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%

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%

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

BackgroundCheyne-Stokes respiration and periodic breathing (CSRPB) have not been studied sufficiently in the intensive care unit setting (ICU).ObjectivesTo determine whether CSRPB is associated with adverse outcomes in ICU patients.MethodsThe 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).ResultsICU 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.ConclusionsCSRPB can be measured in the ICU and it's severity is associated with adverse outcomes in critically ill patients

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

BackgroundPatients 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.PurposeThis 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).MethodsThis 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).ResultsIn 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.ConclusionsSensitivity 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