Serial electrocardiogram recordings revealed a high prevalence of QT interval prolongation in patients with tuberculosis receiving fluoroquinolones.

BACKGROUND: Fluoroquinolones, crucial components of treatment regimens for drug-resistant tuberculosis (TB), are associated with QT interval prolongation and risks of fatal cardiac arrhythmias. However, few studies have explored dynamic changes in the QT interval in patients receiving QT-prolonging agents. METHODS: This prospective cohort study recruited hospitalized patients with TB who received fluoroquinolones. The study investigated the variability of the QT interval by using serial electrocardiograms (ECGs) recorded four times daily. This study analyzed the accuracy of intermittent and single-lead ECG monitoring in detecting QT interval prolongation. RESULTS: This study included 32 patients. The mean age was 68.6 ± 13.2 years. The results revealed mild-to-moderate and severe QT interval prolongation in 13 (41%) and 5 (16%) patients, respectively. The incremental yields in sensitivity of one to four daily ECG recordings were 61.0%, 26.1%, 5.6%, and 7.3% in detecting mild-to-moderate QT interval prolongation, and 66.7%, 20.0%, 6.7%, and 6.7% in detecting severe QT interval prolongation. The sensitivity levels of lead II and V5 ECGs in detecting mild-to-moderate and severe QT interval prolongation exceeded 80%, and their specificity levels exceeded 95%. CONCLUSION: This study revealed a high prevalence of QT interval prolongation in older patients with TB who receive fluoroquinolones, particularly those with multiple cardiovascular risk factors. Sparsely intermittent ECG monitoring, the prevailing strategy in active drug safety monitoring programs, is inadequate owing to multifactorial and circadian QT interval variability. Additional studies performing serial ECG monitoring are warranted to enhance the understanding of dynamic QT interval changes in patients receiving QT-prolonging anti-TB agents.

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1.

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.