for global QRS onset and T offset. T offset was established from the time of peak T downslope plus a rate-dependent offset, analogous to the "tangent method," wherein T offset is determined by extrapolating the T downslope to an intersection with the baseline. RESULTS AND CONCLUSIONS: Variances of the beat-to-beat QT measurements were in the range 2.5-3.4 ms over three distinct databases, including a large heart failure database. Application to a moxifloxacin/placebo control database of 29 subjects showed excellent results.

ECG derived Cheyne-Stokes respiration and periodic breathing are associated with cardiorespiratory arrest in intensive care unit patients.

BACKGROUND: Cheyne-Stokes respiration and periodic breathing (CSRPB) have not been studied sufficiently in the intensive care unit setting (ICU). OBJECTIVES: To determine whether CSRPB is associated with adverse outcomes in ICU patients. METHODS: The ICU group was divided into quartiles by CSRPB (86 patients in quartile 1 had the least CSRPB and 85 patients in quartile 4 had the most CSRPB). Adverse outcomes (emergent intubation, cardiorespiratory arrest, inpatient mortality and the composite of all) were compared between patients with most CSRPB (quartile 4) and those with least CSRPB (quartile 1). RESULTS: ICU patients in quartile 4 had a higher proportion of cardiorespiratory arrests (5% versus 0%, (p=.042), and more adverse events over all (19% versus 8%, p=.041) as compared to patients in quartile 1. CONCLUSIONS: CSRPB can be measured in the ICU and it's severity is associated with adverse outcomes in critically ill patients.

A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings.

Patients with suspected acute coronary syndrome (ACS) are at risk of transient myocardial ischemia (TMI), which could lead to serious morbidity or even mortality. Early detection of myocardial ischemia can reduce damage to heart tissues and improve patient condition. Significant ST change in the electrocardiogram (ECG) is an important marker for detecting myocardial ischemia during the rule-out phase of potential ACS. However, current ECG monitoring software is vastly underused due to excessive false alarms. The present study aims to tackle this problem by combining a novel image-based approach with deep learning techniques to improve the detection accuracy of significant ST depression change. The obtained convolutional neural network (CNN) model yields an average area under the curve (AUC) at 89.6% from an independent testing set. At selected optimal cutoff thresholds, the proposed model yields a mean sensitivity at 84.4% while maintaining specificity at 84.9%.