Risk markers of sudden cardiac death in standard 12-lead electrocardiograms

Manual vs. automatic assessment of the QT-interval and corrected QT

AIMS

Sudden cardiac death (SCD) is challenging to predict. Electrocardiogram (ECG)-derived heart rate-corrected QT-interval (QTc) is used for SCD-risk assessment. QTc is preferably determined manually, but vendor-provided automatic results from ECG recorders are convenient. Agreement between manual and automatic assessments is unclear for populations with aberrant QTc. We aimed to systematically assess pairwise agreement of automatic and manual QT-intervals and QTc.

METHODS AND RESULTS

A multi-centre cohort enriching aberrant QTc comprised ECGs of healthy controls and long-QT syndrome (LQTS) patients. Manual QT-intervals and QTc were determined by the tangent and threshold methods and compared to automatically generated, vendor-provided values. We assessed agreement globally by intra-class correlation coefficients and pairwise by Bland-Altman analyses and 95% limits of agreement (LoA). Further, manual results were compared to a novel automatic QT-interval algorithm. ECGs of 1263 participants (720 LQTS patients; 543 controls) were available [median age 34 (inter-quartile range 35) years, 55% women]. Comparing cohort means, automatic and manual QT-intervals and QTc were similar. However, pairwise Bland-Altman-based agreement was highly discrepant. For QT-interval, LoAs spanned 95 (tangent) and 92 ms (threshold), respectively. For QTc, the spread was 108 and 105 ms, respectively. LQTS patients exhibited more pronounced differences. For automatic QTc results from 440-540 ms (tangent) and 430-530 ms (threshold), misassessment risk was highest. Novel automatic QT-interval algorithms may narrow this range.

CONCLUSION

Pairwise vendor-provided automatic and manual QT-interval and QTc results can be highly discrepant. Novel automatic algorithms may improve agreement. Within the above ranges, automatic QT-interval and QTc results require manual confirmation, particularly if T-wave morphology is challenging.

Inherited arrhythmia syndrome predisposing to sudden cardiac death

Inherited arrhythmia (IA) is one of the main causes of sudden cardiac death (SCD) in young people, and is reported to be a more prevalent cause of SCD in Asia than in Western countries. IAs are a group of genetic disorders caused by mutations in genes encoding cardiac ion channels, leading to electrophysiological characteristics that often occur in the absence of structural abnormalities. Channelopathies, such as long QT syndrome and Brugada syndrome, carry a potential risk of life-threatening ventricular tachyarrhythmias that predispose to SCD, although early prediction and prevention of the risk remain challenging. Recent advances in genetic testing have facilitated risk stratification as well as a precise diagnosis for IA, despite ongoing debates about the implications. Herein, we provide epidemiological data, a pathophysiological overview, and the current clinical approach to IAs related to SCD. In addition, we review the general issues arising from genetic testing for IAs.

Association of Silent Myocardial Infarction and Sudden Cardiac Death

Importance

Myocardial infarction in the absence of major or unrecognized symptoms are characterized as silent (SMI). The prevalence of SMI among individuals who experience sudden cardiac death (SCD), with or without concomitant electrocardiographic (ECG) changes, has not previously been described in detail from large studies to our knowledge.

Objective

To determine the prevalence of SMI in individuals who experience SCD without a prior diagnosis of coronary artery disease (CAD) and to detect ECG abnormalities associated with SMI-associated SCD.

Design, Setting, and Participants

This case-control study compared autopsy findings, clinical characteristics, and ECG markers associated with SMI in a consecutive cohort of individuals in the Finnish Genetic Study of Arrhythmic Events (Fingesture) study population who were verified to have had SCD. The Fingesture study consists of individuals who had autopsy-verified SCD in Northern Finland between 1998 and 2017. Individuals who had SCD with CAD and evidence of SMI were regarded as having had cases; those who had SCD with CAD without SMI were considered control participants. Analyses of ECG tests were carried out by investigators blinded to the SMI data. Data analysis was completed from October 2018 through November 2018.

Main Outcomes and Measures

Silent MI was defined as a scar detected by macroscopic and microscopic evaluation of myocardium without previously diagnosed CAD. Clinical history was obtained from medical records, previously recorded ECGs, and a standardized questionnaire provided to the next of kin. The hypothesis tested was that SMI would be prevalent in the population who had had SCD with CAD, and it might be detected or suspected from findings on ECGs prior to death in many individuals.

Results

A total of 5869 individuals were included (2459 males [78.8%]; mean [SD] age, 64.9 [12.4] years). The cause of SCD was CAD in 4392 individuals (74.8%), among whom 3122 had no history of previously diagnosed CAD. Two individuals were excluded owing to incomplete autopsy information. An ECG recorded prior to SCD was available in 438 individuals. Silent MI was detected in 1322 individuals (42.4%) who experienced SCD without a clinical history of CAD. The participants with SMI were older than participants without MI scarring (mean [SD] age, 66.9 [11.1] years; 65.5 [11.6] years; P < .001) and were more often men (1102 of 1322 [83.4%] vs 1357 of 1798 [75.5%]; P < .001). Heart weight was higher in participants with SMI (mean [SD] weight, 483 [109] g vs 438 [106] g; P < .001). In participants with SMI, SCD occurred more often during physical activity (241 of 1322 [18.2%] vs 223 of 1798 [12.4%]; P < .001). A prior ECG was abnormal in 125 of the 187 individuals (66.8%) who had SCD after SMI compared with 139 of 251 (55.4%) of those who had SCD without SMI (P = .02).

Conclusions and Relevance

Many individuals who experienced SCD associated with CAD had a previously undetected MI at autopsy. Previous SMI was associated with myocardial hypertrophy and SCD during physical activity. Premortem ECGs in a subset with available data were abnormal in 67% of the individuals who had had a SCD after an SMI.

Computational Electrocardiography: Revisiting Holter ECG Monitoring

BACKGROUND

Since 1942, when Goldberger introduced the 12-lead electrocardiography (ECG), this diagnostic method has not been changed.

OBJECTIVES

After 70 years of technologic developments, we revisit Holter ECG from recording to understanding.

METHODS

A fundamental change is fore-seen towards "computational ECG" (CECG), where continuous monitoring is producing big data volumes that are impossible to be inspected conventionally but require efficient computational methods. We draw parallels between CECG and computational biology, in particular with respect to computed tomography, computed radiology, and computed photography. From that, we identify technology and methodology needed for CECG.

RESULTS

Real-time transfer of raw data into meaningful parameters that are tracked over time will allow prediction of serious events, such as sudden cardiac death. Evolved from Holter's technology, portable smartphones with Bluetooth-connected textile-embedded sensors will capture noisy raw data (recording), process meaningful parameters over time (analysis), and transfer them to cloud services for sharing (handling), predicting serious events, and alarming (understanding). To make this happen, the following fields need more research: i) signal processing, ii) cycle decomposition; iii) cycle normalization, iv) cycle modeling, v) clinical parameter computation, vi) physiological modeling, and vii) event prediction.

CONCLUSIONS

We shall start immediately developing methodology for CECG analysis and understanding.

Wireless Smartphone ECG Enables Large-Scale Screening in Diverse Populations

BACKGROUND

The ubiquitous presence of internet-connected phones and tablets presents a new opportunity for cost-effective and efficient electrocardiogram (ECG) screening and on-demand diagnosis. Wireless, single-lead real-time ECG monitoring supported by iOS and android devices can be obtained quickly and on-demand. ECGs can be immediately downloaded and reviewed using any internet browser.

OBJECTIVE

We compared the standard 12-lead ECG to the smartphone ECG in healthy young adults, elite athletes, and cardiology clinic patients. Accuracy for determining baseline ECG intervals and rate and rhythm was assessed.

METHODS

In 381 participants, 30-second lead I ECG waveforms were obtained using an iPhone case or iPad. Standard 12-lead ECGs were acquired immediately after the smartphone tracing was obtained. De-identified ECGs were interpreted by automated algorithms and adjudicated by two board-certified electrophysiologists.

RESULTS

Both smartphone and standard ECGs detected atrial rate and rhythm, AV block, and QRS delay with equal accuracy. Sensitivities ranged from 72% (QRS delay) to 94% (atrial fibrillation). Specificities were all above 94% for both modalities.

CONCLUSION

Smartphone ECG accurately detects baseline intervals, atrial rate, and rhythm and enables screening in diverse populations. Efficient ECG analysis using automated discrimination and an enhanced smartphone application with notification capabilities are features that can be easily incorporated into the acquisition process.