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Detection of unexpected ischaemia due to left main disease during tele-rehabilitation using 12-lead electrocardiogram monitoring: a case report.

ABSTRACT: BACKGROUND:Cardiac rehabilitation (CR) reduces mortality and improves quality of life. Unfortunately, participation in CR remains low and studies have examined the use of home-based tele-monitoring to improve participation in CR. These studies generally utilized single- or three-lead electrocardiogram (ECG) channels with limited sensitivity to detect ischaemic changes. In this report, we describe detection of unexpected, significant left main coronary disease in a patient participating in a home CR programme using a unique 12-lead ECG monitoring system. CASE SUMMARY:A 68-year-old man was referred for CR after acute coronary syndrome and stent implantation to the obtuse marginal. Three months following this intervention, he began complaining of chest pain. Repeat coronary angiogram showed a borderline lesion in the left main not felt to be clinically significant. The patient performed CR and was monitored with the Master Caution® System, a digital platform including a garment with 10 textile electrodes (Master Caution garment) configuring a device which enables 12 lead ECG. While being monitored, the ECG technician detected 1-2?mm ST-depression in leads 1, L V2-V5. The patient was asymptomatic at the time the ST-depressions were noted. Repeat angiography revealed a significant increase in the severity of the left main lesion and the patient was referred for bypass surgery. DISCUSSION:This life-threatening diagnosis could easily have been missed using conventional ECG monitoring and our case demonstrates the potential clinical utility of tele-monitoring with a 12-lead digital home ECG monitoring device.

SUBMITTER: Katzburg S

PROVIDER: S-EPMC6601168 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Detection of unexpected ischaemia due to left main disease during tele-rehabilitation using 12-lead electrocardiogram monitoring: a case report.

Katzburg Sara S Dor-Haim Horesh H Weiss Abraham Teddy AT Leibowitz David D

European heart journal. Case reports 20190601 2

<h4>Background</h4>Cardiac rehabilitation (CR) reduces mortality and improves quality of life. Unfortunately, participation in CR remains low and studies have examined the use of home-based tele-monitoring to improve participation in CR. These studies generally utilized single- or three-lead electrocardiogram (ECG) channels with limited sensitivity to detect ischaemic changes. In this report, we describe detection of unexpected, significant left main coronary disease in a patient participating i ...[more]

PMID: 31449641

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Project description:Abstract Background We have demonstrated that a neural network is able to predict a person’s age from the electrocardiogram (ECG) [artificial intelligence (AI) ECG age]. However, some discrepancies were observed between ECG-derived and chronological ages. We assessed whether the difference between AI ECG and chronological age (Age-Gap) represents biological ageing and predicts long-term outcomes. Methods and results We previously developed a convolutional neural network to predict chronological age from ECGs. In this study, we used the network to analyse standard digital 12-lead ECGs in a cohort of 25 144 subjects ≥30 years who had primary care outpatient visits from 1997 to 2003. Subjects with coronary artery disease, stroke, and atrial fibrillation were excluded. We tested whether Age-Gap was correlated with total and cardiovascular mortality. Of 25 144 subjects tested (54% females, 95% Caucasian) followed for 12.4 ± 5.3 years, the mean chronological age was 53.7 ± 11.6 years and ECG-derived age was 54.6 ± 11 years (R2 = 0.79, P < 0.0001). The mean Age-Gap was small at 0.88 ± 7.4 years. Compared to those whose ECG-derived age was within 1 standard deviation (SD) of their chronological age, patients with Age-Gap ≥1 SD had higher all-cause and cardiovascular disease (CVD) mortality. Conversely, subjects whose Age-Gap was ≤1 SD had lower all-cause and CVD mortality. Results were unchanged after adjusting for CVD risk factors and other survival influencing factors. Conclusion The difference between AI ECG and chronological age is an independent predictor of all-cause and cardiovascular mortality. Discrepancies between these possibly reflect disease independent biological ageing. Graphical Abstract In subjects without baseline cardiovascular disease, an older artificial intelligence (AI)-derived electrocardiogram (ECG) Age (AI-ECG age) compared to chronological age was associated with increased all-cause and cardiovascular mortality. Subjects, where the reverse was true, had lower mortality. Discrepancies between chronological age and ECG-derived age possibly reflect disease independent biological ageing.

Project description:OBJECTIVE:This study aims to validate the 12-lead electrocardiogram (ECG) as a biometric modality based on two straightforward binary QRS template matching characteristics. Different perspectives of the human verification problem are considered, regarding the optimal lead selection and stability over sample size, gender, age, heart rate (HR). METHODS:A clinical 12-lead resting ECG database, including a population of 460 subjects with two-session recordings (>1 year apart) is used. Cost-effective strategies for extraction of personalized QRS patterns (100ms) and binary template matching estimate similarity in the time scale (matching time) and dissimilarity in the amplitude scale (mismatch area). The two-class person verification task, taking the decision to validate or to reject the subject identity is managed by linear discriminant analysis (LDA). Non-redundant LDA models for different lead configurations (I,II,III,aVF,aVL,aVF,V1-V6) are trained on the first half of 230 subjects by stepwise feature selection until maximization of the area under the receiver operating characteristic curve (ROC AUC). The operating point on the training ROC at equal error rate (EER) is tested on the independent dataset (second half of 230 subjects) to report unbiased validation of test-ROC AUC and true verification rate (TVR = 100-EER). The test results are further evaluated in groups by sample size, gender, age, HR. RESULTS AND DISCUSSION:The optimal QRS pattern projection for single-lead ECG biometric modality is found in the frontal plane sector (60°-0°) with best (Test-AUC/TVR) for lead II (0.941/86.8%) and slight accuracy drop for -aVR (-0.017/-1.4%), I (-0.01/-1.5%). Chest ECG leads have degrading accuracy from V1 (0.885/80.6%) to V6 (0.799/71.8%). The multi-lead ECG improves verification: 6-chest (0.97/90.9%), 6-limb (0.986/94.3%), 12-leads (0.995/97.5%). The QRS pattern matching model shows stable performance for verification of 10 to 230 individuals; insignificant degradation of TVR in women by (1.2-3.6%), adults ?70 years (3.7%), younger <40 years (1.9%), HR<60bpm (1.2%), HR>90bpm (3.9%), no degradation for HR change (0 to >20bpm).

Dataset Information

Detection of unexpected ischaemia due to left main disease during tele-rehabilitation using 12-lead electrocardiogram monitoring: a case report.

Publications

Detection of unexpected ischaemia due to left main disease during tele-rehabilitation using 12-lead electrocardiogram monitoring: a case report.

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