Publications by authors named "Ben J M Hermans"

13 Publications

  • Page 1 of 1

Corrected QT interval prolongation after ganglionated plexus ablation: myth or reality?-Authors' reply.

Europace 2021 Jun 23. Epub 2021 Jun 23.

Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

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http://dx.doi.org/10.1093/europace/euab148DOI Listing
June 2021

Does pulmonary vein isolation prolong QT-interval?- Authors' reply.

Europace 2021 Jun 15. Epub 2021 Jun 15.

Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

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http://dx.doi.org/10.1093/europace/euab153DOI Listing
June 2021

Diagnostic accuracy of the response to the brief tachycardia provoked by standing in children suspected for long QT syndrome.

Heart Rhythm O2 2021 Apr 13;2(2):149-159. Epub 2021 Mar 13.

Department of Cardiology, Amsterdam UMC, University of Amsterdam, Heart Center, Amsterdam, The Netherlands.

Background: Adult long QT syndrome (LQTS) patients have inadequate corrected QT interval (QTc) shortening and an abnormal T-wave response to the sudden heart rate acceleration provoked by standing. In adults, this knowledge can be used to aid an LQTS diagnosis and, possibly, for risk stratification. However, data on the diagnostic value of the standing test in children are currently limited.

Objective: To determine the potential value of the standing test to aid LQTS diagnostics in children.

Methods: In a prospective cohort including children (≤18 years) who had a standing test, comprehensive analyses were performed including manual and automated QT interval assessments and determination of T-wave morphology changes.

Results: We included 47 LQTS children and 86 control children. At baseline, the QTc that identified LQTS children with a 90% sensitivity was 435 ms, which yielded a 65% specificity. A QTc ≥ 490 ms after standing only slightly increased sensitivity (91%, 95% confidence interval [CI]: 80%-98%) and slightly decreased specificity (58%, 95% CI: 47%-70%). Sensitivity increased slightly more when T-wave abnormalities were present (94%, 95% CI: 82%-99%; specificity 53%, 95% CI: 42%-65%). When a baseline QTc ≥ 440 ms was accompanied by a QTc ≥ 490 ms and T-wave abnormalities after standing, sensitivity further increased (96%, 95% CI: 85%-99%) at the expense of a further specificity decrease (41%, 95% CI: 30%-52%). Beat-to-beat analysis showed that 30 seconds after standing, LQTS children had a greater increase in heart rate compared to controls, which was more evidently present in LQTS boys and LQTS type 1 children.

Conclusion: In children, the standing test has limited additive diagnostic value for LQTS over a baseline electrocardiogram, while T-wave abnormalities after standing also have limited additional value. The standing test for LQTS should only be used with caution in children.
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http://dx.doi.org/10.1016/j.hroo.2021.03.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8183857PMC
April 2021

Effect of citric-acid dialysate on the QTC-interval.

Sci Rep 2021 05 10;11(1):9909. Epub 2021 May 10.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Lower dialysate calcium (dCa) concentration and dialysate citric-acidification may positively affect calcification propensity in serum of haemodialysis (HD) patients. However, the accompanying lower ionized blood calcium concentration may lead to a prolonged cardiac action potential, which is possibly pro-arrhythmic. The aim of this study is to investigate the influence of citric-acid dialysate on the QT-interval corrected for heart rate (QTc) compared to conventional dialysate with different dCa concentrations. We conducted a four-week multicentre, randomized cross-over trial. In week one and three patients received acetic-acid dialysate with a dCa of 1.50 mmol/l (A1.5), in week two and four acetic-acid dialysate with a dCa of 1.25 mmol/l (A1.25) or citric-acid dialysate (1.0 mmol/l) with a dCa of 1.50 mmol/l (C1.5) depending on randomization. Patients had continuous ECG monitoring during one session in week one, two and four. The data of 13 patients were available for analysis. Results showed a significant though limited increase of QTc with C1.5 (from 427 to 444 ms (start to end); p = 0.007) and with A1.25 (from 431 to 449 ms; p < 0.001), but not with A1.5 (from 439 to 443 ms; p = 0.13). In conclusion, we found that the use of C1.5 or A1.25 is associated with a significant prolongation of QTc which was however relatively limited.
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http://dx.doi.org/10.1038/s41598-021-89083-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110800PMC
May 2021

Incidence, prevalence, and trajectories of repetitive conduction patterns in human atrial fibrillation.

Europace 2021 03;23(23 Suppl 1):i123-i132

Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands.

Aims: Repetitive conduction patterns in atrial fibrillation (AF) may reflect anatomical structures harbouring preferential conduction paths and indicate the presence of stationary sources for AF. Recently, we demonstrated a novel technique to detect repetitive patterns in high-density contact mapping of AF. As a first step towards repetitive pattern mapping to guide AF ablation, we determined the incidence, prevalence, and trajectories of repetitive conduction patterns in epicardial contact mapping of paroxysmal and persistent AF patients.

Methods And Results: A 256-channel mapping array was used to record epicardial left and right AF electrograms in persistent AF (persAF, n = 9) and paroxysmal AF (pAF, n = 11) patients. Intervals containing repetitive conduction patterns were detected using recurrence plots. Activation movies, preferential conduction direction, and average activation sequence were used to characterize and classify conduction patterns. Repetitive patterns were identified in 33/40 recordings. Repetitive patterns were more prevalent in pAF compared with persAF [pAF: median 59%, inter-quartile range (41-72) vs. persAF: 39% (0-51), P < 0.01], larger [pAF: = 1.54 (1.15-1.96) vs. persAF: 1.16 (0.74-1.56) cm2, P < 0.001), and more stable [normalized preferentiality (0-1) pAF: 0.38 (0.25-0.50) vs. persAF: 0.23 (0-0.33), P < 0.01]. Most repetitive patterns were peripheral waves (87%), often with conduction block (69%), while breakthroughs (9%) and re-entries (2%) occurred less frequently.

Conclusion: High-density epicardial contact mapping in AF patients reveals frequent repetitive conduction patterns. In persistent AF patients, repetitive patterns were less frequent, smaller, and more variable than in paroxysmal AF patients. Future research should elucidate whether these patterns can help in finding AF ablation targets.
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http://dx.doi.org/10.1093/europace/euaa403DOI Listing
March 2021

Pulmonary vein isolation in a real-world population does not influence QTc interval.

Europace 2021 03;23(23 Suppl 1):i48-i54

Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

Aims: We aimed to examine whether routine pulmonary vein isolation (PVI) induces significant ventricular repolarization changes as suggested earlier.

Methods And Results: Five-minute electrocardiograms were recorded at hospital's admission (T-1d), 1 day after the PVI-procedure (T+1d) and at 3 months post-procedure (T+3m) from a registry of consecutive atrial fibrillation (AF) patients scheduled for routine PVI with different PVI modalities (radiofrequency, cryo-ablation, and hybrid). Only patients who were in sinus rhythm at all three recordings (n = 117) were included. QT-intervals and QT-dispersion were evaluated with custom-made software and QTc was calculated using Bazett's, Fridericia's, Framingham's, and Hodges' formulas. Both QT- and RR-intervals were significantly shorter at T+1d (399 ± 37 and 870 ± 141 ms) and T+3m (407 ± 36 and 950 ± 140 ms) compared with baseline (417 ± 36 and 1025 ± 164 ms). There was no statistically significant within-subject difference in QTc Fridericia (T-1d 416 ± 28 ms, T+1d 419 ± 33 ms, and T+3m 414 ± 25 ms) and QT-dispersion (T-1d 18 ± 12 ms, T+1d 21 ± 19 ms, and T+3m 17 ± 12 ms) between the recordings. A multiple linear regression model with age, sex, AF type, ablation technique, first/re-do ablation, and AF recurrence to predict the change in QTc at T+3m with respect to QTc at T-1d did not reach significance which indicates that the change in QTc does not differ between all subgroups (age, sex, AF type, ablation technique, first/re-do ablation, and AF recurrence).

Conclusion: Based on our data a routine PVI does not result in a prolongation of QTc in a real-world population. These findings, therefore, suggest that there is no need to intensify post-PVI QT-interval monitoring.
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http://dx.doi.org/10.1093/europace/euaa390DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7943360PMC
March 2021

Abnormal electrograms during sinus rhythm for VT ablation: Beware of Fata Morganas.

J Cardiovasc Electrophysiol 2021 02 24;32(2):389-390. Epub 2021 Jan 24.

Department of Cardiology, Cardiovascular Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, The Netherlands.

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http://dx.doi.org/10.1111/jce.14855DOI Listing
February 2021

Serial measurements in COVID-19-induced acute respiratory disease to unravel heterogeneity of the disease course: design of the Maastricht Intensive Care COVID cohort (MaastrICCht).

BMJ Open 2020 09 29;10(9):e040175. Epub 2020 Sep 29.

Department of Intensive Care, Maastricht University Medical Center+, Maastricht, The Netherlands.

Introduction: The course of the disease in SARS-CoV-2 infection in mechanically ventilated patients is unknown. To unravel the clinical heterogeneity of the SARS-CoV-2 infection in these patients, we designed the prospective observational Maastricht Intensive Care COVID cohort (MaastrICCht). We incorporated serial measurements that harbour aetiological, diagnostic and predictive information. The study aims to investigate the heterogeneity of the natural course of critically ill patients with a SARS-CoV-2 infection.

Methods And Analysis: Mechanically ventilated patients admitted to the intensive care with a SARS-CoV-2 infection will be included. We will collect clinical variables, vital parameters, laboratory variables, mechanical ventilator settings, chest electrical impedance tomography, ECGs, echocardiography as well as other imaging modalities to assess heterogeneity of the course of a SARS-CoV-2 infection in critically ill patients. The MaastrICCht is also designed to foster various other studies and registries and intends to create an open-source database for investigators. Therefore, a major part of the data collection is aligned with an existing national intensive care data registry and two international COVID-19 data collection initiatives. Additionally, we create a flexible design, so that additional measures can be added during the ongoing study based on new knowledge obtained from the rapidly growing body of evidence. The spread of the COVID-19 pandemic requires the swift implementation of observational research to unravel heterogeneity of the natural course of the disease of SARS-CoV-2 infection in mechanically ventilated patients. Our study design is expected to enhance aetiological, diagnostic and prognostic understanding of the disease. This paper describes the design of the MaastrICCht.

Ethics And Dissemination: Ethical approval has been obtained from the medical ethics committee (Medisch Ethische Toetsingscommissie 2020-1565/3 00 523) of the Maastricht University Medical Centre+ (Maastricht UMC+), which will be performed based on the Declaration of Helsinki. During the pandemic, the board of directors of Maastricht UMC+ adopted a policy to inform patients and ask their consent to use the collected data and to store serum samples for COVID-19 research purposes. All study documentation will be stored securely for fifteen years after recruitment of the last patient. The results will be published in peer-reviewed academic journals, with a preference for open access journals, while particularly considering deposition of the manuscripts on a preprint server early.

Trial Registration Number: The Netherlands Trial Register (NL8613).
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http://dx.doi.org/10.1136/bmjopen-2020-040175DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526030PMC
September 2020

Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.

Heart Rhythm 2020 05 7;17(5 Pt A):752-758. Epub 2020 Jan 7.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands; Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, The Netherlands.

Background: Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.

Objective: The purpose of this study was to improve LQTS diagnosis by combining new polynomial-based T-wave morphology parameters with the corrected QT interval (QTc), age, and sex in a model.

Methods: A retrospective cohort consisting of 333 patients with LQTS and 345 genotype-negative family members was used in this study. For each patient, a linear combination of the first 2 Hermite-Gauss (HG) polynomials was fitted to the STT segments of an average complex of all precordial leads and limb leads I and II. The weight coefficients as well as the error of the best fit were used to characterize T-wave morphology. Subjects were classified as patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine models fed with different features. An external cohort consisting of 72 patients and 45 controls was finally used to check the robustness of the models.

Results: Baseline QTc cutoffs were specific but had low sensitivity in diagnosing LQTS. The model with T-wave morphology features, QTc, age, and sex had the best overall accuracy (84%), followed by a model with QTc, age, and sex (79%). The model with T-wave morphology features especially performed better in LQTS type 3 patients (69%).

Conclusion: T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis.
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http://dx.doi.org/10.1016/j.hrthm.2019.12.020DOI Listing
May 2020

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace 2018 Nov;20(suppl_3):iii113-iii119

Department of Biomedical Engineering, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands.

Aims: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from 12-lead electrocardiograms (ECGs) in diagnosing LQTS in a large cohort of gene-positive LQTS patients and gene-negative family members using a support vector machine.

Methods And Results: A retrospective study was performed including 688 digital 12-lead ECGs recorded from genotype-positive LQTS patients and genotype-negative relatives at their first visit. Two models were trained and tested equally: a baseline model with age, gender, RR-interval, QT-interval, and QTc-intervals as inputs and an extended model including morphology features as well. The best performing baseline model showed an area under the receiver-operating characteristic curve (AUC) of 0.821, whereas the extended model showed an AUC of 0.901. Sensitivity and specificity at the maximal Youden's indexes changed from 0.694 and 0.829 with the baseline model to 0.820 and 0.861 with the extended model. Compared with clinically used QTc-interval cut-off values (>480 ms), the extended model showed a major drop in false negative classifications of LQTS patients.

Conclusion: The support vector machine-based extended model with T-wave morphology markers resulted in a major rise in sensitivity and specificity at the maximal Youden's index. From this, it can be concluded that T-wave morphology assessment has an added value in the diagnosis of LQTS.
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http://dx.doi.org/10.1093/europace/euy243DOI Listing
November 2018

ST waveform analysis for monitoring hypoxic distress in fetal sheep after prolonged umbilical cord occlusion.

PLoS One 2018 16;13(4):e0195978. Epub 2018 Apr 16.

Department of Biomedical Engineering, Maastricht University, Maastricht, the Netherlands.

Introduction: The inconclusive clinical results for ST-waveform analysis (STAN) in detecting fetal hypoxemia may be caused by the signal processing of the STAN-device itself. We assessed the performance of a clinical STAN device in signal processing and in detecting hypoxemia in a fetal sheep model exposed to prolonged umbilical cord occlusion (UCO).

Methods: Eight fetal lambs were exposed to 25 minutes of UCO. ECG recordings were analyzed during a baseline period and during UCO. STAN-event rates and timing of episodic T/QRS rise, baseline T/QRS rise and the occurrence of biphasic ST-waveforms, as well as signal loss, were assessed.

Results: During baseline conditions of normoxemia, a median of 40 (IQR, 25-70) STAN-events per minute were detected, compared to 10 (IQR, 2-22) during UCO. During UCO STAN-events were detected in five subjects within 10 minutes and in six subjects after 18 minutes, respectively. Two subjects did not generate any STAN-event during UCO. Biphasic ST event rate was reduced during UCO (median 0, IQR 0-5), compared to baseline (median 32, IQR, 6-55). ST-waveforms could not be assessed in 62% of the recording time during UCO, despite a good quality of the ECG signal.

Conclusions: The STAN device showed limitations in detecting hypoxemia in fetal sheep after prolonged UCO. The STAN device produced high false positive event rates during baseline and did not detect T/QRS changes adequately after prolonged fetal hypoxemia. During 14% of baseline and 62% of the UCO period, the STAN-device could not process the ECG signal, despite its good quality. Resolving these issues may improve the clinical performance of the STAN device.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0195978PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5901956PMC
July 2018

Tailoring device settings in cardiac resynchronization therapy using electrograms from pacing electrodes.

Europace 2018 07;20(7):1146-1153

Department of Physiology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands.

Aims: Left ventricular (LV) fusion pacing appears to be at least as beneficial as biventricular pacing in cardiac resynchronization therapy (CRT). Optimal LV fusion pacing critically requires adjusting the atrioventricular (AV)-delay to the delay between atrial pacing and intrinsic right ventricular (RV) activation (Ap-RV). We explored the use of electrogram (EGM)-based vectorloop (EGMV) derived from EGMs of implanted pacing leads to achieve optimal LV fusion pacing and to compare it with conventional approaches.

Methods And Results: During CRT-device implantation, 28 patients were prospectively studied. During atrial-LV pacing (Ap-LVp) at various AV-delays, LV dP/dtmax, 12-lead electrocardiogram (ECG), and unipolar EGMs were recorded. Electrocardiogram and electrogram were used to reconstruct a vectorcardiogram (VCG) and EGMV, respectively, from which the maximum QRS amplitude (QRSampl), was extracted. Ap-RV was determined: (i) conventionally as the longest AV-delay at which QRS morphology was visually unaltered during RV pacing at increasing AV-delays(Ap-RVvis; reference-method); (ii) 70% of delay between atrial pacing and RV sensing (Ap-RVaCRT); and (iii) the delay between atrial pacing and onset of QRS (Ap-QRSonset). In both the EGMV and VCG, the longest AV-delay showing an unaltered QRSampl as compared with Ap-LVp with a short AV-delay, corresponded to Ap-RVvis. In contrast, Ap-QRSonset and Ap-RVaCRT were larger. The Ap-LVp induced increase in LV dP/dtmax was larger at Ap-RVvis, Ap-RVEGMV, and Ap-RVVCG than at Ap-QRSonset (all P < 0.05) and Ap-RVaCRT (P = 0.02, P = 0.13, and P = 0.03, respectively).

Conclusion: In this acute study, it is shown that the EGMV QRSampl can be used to determine optimal and individual CRT-device settings for LV fusion pacing, possibly improving long-term CRT response.
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http://dx.doi.org/10.1093/europace/eux208DOI Listing
July 2018

The development and validation of an easy to use automatic QT-interval algorithm.

PLoS One 2017 1;12(9):e0184352. Epub 2017 Sep 1.

Department of Biomedical Engineering, Maastricht University, Maastricht, The Netherlands.

Background: To evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.

Objective: To develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.

Methods: Additionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat. Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.

Results: After visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.

Conclusion: Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0184352PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5581168PMC
October 2017
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