Phosphoinositide 3-kinase (PI3K) inhibitors have shown synergistic anticancer effects with endocrine therapy against ER+/PIK3CA-mutated breast cancer. PI3K inhibitors for cancer therapy are becoming more common. There is an increasing need to understand their cardiac adverse events. In this report, we describe the features of near-fatal mixed arrhythmias in a patient who was undergoing a phase Ib clinical study of PI3Kα inhibitor with fulvestrant. Subsequently, the patient survived by cardiopulmonary resuscitation and therefore did not die. This case highlights that PI3K inhibitors can induce QT/QTc prolongation and predispose patients to TdP. The combination of QT/QTc prolongation in combination with prolonged cardiac repolarization, such as an AV block during treatment with PI3Kα inhibitor, may aggravate the occurrence of TdP. It is likely to be a safer strategy to adjust the standard of discontinuing drugs and continuing drugs (QTc interval was <500 and <60 ms at baseline) or choose other types of alternative treatment options. This report provided some ideas for clinicians to identify early and prevent the occurrence of fatal arrhythmias during anticancer treatment.

UNASSIGNED: A notched P-wave is associated with the occurrence of atrial fibrillation (AF). However, the association between a notched P-wave and AF recurrence in patients who have undergone a catheter ablation for AF is unclear.
UNASSIGNED: We enrolled 100 subjects who underwent catheter ablation for AF (paroxysmal AF: 60 cases; persistent AF: 40 cases). Twelve-lead electrocardiography (ECG) was conducted, and the peak-to-peak distance in the M shape was calculated automatically using a 12-lead ECG analysis system. A notched P-wave was defined as a P-wave with an M-shape and a peak-to-peak distance of ≥20 ms in lead II. We compared the recurrence of AF in the patients with notched P-wave and the others.
UNASSIGNED: The mean follow-up period was 12 ± 8 months, and a recurrence of AF was observed in 28 patients. The recurrence of AF in the notched P-wave group was significantly higher than that in the controls (log rank 5.14, p = .023). A notched P-wave was a significant predictor of the recurrence of AF after adjustment for age, gender, history of heart failure, history of catheter ablation, persistent AF, use of antiarrhythmic drugs, and the left atrial volume index (hazard ratio 2.470, 95% confidence interval 1.065-5.728, p = .035).
UNASSIGNED: Automatically identified notched P-waves with peak-to-peak distance ≥20 ms were associated with AF recurrence in patients who had undergone catheter ablation.

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BACKGROUND: We aimed to assess the efficacy of the neutrophil elastase inhibitor, sivelestat, in the treatment of sepsis-induced acute respiratory distress syndrome (ARDS) and septic cardiomyopathy (SCM).
METHODS: Between January 2019 and December 2021, we conducted a randomized trial on patients who had been diagnosed with sepsis-induced acute respiratory distress syndrome (ARDS) and septic cardiomyopathy (SCM) at Wuhan Union Hospital. The patients were divided into two groups by random envelop method, the Sivelestat group and the Control group. We measured the serum concentrations of Interleukin (IL)-6, IL-8, Tumor necrosis factor-α (TNF-α), and High-mobility group box 1 (HMGB1) at five time points, which were the baseline, 12 h, 24 h, 48 h, and 72 h after admission to the ICU. We evaluated the cardiac function by sonography and the heart rate variability (HRV) with 24-hour Holter recording between the time of admission to the intensive care unit (ICU) and 72 h after Sivelestat treatment.
RESULTS: From January 2019 to December 2021, a total of 70 patients were included in this study. The levels of IL-6, IL-8, and TNF-α were significantly lower in the Sivelestat group at different time points (12 h, 24 h, 48 h, and 72 h). HMGB1 levels were significantly lower at 72 h after Sivelestat treatment (19.46 ± 2.63pg/mL vs. 21.20 ± 2.03pg/mL, P = 0.003). The stroke volume (SV), tricuspid annular plane systolic excursion (TAPSE), early to late diastolic transmitral flow velocity (E/A), early (e\') and late (a\') diastoles were significantly low in the Control group compared with the Sivelestat group. Tei index was high in the Control group compared with the Sivelestat group (0.60 ± 0.08 vs. 0.56 ± 0.07, P = 0.029). The result of HRV showed significant differences in standard deviation of normal-to-normal intervals (SDNN), low frequency (LF), and LF/HF (high frequency) between the two groups.
CONCLUSIONS: Sivelestat can significantly reduce the levels of serum inflammatory factors, improve cardiac function, and reduce heart rate variability in patients with Sepsis-induced ARDS and SCM.

Atrial fibrillation (AF) is a common arrhythmia, and out-of-hospital, wearable, long-term electrocardiogram (ECG) monitoring can help with the early detection of AF. The presence of a motion artifact (MA) in ECG can significantly affect the characteristics of the ECG signal and hinder early detection of AF. Studies have shown that (a) using reference signals with a strong correlation with MAs in adaptive filtering (ADF) can eliminate MAs from the ECG, and (b) artificial intelligence (AI) algorithms can recognize AF when there is no presence of MAs. However, no literature has been reported on whether ADF can improve the accuracy of AI for recognizing AF in the presence of MAs. Therefore, this paper investigates the accuracy of AI recognition for AF when ECGs are artificially introduced with MAs and processed by ADF. In this study, 13 types of MA signals with different signal-to-noise ratios ranging from +8 dB to -16 dB were artificially added to the AF ECG dataset. Firstly, the accuracy of AF recognition using AI was obtained for a signal with MAs. Secondly, after removing the MAs by ADF, the signal was further identified using AI to obtain the accuracy of the AF recognition. We found that after undergoing ADF, the accuracy of AI recognition for AF improved under all MA intensities, with a maximum improvement of 60%.

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

The aim of this study was to observe electric cardiac activity in real working conditions, with the application of Holter and the electrocardiogram in search and rescue dogs. Thirty-one handlers of search and rescue dogs voluntarily participated in this study. Nine dogs were selected to wear the Holter, and twenty-three were submitted to electrocardiographic recordings (one dog, excluded by Holter examination, was then included in the ECG group). Our results showed few cardiac rhythm alterations, such as escape beats, premature ventricular beat, and depression and elevation of the ST segment, particularly during the working phase in the Holter group and during recovery time immediately after activity in the electrocardiographic group. Detected alterations in real working conditions may provide more information than routine checks, and Holter monitoring can be more functional. However, not all dogs tolerate wearing the Holter harness, and more time is thus needed to apply the equipment. In addition, the results are not immediate, and the absence of water is essential because it would damage the equipment.

BACKGROUND: Approximately 10% of newborns require assistance at delivery, and heart rate (HR) is the primary vital sign providers use to guide resuscitation methods. In 2016, the American Heart Association (AHA) suggested electrocardiogram in the delivery room (DR-ECG) to measure heart rate during resuscitation. This study aimed to compare the frequency of resuscitation methods used before and after implementation of the AHA recommendations.
METHODS: This longitudinal retrospective cohort study compared a pre-implementation (2015) cohort with two post-implementation cohorts (2017, 2021) at our Level IV neonatal intensive care unit.
RESULTS: An initial increase in chest compressions at birth associated with the introduction of DR-ECG monitoring was mitigated by focused educational interventions on effective ventilation. Implementation was accompanied by no changes in neonatal mortality.
CONCLUSIONS: Investigation of neonatal outcomes during the ongoing incorporation of DR-ECG may help our understanding of human and system factors, identify ways to optimize resuscitation team performance, and assess the impact of targeted training initiatives on clinical outcomes.