OBJECTIVE: Single-lead electrocardiograms (ECGs) can be recorded using widely available devices such as smartwatches and handheld ECG recorders. Such devices have been approved for atrial fibrillation (AF) detection. However, little evidence exists on the reliability of single-lead ECG interpretation. We aimed to assess the level of agreement on detection of AF by independent cardiologists interpreting single lead ECGs, and to identify factors influencing agreement.
METHODS: In a population-based AF screening study, adults aged ≥65 years old recorded four single-lead ECGs per day for 1-4 weeks using a handheld ECG recorder. ECGs showing signs of possible AF were identified by a nurse, aided by an automated algorithm. These were reviewed by two independent cardiologists who assigned participant- and ECG-level diagnoses. Inter-rater reliability of AF diagnosis was calculated using linear weighted Cohen\'s kappa (kw).
RESULTS: Out of 2,141 participants and 162,515 ECGs, only 1,843 ECGs from 185 participants were reviewed by both cardiologists. Agreement was moderate: kw = 0.48 (95% CI, 0.37-0.58) at participant-level; and kw = 0.58 (0.53-0.62) at ECG-level. At participant-level, agreement was associated with the number of adequate-quality ECGs recorded, with higher agreement in participants who recorded at least 67 adequate-quality ECGs. At ECG-level, agreement was associated with ECG quality and whether ECGs exhibited algorithm-identified possible AF.
CONCLUSIONS: Inter-rater reliability of AF diagnosis from single-lead ECGs was found to be moderate in older adults. Strategies to improve reliability might include participant and cardiologist training and designing AF detection programmes to obtain sufficient ECGs for reliable diagnoses.

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.

BACKGROUND: The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms.
METHODS: Ovid MEDLINE, CINAHL, EMBASE, Cochrane, PubMed and Scopus were searched from inception through to 8th of December 2023. A thorough search of the grey literature and reference lists of relevant articles was also performed to identify additional studies. Articles were included if they reported the use of ANN for ECG interpretation of Acute Coronary Syndrome in the Emergency Department patients.
RESULTS: The search yielded a total of 244 articles. After removing duplicates and excluding non-relevant articles, 14 remained for analysis. There was significant heterogeneity in the types of ANN models used and the outcomes assessed, making direct comparisons challenging. Nevertheless, ANN appeared to demonstrate higher accuracy than physician interpreters for the evaluated outcomes and this proved independent of both specialty and years of experience.
CONCLUSIONS: The interpretation of ECGs in patients with suspected ACS using ANN appears to be accurate and potentially superior when compared to human interpreters and computerised algorithms. This appears consistent across various ANN models and outcome variables. Future investigations should emphasise ANN interpretation of ECGs in patients with ACO, where rapid and accurate diagnosis can significantly benefit patients through timely access to reperfusion therapies.

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.