| 1. |
- Axelsson, Karl-Jonas, et al.
(författare)
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Adaptation of ventricular repolarization dispersion during heart rate increase in humans: A roller coaster process.
- 2021
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Ingår i: Journal of electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 68, s. 90-100
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Tidskriftsartikel (refereegranskat)abstract
- Regional differences in ventricular activation sequence and action potential duration and morphology result in dispersion in ventricular repolarization (VR). VR dispersion is a key factor in arrhythmogenesis. We studied the adaptation of global VR dispersion in humans during normal and abnormal ventricular activation, and the relation to the QT adaptation (hysteresis).We measured global VR dispersion as T amplitude, T area, and ventricular gradient (VG), using continuous Frank vectorcardiography, in response to abrupt and sustained atrial (AP) or ventricular pacing (VP) aiming at 120 bpm, in 21 subjects with permanent pacemakers.Following pacing start, VR adaptation showed an initially rapid and complex tri-phasic pattern, most pronounced for T amplitude. There were major differences in the patterns of VR dispersion adaptation following abrupt AP vs VP, confirming that the adaptation pattern is activation dependent. In response to AP, an instantaneous decrease in VR dispersion occurred, followed by an increase and then a slow decrease, all at a lower level than baseline. In contrast, following VP there was an immediate increase to ~4× baseline in T amplitude and T area (but not in VG), with a subsequent biphasic adaptation lasting longer during VP than AP. The initial rapid changes occurred within the time for QT adaptation to reach steady-state.Our results corroborate and expand data from animal and invasive human studies, showing similarities of the adaptation pattern on different scales. The initial rapidly changing VR adaptation phase presumably reflects a window of increased vulnerability to arrhythmias.
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| 2. |
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| 3. |
- Baturova, Maria A., et al.
(författare)
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P-wave characteristics as electrocardiographic markers of atrial abnormality in prediction of incident atrial fibrillation – The Malmö Preventive Project
- 2024
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Ingår i: Journal of Electrocardiology. - 0022-0736 .- 1532-8430. ; 82, s. 125-130
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Tidskriftsartikel (refereegranskat)abstract
- Background: P-wave indices reflect atrial abnormalities contributing to atrial fibrillation (AF). We aimed to assess a comprehensive set of P-wave characteristics for prediction of incident AF in a population-based setting. Methods: Malmö Preventative Project (MPP) participants were reexamined in 2002–2006 with electrocardiographic (ECG) and echocardiographic examinations and followed for 5 years. AF-free subjects (n = 983, age 70 ± 5 years, 38% females) with sinus rhythm ECGs were included in the study. ECGs were digitally processed using the Glasgow algorithm. P-wave duration, axis, dispersion, P-terminal force in lead V1 and interatrial block (IAB) were evaluated. ECG risk score combining the morphology, voltage and length of P-wave (MVP score) was calculated. New-onset diagnoses of AF were obtained from nation-wide registers. Results: During follow up, 66 patients (7%) developed AF. After adjustment for age and gender, the independent predictors of AF were abnormal P-wave axis > 75° (HR 1.63 CI95% 1.95–11.03) and MVP score 4 (HR 6.17 CI 95% 1.76–21.64), both correlated with LA area: Person r − 0.146, p < 0.001 and 0.192, p < 0.001 respectively. Advanced IAB (aIAB) with biphasic P-wave morphology in leads III and aVF was the most prevalent variant of aIAB and predicted AF in a univariate model (HR 2.59 CI 95% 1.02–6.58). Conclusion: P-wave frontal axis and MVP score are ECG-based AF predictors in the population-based cohort. Our study provides estimates for prevalence and prognostic importance of different variants of aIAB, providing a support to use biphasic P-wave morphology in lead aVF as the basis for aIAB definition.
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| 4. |
- Bergfeldt, Lennart, 1950, et al.
(författare)
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Spatial peak and mean QRS-T angles: A comparison of similar but different emerging risk factors for cardiac death.
- 2020
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Ingår i: Journal of electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 61, s. 112-120
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Tidskriftsartikel (refereegranskat)abstract
- The spatial peak and mean QRS-T angles are scientifically but not clinically established risk factors for cardiovascular events including cardiac death. The study aims were to compare these angles, assess their association with hypertension (HT) and diabetes mellitus (DM), and explore the relation between the mean QRS-T angle and the ventricular gradient (VG; reflecting electrical heterogeneity), which both are derived from the QRSarea and Tarea vectors.Altogether 1094 participants (aged 50-65years, 550 women) from the pilot of the population-based Swedish CArdioPulmonary bioImage Study with Frank vectorcardiographic recordings were included and divided into 5 subgroups: apparently healthy n=320; HT n=311; DM n=33; DM+HT n=53; miscellaneous conditions n=377. Abnormal peak and mean QRS-T angles were defined as >95th percentile.Peak QRS-T angles were generally narrower than the mean QRS-T angles; both were narrower in women than in men. Abnormal peak (>124°) and/or mean (>119°) QRS-T angles were found in 73 participants (6.7%). The concordance regarding abnormal versus normal-borderline QRS-T angles was good (Cohen's kappa 0.61). The prevalence of abnormal angles varied from 2.5% in healthy to 21.2% in DM. There was an inverse logarithmical relation between the mean QRS-T angle and the VG.The peak and mean QRS-T angles are not interchangeable but complementary. DM, HT, sex and absence of disease are important determinants of both QRS-T angles. The mean QRS-T angle and the VG relationship is complex. All three VCG derived measures reflect related but differing electrophysiological properties and have potential prognostic value vis-à-vis cardiovascular events.
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| 5. |
- Habineza, Theogene, et al.
(författare)
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End-to-end risk prediction of atrial fibrillation from the 12-Lead ECG by deep neural networks
- 2023
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Ingår i: Journal of Electrocardiology. - : Elsevier. - 0022-0736 .- 1532-8430. ; 81, s. 193-200
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Tidskriftsartikel (refereegranskat)abstract
- Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovas-cular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil.Methods: We used the CODE cohort to develop and test a model for AF risk prediction for individual patients from the raw ECG recordings without the use of additional digital biomarkers. The cohort is a collection of ECG recordings and annotations by the Telehealth Network of Minas Gerais, in Brazil. A convolutional neural network based on a residual network architecture was implemented to produce class probabilities for the classification of AF. The probabilities were used to develop a Cox proportional hazards model and a Kaplan-Meier model to carry out survival analysis. Hence, our model is able to perform risk prediction for the development of AF in patients without the condition.Results: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being >0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have >85% chance of remaining AF free up until after seven years.Conclusion: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision- making and patient management processes.
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| 7. |
- Lindow, Thomas, et al.
(författare)
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Optimal measuring point for ST deviation in chest pain patients with possible acute coronary syndrome
- 2020
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Ingår i: Journal of Electrocardiology. - : Elsevier BV. - 1532-8430 .- 0022-0736. ; 58, s. 165-170
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Tidskriftsartikel (refereegranskat)abstract
- INTRODUCTION: In the ECG, significant ST elevation or depression according to specific amplitude criteria can be indicative of acute coronary syndrome (ACS). Guidelines state that the ST amplitude should be measured at the J point, but data to support that this is the optimal measuring point for ACS detection is lacking. We evaluated the impact of different measuring points for ST deviation on the diagnostic accuracy for ACS in unselected emergency department (ED) chest pain patients.MATERIAL AND METHODS: We included 14,148 adult patients with acute chest pain and an ECG recorded at a Swedish ED between 2010 and 2014. ST deviation was measured at the J point (STJ) and at 20, 40, 60 and 80 ms after the J point. A discharge diagnosis of ACS or not at the index visit was noted in all patients.RESULTS: In total, 1489 (10.5%) patients had ACS. ST amplitude criteria at STJ had a sensitivity of 28% and a specificity of 92% for ACS. With these criteria, the highest positive and negative predictive values for ACS were obtained near the J point, but the optimal point varied with ST deviation, age group and sex. The overall best measuring points were STJ and ST20.CONCLUSIONS: This study indicates that the diagnostic accuracy of the ECG criteria for ACS is very low in ED chest pain patients, and that the optimal measuring point for the ST amplitude in the detection of ACS differs between ST elevation and depression, and between patient subgroups.
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| 10. |
- Nyström, Axel, et al.
(författare)
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Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients
- 2024
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Ingår i: Journal of Electrocardiology. - Philadelphia, PA : Elsevier. - 0022-0736 .- 1532-8430. ; 82, s. 42-51
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Tidskriftsartikel (refereegranskat)abstract
- At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice. © 2023 The Authors
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