Drug-induced QT prolongation is the leading cause of FDA nonapproval and a key safety concern for many medications, including cardiac drugs, antibiotics, psychotropic drugs, and oncology treatments. Conventional automated QT algorithms have shown variable reliability in recordings with abnormal or irregular morphology, with misclassification rates up to 75%. This article presents SafeBeat Rx, an AI-powered ECG platform that provides beat-by-beat ECG analysis, enabling full transparency and optional adjustment of measurements for each individual ECG heartbeat. Validated against expert cardiologist adjudication, SafeBeat addresses a critical gap in postdischarge cardiac surveillance and represents a novel approach to guideline-adherent pharmacological disease management.

Long QT syndrome (LQTS) is a primary risk factor for ventricular arrhythmias and sudden cardiac death in children. Accurate corrected QT intervals (QTc) measurement is imperative but challenging for non-heart-rhythm specialists, especially in children. We developed and evaluated pedQTNet, a deep neural network model for estimating QTc and detecting LQTS in pediatric patients. We analyzed a cohort of 37,992 patients aged 0-18 years with 65,370 ECGs annotated by pediatric electrophysiologists (PEPs) between 2010 and 2020. Using PEP-annotated QTc measurements as ground truth, pedQTNet was trained and calibrated on raw ECG waveforms to optimize QTc estimation and LQTS classification. Performance was compared to GE Healthcare's Marquette 12SL algorithm, and to PEPs in cross-validation, as well as an additional prospective set of 200 ECGs. In 10-fold cross-validation, pedQTNet estimated QTc's with a mean absolute error (MAE) of 18.8 ms (95% CI: 18.4-19.2) and predicted LQTS at 470 ms with 85% sensitivity (83%-87%), 87% specificity (87%-88%), positive likelihood ratio (PLR) of 6.7 (6.5-7.0), and negative likelihood ratio (NLR) of 0.17 (0.15-0.19), outperforming Marquette 12SL. In the prospective set, pedQTNet had higher sensitivity than PEPs (100% [69%-100%] vs. 71% [53%-85%], P < 0.05), and a lower but not statistically significant NLR (0.00 [0.00-0.70] vs. 0.30 [0.18-0.50], P = 0.2). PedQTNet demonstrated high QTc estimation accuracy and reliable LQTS detection, outperforming a commercial tool and on par with expert interpretation. Its strong performance supports its clinical use for scalable, automated pediatric ECG screening and LQTS risk assessment, offering a practical tool for enhancing pediatric cardiac care.

Pacritinib, an inhibitor of JAK2/IRAK1/ACVR1 that is devoid of JAK1 activity, approved for treating myelofibrosis in patients with severe thrombocytopenia, carries a label warning for QT interval prolongation. To evaluate the cardiac safety of pacritinib, a randomized, placebo- and active-controlled, single-dose thorough QT (TQT) study was conducted in healthy subjects, and a dose-finding study (PAC203) was conducted in patients with myelofibrosis. In the TQT study, 42 subjects received single doses of pacritinib 400 mg, moxifloxacin 400 mg (positive control), and placebo in a crossover design. The maximal placebo-corrected change in QT interval with pacritinib was -9.7 ms (90% confidence interval: -13.4, -6.1), and all upper confidence bounds were <10 ms. A small, clinically irrelevant inverse concentration-QTc relationship was observed. No subject administered pacritinib had a QTc interval using Fridericia's formula (QTcF) >480 ms or a change from baseline in QTcF >30 ms. Moreover, in the PAC203 trial, there was no correlation between pacritinib exposure and QTcF at weeks 12 or 24. Median QTcF changes from pre-dose to 4 h post-dose were minimal (+2.7 and -0.3 ms, respectively), and no dose-response relationship was identified. These findings suggest that pacritinib exposure is unlikely to be correlated with QT prolongation.

Histone deacetylase (HDAC) inhibitors are approved for cancer treatment and are being investigated for a wide range of other diseases. Despite their therapeutic promise, clinical studies have reported cardiac side effects, particularly electrocardiogram (EKG) abnormalities, with QT interval prolongation being one of the most consistently reported findings. The mechanisms underlying these cardiac effects remain unclear. In this study, we investigated the role of HDAC3 in cardiac electrophysiology. We found that postnatal depletion of cardiac HDAC3 in mice caused QT interval prolongation, recapitulating the EKG abnormalities reported with HDAC inhibitor use. Adult-onset inducible depletion of cardiac HDAC3 induced additional EKG abnormalities, including T-wave flattening, inversion, and biphasic T waves, which are also observed clinically. Loss of HDAC3 deacetylase activity, without affecting HDAC3 protein levels, was sufficient to induce QT prolongation. Disruption of HDAC3 function altered the expression of ion channel genes, including the downregulation of potassium channel genes such as Kcnh2, Kcne1, and Kcnip2. Moreover, a single dose of HDAC inhibitors, romidepsin or mocetinostat, caused reversible QT prolongation in mice. Consistent with these findings, HDAC inhibitor treatment altered the expression of potassium channel genes, with a predominant downregulation of multiple Kcn family members, including Kcnq1, Kcnh2, and Kcnip2. These findings establish HDAC3 enzymatic activity as a key regulator of cardiac repolarization and provide mechanistic insight into HDAC inhibitor-associated cardiotoxicity.

Nacubactam is a β-lactamase inhibitor. Early-phase studies with 1000/2000/4000/8000 mg nacubactam monotherapy were not powered to assess proarrhythmic risk. Therefore, this analysis pooled electrocardiogram data from two placebo-controlled studies to quantify potential effects of nacubactam on cardiac repolarization. The primary endpoint was ΔQTcF, denoting the time-matched change in QT interval corrected for heart rate. The primary estimand was mean ΔΔQTcF (placebo-corrected ΔQTcF) in healthy adults at double the maximum clinically relevant exposure levels of nacubactam/metabolites. Mixed models for repeated measures and concentration-QTc modeling were used to analyze ΔQTcF. From Mixed models for repeated measures, most 90% confidence intervals for ΔΔQTcF fell below 10 ms, but there were some inconsistencies at 3 h after 1000/2000 mg doses, where 90% confidence intervals straddled 10 ms. However, after 4000/8000 mg doses, 90% confidence intervals fell below 10 ms, with no trend of increasing QTcF prolongation with increasing dose in pooled or individual study data. Moreover, from concentration-QTc modeling, 90% confidence intervals for ΔΔQTcF were below 10 ms for maximum clinically relevant exposure levels, with no evidence of concentration-dependent effects. Results from concentration-QTc modeling fell below the safety margin of 10 ms at relevant analyte levels. While certain confidence intervals from Mixed models for repeated measures straddled 10 ms at 3 h after 1000/2000 mg doses, these inconsistencies were not sustained and not evident at higher doses. Overall, findings support the safety of nacubactam and justify evaluation in larger, multidose, combination-therapy studies and provide regulatory-relevant evidence supporting progression to multidose and combination therapy trials without the need for a dedicated thorough QT study.

Drug-induced QTc prolongation has emerged as a major, preventable cause of malignant ventricular arrhythmias in cancer therapy. This narrative review integrates mechanistic, clinical, and practical insights into QTc prolongation associated with antineoplastic and supportive medications. We outline direct ionic mechanisms, most notably hERG/IKr blockade and PI3K/Akt pathway modulation, as well as indirect contributors, including cytokine-mediated myocarditis, electrolyte disturbances, and structural remodeling. High-risk agents, including arsenic trioxide, vandetanib, nilotinib, sunitinib, and ribociclib, are contrasted with lower-risk alternatives and QT-sparing strategies. Polypharmacy and drug-drug interactions are emphasized as major real-world amplifiers of risk. Among supportive medications, fluoroquinolones, azole antifungals, and certain antiemetics frequently interact with QT-active anticancer agents, whereas beta-lactams, echinocandins, isavuconazole, and palonosetron may represent safer alternatives. We propose a mechanism-to-bedside framework that incorporates baseline ECG and electrolyte assessment, QT-risk stratification, and clear intervention thresholds (QTc ≥ 500 ms or an increase of ≥ 60 ms). Acute management of torsades de pointes, including intravenous magnesium and overdrive pacing, as well as structured hold-and-rechallenge algorithms, is also summarized. Finally, we highlight system-level measures, such as drug interaction stewardship, standardized discharge communication, and clinician education, to shift cardio-oncology practice from reactive arrhythmia management toward anticipatory prevention. This integrative synthesis aims to preserve oncologic efficacy while mitigating arrhythmic risk in modern cancer care.

Cardiac toxicity from QT-prolonging drugs can precipitate malignant ventricular arrhythmias in susceptible individuals, and family screening may clarify inherited risk. We report a 33-year-old woman with a history of postpartum cardiac arrest treated with a secondary-prevention implantable cardioverter-defibrillator (ICD) who developed an electrical storm after self-administration of a single low dose of amitriptyline (12.5 mg). ICD interrogation documented 176 episodes of ventricular fibrillation requiring repeated shocks, followed by complete battery depletion, hemodynamic collapse, and the need for venoarterial extracorporeal membrane oxygenation and continuous renal replacement therapy. The admission electrocardiogram showed marked QT prolongation (QTc 651 ms), with previously documented prolonged baseline QTc values. Targeted next-generation sequencing identified a novel SCN5A missense variant (NM_000335.5:c.5738G > A) and a rare pathogenic KCNQ1 splice variant (NM_000218.3:c.1032G > C), cascade testing across the family demonstrated variable expressivity among carriers. Given a suspected contribution of late sodium current, a mechanism-based strategy was implemented with mexiletine added to propranolol and overdrive pacing (90 bpm). This case underscores the risk of malignant ventricular arrhythmias after exposure to QT-prolonging agents even at low doses, and supports genotype-informed, mechanism-based therapy to mitigate arrhythmic risk in patients with marked QT prolongation.

Objective. Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. Biological systems, including the heart, exhibit complex nonlinear dynamics arising from interactions between their subsystems. Information-theoretic measures, particularly entropy-based methods, provide a framework to quantify these interactions. Using ECG recordings, we investigate information flow between heart rhythm and ventricular repolarization to identify potential markers of pathological alterations in cardiac electrical activity.Approach. Entropy-based measures of information transfer were derived from beat-to-beat ECG time series using a window-based approach and subsequently averaged at the subject level. These features were used as inputs to supervised machine learning models to discriminate patients with congenital long QT syndrome (LQTS) from healthy controls. Model performance was evaluated using repeated stratified train-test splits, and classification robustness was assessed across multiple runs using standard performance metrics, including the area under the receiver operating characteristic curve. The explainable artificial intelligence techniques were applied. SHapley Additive exPlanations were used to quantify the contribution of entropy-based features to the model predictions. This post-hoc explainability analysis enabled systematic assessment of feature importance while preserving the predictive performance of the models.Results. The proposed approach achieved high and stable classification performance across repeated validation runs. Both random forest (RF) and support vector machine (SVM) classifiers demonstrated high discrimination between LQTS patients and healthy controls, with consistently high area under the curve. For RF a mean accuracy of 95.9%, mean sensitivity of 95.9%, and mean specificity of 92.9% were achieved across repeated runs. For SVM the corresponding mean values were 93.1%, 93.1%, and 92.0%, respectively.Conclusions.Explainability analysis revealed a dominant contribution of multivariate and conditional information flow features compared with single-source entropy measures, highlighting the relevance of joint and conditional interactions in the classification patterns.

Prolongation of the QT interval is a known precursor to serious arrhythmias and sudden cardiac death, often triggered by medication use. Current medication risk evaluation platforms rely on literature-based synthesis and may lag behind real-world developments. We aimed to evaluate whether a machine learning (ML) model trained on real-world genomic and medication data can identify associations between drug use and QTc duration, potentially enabling automated risk detection in clinical workflows. We included 10,208 individuals from the FinnGen biobank Expansion Area 3 substudy, integrating prescription records, clinical variables, and genetic information. We applied a nested-cross-validation approach to develop an ML framework to predict QTc duration using clinical characteristics, recent medication purchases, and polygenic score for QTc duration. We performed conventional linear regression analyses to estimate the robustness of the findings. Only a minority of ML-detected drug-QTc associations aligned with known effects listed in expert-curated reference. Several apparent false positives were observed, and effect sizes for true positives, such as amiodarone, were small and likely interpreted as clinically not meaningful (+1 ms in ML vs. +49 ms in linear regression). These findings highlight challenges in using ML to detect meaningful drug effects on ECG. ML models did not reliably identify medications associated with QT-interval prolongation. Consequently, risk quantification using QTc as an intermediate marker of electrophysiological vulnerability was limited in this framework. While new approaches continue to develop in medication safety assessment, a systematic evidence review conducted by clinical pharmacology experts is unlikely to be supplanted in the foreseeable future.

A historical cohort study in southern Iran investigated the effect of lopinavir-ritonavir treatment, with or without azithromycin and/or hydroxychloroquine, on QT interval prolongation in 500 confirmed COVID-19 patients. The outcomes showed a notable increase in the mean corrected QT interval post-treatment, specifically from 415.75±46.848 ms to 437.91±50.455 ms. QTc prolongation was observed only in patients receiving lopinavir-ritonavir monotherapy who also presented with cardiovascular conditions, angina pectoris, or hypomagnesemia, whereas those receiving concomitant therapy with azithromycin or hydroxychloroquine did not exhibit this effect. Other than one-third of patients with comorbidities or additional QT-prolonging drugs, aside from hypertension and obesity, had clinically the same potential of QT prolongation, and only 10.6% of patients had the problem. The work stresses the significance of careful monitoring of cardiac health, especially for those patients who have electrolyte imbalances and cardiovascular diseases as underlying diseases when they use lopinavir-ritonavir with other QT-prolonging medications.