Thyroid hormones regulate cardiac repolarization and QT-interval related gene expression in hiPSC cardiomyocytes

Prolongation of cardiac repolarization (QT interval) represents a dangerous and potentially life-threatening electrical event affecting the heart. Thyroid hormones (THs) are critical for cardiac development and heart function. However, little is known about THs influence on ventricular repolarization and controversial effects on QT prolongation are reported. Human iPSC-derived cardiomyocytes (hiPSC-CMs) and multielectrode array (MEA) systems were used to investigate the influence of 3,3′,5-triiodo-l-Thyronine (T3) and 3,3′,5,5′-tetraiodo-l-Thyronine (T4) on corrected Field Potential Duration (FPDc), the in vitro analog of QT interval, and on local extracellular Action Potential Duration (APD). Treatment with high THs doses induces a significant prolongation of both FPDc and APD, with the strongest increase reached after 24 h exposure. Preincubation with reverse T3 (rT3), a specific antagonist for nuclear TH receptor binding, significantly reduces T3 effects on FPDc, suggesting a TRs-mediated transcriptional mechanism. RNA-seq analysis showed significant deregulation in genes involved in cardiac repolarization pathways, including several QT-interval related genes. In conclusion, long-time administration of high THs doses induces FPDc prolongation in hiPSC-CMs probably through the modulation of genes linked to QT-interval regulation. These results open the way to investigate new potential diagnostic biomarkers and specific targeted therapies for cardiac repolarization dysfunctions.

QT Prolongation in Critically Ill Patients With SARS-CoV-2 Infection

Background: Several reports linked the use of repurposed drugs such as hydroxychloroquine (HCQ), azithromycin, lopinavir/ritonavir, and favipiravir with QT interval prolongation in patients with SARS-CoV2 infection. Little is known about the risk factors for QT interval prolongation in this population. We sought to describe the prevalence and identify the main risk factors associated with clinically significant corrected QT (QTc) prolongation in this population. Methods: We conducted a retrospective analysis of critically ill patients who were admitted to our intensive care unit (ICU), had at least one electrocardiogram performed during their ICU stay, and tested positive for SARs-CoV-2. Clinically significant QTc interval prolongation was defined as QTc >500 milliseconds (ms). Results: Out of the 111 critically ill patients with SARS-CoV-2 infection, QTc was significantly prolonged in 47 cases (42.3%). Patients with a clinically significant QTc prolongation had significantly higher proportions of history of cardiac diseases/surgery (22 [46.8%] vs. 10 [15.6%], P < .001), hypokalemia (10 [21.3] vs. 5 [7.8%], P = .04), and male gender (95% vs. 82.8%, P = .036) than patients with QTc ≤500 ms, respectively. A total of 46 patients (41.4%) received HCQ, 28 (25.2%) received lopinavir/ritonavir, and 5 (4.5%) received azithromycin. Multivariate logistic regression analysis showed that a history of cardiac disease was the only independent factor associated with clinically significant QTc prolongation ( P = .004 for the likelihood-ratio test). Conclusion: The prevalence of clinically significant QTc prolongation in critically ill patients with SARS-CoV-2 infection was high and independent of drugs used. Larger prospective observational studies are warranted to elucidate independent risk factors associated with clinically significant QTc prolongation in this study population.

Drug-Induced QT Interval Prolongation

The correct measurement of the QT interval (using the QT correction formulas, preferably Fridericia and Framingham) as well as a correct interpretation of the causes and of the clinical consequences of a QT prolongation is very important in clinical practice. Drug-induced long QT syndrome (DILQTS) is one of the most common causes of LQTS. In the diagnosis and management of the DILQTS, it can be useful to follow the three-step rule presented in this article: detailed pharmacological anamnesis and correct ECG interpretation; database search and clinical interpretation; confirmatory test.

Skin Sympathetic Nerve Activity and the Short-Term QT Interval Variability in Patients With Electrical Storm

Background: Skin sympathetic nerve activity (SKNA) and QT interval variability are known to be associated with ventricular arrhythmias. However, the relationship between the two remains unclear.Objective: The aim was to test the hypothesis that SKNA bursts are associated with greater short-term variability of the QT interval (STVQT) in patients with electrical storm (ES) or coronary heart disease without arrhythmias (CHD) than in healthy volunteers (HV).Methods: We simultaneously recorded the ECG and SKNA during sinus rhythm in patients with ES (N = 10) and CHD (N = 8) and during cold-water pressor test in HV (N = 12). The QT and QTc intervals were manually marked and calculated within the ECG. The STVQT was calculated and compared to episodes of SKNA burst and non-bursting activity.Results: The SKNA burst threshold for ES and HV was 1.06 ± 1.07 and 1.88 ± 1.09 μV, respectively (p = 0.011). During SKNA baseline and burst, the QT/QTc intervals and STVQT for ES and CHD were significantly higher than those of the HV. In all subjects, SKNA bursts were associated with an increased STVQT (from 6.43 ± 2.99 to 9.40 ± 5.12 ms, p = 0.002 for ES; from 9.48 ± 4.40 to 12.8 ± 5.26 ms, p = 0.016 for CHD; and from 3.81 ± 0.73 to 4.49 ± 1.24 ms, p = 0.016 for HV). The magnitude of increased STVQT in ES (3.33 ± 3.06 ms) and CHD (3.34 ± 2.34 ms) was both higher than that of the HV (0.68 ± 0.84 ms, p = 0.047 and p = 0.020).Conclusion: Compared to non-bursting activity, SKNA bursts were associated with a larger increase in the QTc interval and STVQT in patients with heart disease than in HV.

Prevalence And Risk Factors of Electrocardiogram Abnormalities In Patients With Rheumatoid Arthritis: A Machine Learning Study With Follow-Up Data

OBJECTIVES: Electrocardiogram (ECG) abnormalities could predict some subsequent cardiovascular events. Cardiac involvement is a major extra-articular manifestation in rheumatoid arthritis (RA). We aimed to determine the prevalence of three major ECG abnormalities in RA patients, discover the associated ECG abnormalities associated with machine learning (ML) approaches, and then examine these preselected factors in the follow-up patients with traditional Cox regression. METHODS: Consecutive RA patients’ records were retrieved from the hospital database; about one-third of patients had follow-up data. Abnormal ECGs with clinical significance were grouped into non-specific ST-segment/T-wave changes, QT interval prolongation, and QRS-T angle increase. Machine learning approaches assessed the associated factors of these abnormalities. The top-important factors selected by the most optimal ML would be used to construct Cox regression models. RESULTS: Two hundred twenty-six patients were enrolled for the first step cross-sectional study. Non-specific ST-T changes (27%) were the most prevalent abnormalities among patients with abnormal ECGs. Random forest models had the best performance in the discovery of associated factors for three outcomes. Cox regression validated that rheumatoid factor and low-density lipoprotein were common risk factors within those three abnormalities. Hypertension, ESR, and serum immunoglobulin G were influential factors for non-specific ST-T changes, prolonged QT interval, and increased QRS-T angle specifically. CONCLUSION: Non-specific ST-T changes were the most common abnormalities seen in ECGs of RA patients. Our finding suggests that rheumatoid factor, LDL, hypertension, and inflammatory indicators are important risk factors for these ECG abnormalities.

A Novel QT Interval Analysis Method Based On Continuous Wavelet Transform and Philips Algorithm

QT surveillance is the most vital appliance to detect the possibility of sudden death sourced by using pro-arrhythmic drugs treating abnormal conditions in the heart. The repolarization of ventricles makes QT interval surveillance difficult since noisy conditions and individual cardiac situations. Besides, an automated QT algorithm is crucial due to a manual QT measurement with some disadvantages such as fatigue condition in reading long records. In this study, a fully novel automated method combining Continuous Wavelet Transform and Philips method was established to perform QT interval analysis. ECG recordings were obtained from PhyisoNet database marked by manual and standard automated methods. The proposed algorithm had scores of 15.46 and 11.87 millisecond mean error with 11.85 and 9.91 millisecond standard deviation in terms of gold and silver standards, respectively. Also, the entire QT database was utilized in order to test the algorithm performance with the score of 12.89 and 9.76 millisecond mean and standard deviation errors, respectively. The present algorithm performance had scores of -0.21±7.81 at golden standard, and -4.10±18.21 millisecond error for the whole QT database tests, respectively. The proposed algorithm is attained to more stable and robust results with a higher performance than the previous comparable studies.