Background: Recent evidence showed that the characteristics and outcome of those with de novo heart failure (HF) and acutely decompensated chronic heart failure (ADCHF) were different. We aimed to perform a comprehensive search on the clinical characteristics and outcome of patients with de novo HF and ADCHF. Methods: We performed a comprehensive search on de novo/new onset acute HF vs ADCHF from inception up until December 2019. Results: There were 38320 patients from 15 studies. De novo HF were younger and, had less prevalent hypertension, diabetes mellitus, ischaemic heart disease, chronic obstructive pulmonary disease, atrial fibrillation, and history of stroke/transient ischaemic attack compared to ADCHF. Five studies showed a lower NT-proBNP in de novo HF patients, while one study showed no difference. Valvular heart disease as aetiology of heart failure was less frequent in de novo HF, and upon sensitivity analysis, hypertensive heart disease was more frequent in de novo HF. As for precipitating factors, ACS (OR 2.42; I²:89%) was more frequently seen in de novo HF, whereas infection was less frequently (OR 0.69; I²:32%) in ADCHF. De novo HF was associated with a significantly lower 3-month mortality (OR 0.63; I²:91%) and 1-year (OR 0.59; I²:59%) mortality. Meta-regression showed that 1-year mortality did not significantly vary with age (p = .106), baseline ejection fraction (p = .703), or HF reduced ejection fraction (p = .262). Conclusion: Risk factors, aetiology, and precipitating factors of HF in de novo and ADCHF differ. De novo HF also had lower 1-year mortality and 3-month mortality compared to ADCHF.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical practice. This activity is a monotonous and abstract task that relies on the experience of understanding ECG waveform meaning, including P-wave, QRS-complex, and T-wave. Such a manual process depends on signal quality and the number of leads. ECG signal classification based on deep learning (DL) has produced an automatic interpretation; however, the proposed method is used for specific abnormality conditions. When the ECG signal morphology change to other abnormalities, it cannot proceed automatically. To generalize the automatic interpretation, we aim to delineate ECG waveform. However, the output of delineation process only ECG waveform duration classes for P-wave, QRS-complex, and T-wave. It should be combined with a medical knowledge rule to produce the abnormality interpretation. The proposed model is applied for atrial fibrillation (AF) identification. This study meets the AF criteria with RR irregularities and the absence of P-waves in essential oscillations for even more accurate identification. The QT database by Physionet is utilized for developing the delineation model, and it validates with The Lobachevsky University Database. The results show that our delineation model works properly, with 98.91% sensitivity, 99.01% precision, 99.79% specificity, 99.79% accuracy, and a 98.96% F1 score. We use about 4058 normal sinus rhythm records and 1804 AF records from the experiment to identify AF conditions that are taken from three datasets. The comprehensive testing has produced higher negative predictive value and positive predictive value. This means that the proposed model can identify AF conditions from ECG signal delineation. Our approach can considerably contribute to AF diagnosis with these results.

Background: The electrocardiogram (ECG) is a widely used diagnostic that observes the heart activities of patients to ascertain a heart abnormality diagnosis. The artifacts or noises are primarily associated with the problem of ECG signal processing. Conventional denoising techniques have been proposed in previous literature; however, some lacks, such as the determination of suitable wavelet basis function and threshold, can be a time-consuming process. This paper presents end-to-end learning using a denoising auto-encoder (DAE) for denoising algorithms and convolutional-bidirectional long short-term memory (ConvBiLSTM) for ECG delineation to classify ECG waveforms in terms of the PQRST-wave and isoelectric lines. The denoising reconstruction using unsupervised learning based on the encoder-decoder process can be proposed to improve the drawbacks. First, The ECG signals are reduced to a low-dimensional vector in the encoder. Second, the decoder reconstructed the signals. The last, the reconstructed signals of ECG can be processed to ConvBiLSTM. The proposed architecture of DAE-ConvBiLSTM is the end-to-end diagnosis of heart abnormality detection.Results: As a result, the performance of DAE-ConvBiLSTM has obtained an average of above 98.59% accuracy, sensitivity, specificity, precision, andF1score from the existing studies. The DAE-ConvBiLSTM has also experimented with detecting T-wave (due to ventricular repolarisation) morphology abnormalities.Conclusion: The development architecture for detecting heart abnormalities using an unsupervised learning DAE and supervised learning ConvBiLSTM can be proposed for an end-to-end learning algorithm. In the future, the precise accuracy of the ECG main waveform will affect heart abnormalities detection in clinical practice.

Background: Infections after cardiac implantable electronic device (CIED) placement are associated with significant morbidity and mortality. The incidence of CIED is increasing overtime despite the optimal use of antimicrobial agents. This systematic review and meta‐analysis will address the latest evidence on the use of AE to mitigate the risk of CIED infection, and which subset of patients will they benefit the most.Methods: We performed a comprehensive search on topics that assesses antibiotic envelope and implantable cardiac electronic device up until August 2019.Results: There were a total of 32,329 subjects from six studies. Antibiotic envelope was associated with a lower risk of major infection with OR 0.42 [0.19, 0.97], P = .04; I²: 58% and HR 0.52 [0.32, 0.85], P = .009; I²: 80%. Upon sensitivity analysis by removing a study, the OR became 0.40 [0.27, 0.59], P < .001; I²: 46%. Subgroup analysis for 12 months’ infection was OR 0.65 [0.43, 0.99], P = .04; I²: 49%. Meta‐analysis of propensity‐matched cohort showed a reduced risk of infection with AE (OR of 0.14 [0.05, 0.41], P < .001; I²:0%). Mortality was similar in both AE and control groups. Antibiotic envelope reduced the incidence of infection in patients receiving high‐power device (OR 0.44 [0.27, 0.73], P = .001; I²:0%) but not low‐power device.Conclusion: Antibiotic envelope (TYRX) was found to be safe and effective in reducing the risk of major infections in high‐risk patients receiving CIED implantation, especially in those receiving high‐power CIED.