Basis and ECG measurement of global ventricular repolarization

Evaluation of Tpeak-end interval, Tpeak-end/QT, and Tpeak-end/Qtc ratio during acute migraine attack in the emergency department

INTRODUCTION

During an acute migraine attack, changes in ventricular repolarisation parameters may occur due to an imbalance in the autonomic nervous system. Tpeak-tend (Tp-e) interval, Tp-e/QT ratio, and Tp-e/corrected QT (QTc) ratio are novel parameters of arrhythmogenesis and can be easily calculated in electrocardiography (ECG). The objective of this study is to demonstrate that novel ventricular repolarisation parameters can anticipate the risk of ventricular dysrhythmia in the migraine attack period.

METHODS

This research was a prospective case-control study, which recruited a total of 144 participants, including 74 migraine patients and 70 healthy volunteers in the control group (CG) who met the criteria for migraine with or without aura. All participants underwent 12-lead ECG recordings, and the study compared the Tp-e interval, Tp-e/QT ratio, and Tp-e/QTc ratio with those of the CG.

RESULTS

The average age of patients experiencing migraine attacks was 38.14 ± 10.82, with 58 (76%) of these patients being female. The Tp-e interval mean was higher in the migraine attack group than the CG, with a statistically significant difference discovered (74.22 ± 20.20 ms [ms] compared to 65.39 ± 11.33 ms, p = 0.001). However, there were higher mean Tp-e/QT and Tp-e/QTc ratios in the migraine attack group compared to the CG, and this difference was found to be statistically significant (0.20 ± 0.05 vs. 0.17 ± 0.03, p = 0.001, 0.18 ± 0.52 vs 0.16 ± 0.29, p = 0.003, respectively).

CONCLUSION

Prolonged Tp-e interval and elevated Tp-e/QT and Tp-e/QTc ratios were observed in migraine patients who presented to the emergency department, indicating a potential risk of ventricular dysrhythmia.

Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived OMI risk score provided enhanced rule-in and rule-out accuracy relevant to routine care, and, when combined with the clinical judgment of trained emergency personnel, it helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting ECG are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but we currently have no accurate tools to identify them during initial triage. Herein, we report the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, significantly boosting both precision and sensitivity. Our derived OMI risk score provided superior rule-in and rule-out accuracy compared to routine care, and when combined with the clinical judgment of trained emergency personnel, this score helped correctly reclassify one in three patients with chest pain. ECG features driving our models were validated by clinical experts, providing plausible mechanistic links to myocardial injury.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome

STUDY OBJECTIVE

Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.

METHODS

This prospective observational cohort study recruited patients with out-of-hospital chest pain. We retrieved out-of-hospital-ECG obtained by paramedics in the field and the first ED ECG obtained by nurses during inhospital evaluation. Two independent and blinded reviewers interpreted ECG dyads in mixed order per practice recommendations. Using 179 morphological ECG features, we trained, cross-validated, and tested a random forest classifier to augment non ST-elevation acute coronary syndrome (NSTE-ACS) diagnosis.

RESULTS

Our sample included 2,122 patients (age 59 [16]; 53% women; 44% Black, 13.5% confirmed acute coronary syndrome). The rate of diagnostic ST elevation and ST depression were 5.9% and 16.2% on out-of-hospital-ECG and 6.1% and 12.4% on ED ECG, with ∼40% of changes seen on out-of-hospital-ECG persisting and ∼60% resolving. Using expert interpretation of out-of-hospital-ECG alone gave poor baseline performance with area under the receiver operating characteristic (AUC), sensitivity, and negative predictive values of 0.69, 0.50, and 0.92. Using expert interpretation of serial ECG changes enhanced this performance (AUC 0.80, sensitivity 0.61, and specificity 0.93). Interestingly, augmenting the out-of-hospital-ECG alone with artificial intelligence algorithms boosted its performance (AUC 0.83, sensitivity 0.75, and specificity 0.95), yielding a net reclassification improvement of 29.5% against expert ECG interpretation.

CONCLUSION

In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.

Machine read frontal QRS-T angle and QTc is no substitute for manual measurement of QTc in pro-arrhythmic drug overdose

INTRODUCTION

To investigate whether there is an association between the blocking of cardiac potassium channels, which is characterised by a prolonged QTc interval and the frontal QRS-T angle after overdose by QT prolonging drugs.

METHODS

We obtained patient medical records associated with QT prolonging drugs from 3 different hospitals: the Calvary Mater Newcastle Hospital (CMNH), Royal Prince Alfred Hospital (RPAH) and Prince of Wales Hospital (POWH). RPAH and POWH admissions were taken between 4/01/2017 to 1/11/2019, and CMNH admissions were taken between 4/01/2013 to 24/06/2018. Demographic information and details of overdose were collected. All admission ECGs were manually measured. Linear regression was used to assess the relationship between various QTc formulas and the frontal QRS-T angle. A Bland-Altman plot was used to examine agreement between manual and machine QT intervals.

RESULTS

144 patients met the inclusion criteria for analysis. None of the patients developed torsades de pointes (TdP). There was no linear association between the QRS-T angle and the various QTc formulas (For QRS-T angle: QTcRTH: p = 0.76, QTcB: p = 0.83, QTcFri: p = 0.90, QTcFra: p = 0.13, QTcH: p = 0.97; For square root transformation of the QRS-T angle: QTcRTH: p = 0.18, QTcB: p = 0.33, QTcFri: p = 0.95, QTcFra: p = 0.47, QTcH: p = 0.33). Agreement between machine and manual QT measurements was low.

CONCLUSIONS

The frontal QRS-T angle cannot substitute the QTc in assessing the blockage of cardiac potassium channels in drug induced long QT syndrome. We also support the consensus that despite the availability of machine measurements of the QT interval, manual measurements should also be performed.

The Changes of T-Wave Amplitude and QT Interval Between the Supine and Orthostatic Electrocardiogram in Children With Dilated Cardiomyopathy

Objectives: Electrocardiogram (ECG) can be affected by autonomic nerves with body position changes. The study aims to explore the ECG changes of children with dilated cardiomyopathy (DCM) when their posture changes. Materials and methods: Sixty-four children diagnosed with DCM were recruited as research group and 55 healthy children as control group. T-wave amplitude and QT interval in ECG were recorded, and their differences between supine and orthostatic ECG were compared in both groups. Subsequently, the children with DCM were followed up and the differences before and after treatment compared. Results: ① Comparisons in differences: Differences of T-wave amplitude in lead II and III, aVF, and V₅ and differences of QT interval in lead II, aVL, aVF, and V₅ were lower in the research group than in the control group. ② Logistic regression analysis and diagnostic test evaluation: The differences of T-wave amplitude in lead III and QT interval in lead aVL may have predictive value for DCM diagnosis. When their values were 0.00 mV and 30 ms, respectively, the sensitivity and specificity of the combined index were 37.5 and 83.6%. ③ Follow-up: In the response group, the T-wave amplitude difference in lead aVR increased and the difference of QT interval in lead V₆ decreased after treatment. In the non-response group, there was no difference before and after treatment. When the combined index of the differences of T-wave amplitude difference in lead aVR and QT interval difference in lead V₆, respectively, were -0.05 mV and 5 ms, the sensitivity and specificity of estimating the prognosis of DCM were 44.4 and 83.3%. Conclusions: The differences of T-wave amplitude and QT interval may have a certain value to estimate DCM diagnosis and prognosis.

Acute effects of electronic cigarette smoking on ventricular repolarization in adults

BACKGROUND

Electronic cigarette (e-cigarette) use is constantly increasing. However, the association between e-cigarette use and ventricular arrhythmia is unknown. Thus, in this study, we aimed to evaluate the markers of ventricular repolarization such as QT interval, corrected QT (QTc), QT dispersion (QTd), peak-to-end interval of the T wave (Tp-e), corrected Tp-e and Tp-e/QT ratios in e-cigarette users.

METHODS

The study population consisted 36 e-cigarette users and 40 healthy subjects. Ventricular repolarization parameters were obtained from 12-lead resting electrocardiogram. Ventricular repolarization parameters of the groups were compared.

RESULTS

Basal demographic and laboratory data were similar in both groups. According to the electrocardiographic parameters, the Tp-e interval, corrected Tp-e, and Tp-e/QT ratio were significantly higher in individuals using e-cigarettes than in control subjects [74.9±6.4 milliseconds (ms) vs. 80.1±4.1ms, <0.001; 82.9±7.5 ms vs. 87.8±6.3 ms, p=0.003; 0.20±0.01 vs. 0.21±0.01, p=0.002; respectively].

CONCLUSION

This is the first study to show the disruption of ventricular repolarization properties in e-cigarette users. E-cigarette use in terms of public health leads to augmentation of transmural dispersion of repolarization, which may be potential indicator of ventricular arrhythmogenesis.

Investigation of the Value of T peak to T end and QTc Intervals as Electrocardiographic Arrhythmia Susceptibility Markers in Acute Ischemic Stroke

INTRODUCTION

Arrhythmias are one of the most common causes of mortality in patients with acute ischemic stroke (AIS). This study aimed to investigate the relationships of arrhythmia susceptibility markers (QT, QTc, Tpe, Tpe-D, Tpe/QT, and Tpe/QTc) with the localization and volume of the ischemic area, the National Institutes of Health Stroke Scale (NIHSS) scores, and troponin levels in AIS.

METHODS

Patients diagnosed with AIS in the emergency department in the period from 01 November 2016 to 31 March 2019 were retrospectively reviewed. Patients admitted to the emergency department with no pathological ECG findings were included. The measurements of QT, QTc, Tpe, Tpe-D, Tpe/QTc, and Tpe/QT were performed under a digital microscope. The NIHSS scores, troponin values, and the ischemic area volume based on the diffusion-weighted magnetic resonance imaging findings at the time of admission were found.

RESULTS

A total of 135 patients, comprising 70 AIS patients and 65 individuals as controls, were included in the study. The male/female ratio was 73/62 and the mean age was 68.51±10.80 years. All of the ECG parameters in the AIS group and the control group were statistically significantly different between the groups except Tpe-D (p=0.454) (For QT, QTc, Tpe, Tpe/QTc, and Tpe/QT; p=0.003, 0.022, <0.001, 0.001, 0.001; respectively). QT, QTc, Tpe, Tpe/QTc, and Tpe/QT values were not significantly different between the groups with a NIHSS score of ≤5 and >5 (p=0.480, 0.688, 0.663, 0.512, 0.333, respectively).

CONCLUSIONS

Arrhythmia susceptibility markers including QT, QTc, Tpe, the values of Tpe-D, Tpe/QT, and Tpe/QTc are different in AIS patients compared to the individuals in the control group; therefore, these parameters can be included among the other parameters of close cardiac monitoring.

Moxifloxacin-induced QT interval prolongation and torsades de pointes: a narrative review

INTRODUCTION

Moxifloxacin is widely used for the treatment of a number of infectious diseases because of its favorable pharmacological profile and high clinical success rate. However, it is often criticized for its higher risk of QTc interval prolongation (QTIP) and torsades de pointes (TdP).

AREAS COVERED

A review of published literature on moxifloxacin-related QTIP and TdP. Readers will be provided with a comprehensive overview of the prevalence, cellular mechanism, risk factors, and magnitude of QTIP of moxifloxacin.

EXPERT OPINION

In healthy subjects, moxifloxacin prolongs the QTc interval by 11.5-19.5 ms, it binds at the Tyr652 residue in the S6 pore domain of the human ether a-go-go gene related potassium channel. Considerable QTIP (30-60 ms) have also been reported in some patients, for instance the incidence of QTIP (30-60 ms) in elderly pneumonia patients was 15.5%. Moxifloxacin-induced QTIP may be of little clinical importance in healthy individuals. However, marked QTIP (>60 ms) and TdP have been reported in high-risk patients (patients who have multiple QT prolonging risk factors). Patients must be thoroughly assessed prior to the use of moxifloxacin and high-risk patients must be identified using risk assessment tools to ensure safe use of moxifloxacin and to safeguard patients' health.