METHODS: We included 446 SARS-CoV-2 RT-PCR-positive patients taking at least one treatment drug for COVID-19 within a period of one month (March-April 2020). In addition to COVID-19-related treatment (HCQ/PI), concomitant drugs with risks of QTc prolongation were considered. We defined QTc prolongation as QTc interval of ≥470 ms in postpubertal males, and ≥480 ms in postpubertal females.
RESULTS AND DISCUSSION: QTc prolongation events occurred in 28/446 (6.3%) patients with an incidence rate of 1 case per 100 person-days. A total of 26/28 (93%) patients who had prolonged QTc intervals received at least two pro-QT drugs. Multivariate analysis showed that HCQ and PI combination therapy had five times higher odds of QTc prolongation as compared to HCQ-only therapy after controlling for age, cardiovascular disease, SIRS and the use of concurrent QTc-prolonging agents besides HCQ and/or PI (OR 5.2; 95% CI, 1.11-24.49; p = 0.036). Independent of drug therapy, presence of SIRS resulted in four times higher odds of QTc prolongation (OR 4.3; 95% CI, 1.66-11.06; p = 0.003). In HCQ-PI combination group, having concomitant pro-QT drugs led to four times higher odds of QTc prolongation (OR 3.8; 95% CI, 1.53-9.73; p = 0.004). Four patients who had prolonged QTc intervals died but none were cardiac-related deaths.
WHAT IS NEW AND CONCLUSION: In our cohort, hydroxychloroquine monotherapy had low potential to increase QTc intervals. However, when given concurrently with protease inhibitors which have possible or conditional risk, the odds of QTc prolongation increased fivefold. Interestingly, independent of drug therapy, the presence of systemic inflammatory response syndrome (SIRS) resulted in four times higher odds of QTc prolongation, leading to the postulation that some QTc events seen in COVID-19 patients may be due to the disease itself. ECG monitoring should be continued for at least a week from the initiation of treatment.
METHODS AND RESULTS: In this multicentre, open-label study, we randomly assigned 203 participants to undergo one additional 24-h Holter monitoring (control group, n = 98) vs. 30-day smartphone ECG monitoring (intervention group, n = 105) using KardiaMobile (AliveCor®, Mountain View, CA, USA). Major inclusion criteria included age ≥55 years old, without known AF, and ischaemic stroke or transient ischaemic attack (TIA) within the preceding 12 months. Baseline characteristics were similar between the two groups. The index event was ischaemic stroke in 88.5% in the intervention group and 88.8% in the control group (P = 0.852). AF lasting ≥30 s was detected in 10 of 105 patients in the intervention group and 2 of 98 patients in the control group (9.5% vs. 2.0%; absolute difference 7.5%; P = 0.024). The number needed to screen to detect one AF was 13. After the 30-day smartphone monitoring, there was a significantly higher proportion of patients on oral anticoagulation therapy at 3 months compared with baseline in the intervention group (9.5% vs. 0%, P = 0.002).
CONCLUSIONS: Among patients ≥55 years of age with a recent cryptogenic stroke or TIA, 30-day smartphone ECG recording significantly improved the detection of AF when compared with the standard repeat 24-h Holter monitoring.
METHODS: The dataset used in this study consist of ECG data collected from 45 ADHD, 62 ADHD+CD, and 16 CD patients at the Child Guidance Clinic in Singapore. The ECG data were segmented into 2 s epochs and directly used to train our 1-dimensional (1D) convolutional neural network (CNN) model.
RESULTS: The proposed model yielded 96.04% classification accuracy, 96.26% precision, 95.99% sensitivity, and 96.11% F1-score. The Gradient-weighted class activation mapping (Grad-CAM) function was also used to highlight the important ECG characteristics at specific time points that most impact the classification score.
CONCLUSION: In addition to achieving model performance results with our suggested DL method, Grad-CAM's implementation also offers vital temporal data that clinicians and other mental healthcare professionals can use to make wise medical judgments. We hope that by conducting this pilot study, we will be able to encourage larger-scale research with a larger biosignal dataset. Hence allowing biosignal-based computer-aided diagnostic (CAD) tools to be implemented in healthcare and ambulatory settings, as ECG can be easily obtained via wearable devices such as smartwatches.