Early warning of atrial fibrillation using deep learning.

Atrial fibrillation (AF), the most prevalent cardiac rhythm disorder, significantly increases hospitalization and health risks. Reverting from AF to sinus rhythm (SR) often requires intensive interventions. This study presents a deep-learning model capable of predicting the transition from SR to AF on average 30.8 min before the onset appears, with an accuracy of 83% and an F1 score of 85% on the test data. This performance was obtained from R-to-R interval signals, which can be accessible from wearable technology. Our model, entitled Warning of Atrial Fibrillation (WARN), consists of a deep convolutional neural network trained and validated on 24-h Holter electrocardiogram data from 280 patients, with 70 additional patients used for testing and further evaluation on 33 patients from two external centers. The low computational cost of WARN makes it ideal for integration into wearable technology, allowing for continuous heart monitoring and early AF detection, which can potentially reduce emergency interventions and improve patient outcomes.

Automatic multilabel electrocardiogram diagnosis of heart rhythm or conduction abnormalities with deep learning: a cohort study.

BACKGROUND: Market-applicable concurrent electrocardiogram (ECG) diagnosis for multiple heart abnormalities that covers a wide range of arrhythmias, with better-than-human accuracy, has not yet been developed. We therefore aimed to engineer a deep learning approach for the automated multilabel diagnosis of heart rhythm or conduction abnormalities by real-time ECG analysis. METHODS: We used a dataset of ECGs (standard 10 s, 12-channel format) from adult patients (aged ≥18 years), with 21 distinct rhythm classes, including most types of heart rhythm or conduction abnormalities, for the diagnosis of arrhythmias at multilabel level. The ECGs were collected from three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and annotated by cardiologists. We used these datasets to develop a convolutional neural network approach to generate diagnoses of arrythmias. We collected a test dataset of ECGs from a new group of patients not included in the training dataset. The test dataset was annotated by consensus of a committee of board-certified, actively practicing cardiologists. To evaluate the performance of the model we assessed the F1 score and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, as well as quantifying sensitivity and specificity. To validate our results, findings for the test dataset were compared with diagnoses made by 53 ECG physicians working in cardiology departments who had a wide range of experience in ECG interpretation (range 0 to >12 years). An external public validation dataset of 962 ECGs from other hospitals was used to study generalisability of the diagnostic model. FINDINGS: Our training and validation dataset comprised 180 112 ECGs from 70 692 patients, collected between Jan 1, 2012, and Apr 30, 2019. The test dataset comprised 828 ECGs corresponding to 828 new patients, recorded between Sept 11, 2012, and Aug 30, 2019. At the multilabel level, our deep learning approach to diagnosing heart abnormalities resulted in an exact match in 658 (80%) of 828 ECGs, exceeding the mean performance of physicians (552 [67%] for physicians with 0-6 years of experience; 571 [69%] for physicians with 7-12 years of experience; 621 [75%] for physicians with more than 12 years of experience). Our model had an overall mean F1 score of 0·887 compared with 0·789 for physicians with 0-6 years of experience, 0·815 for physicians with 7-12 years of experience, and 0·831 for physicians with more than 12 years of experience. The model had a mean AUC ROC score of 0·983 (95% CI 0·980-0·986), sensitivity of 0·867 (0·849-0·885) and specificity of 0·995 (0·994-0·996). Promising F1 scores were also obtained from the external public database using our proposed model without any model modifications (mean F1 scores of 0·845 in multilabel and 0·852 in single-label ECGs). INTERPRETATION: Our model is more accurate than physicians working in cardiology departments at distinguishing a range of distinct arrhythmias in single-label and multilabel ECGs, laying a promising foundation for computational decision-support systems in clinical applications. FUNDING: National Natural Science Foundation of China and Hubei Science and Technology Project.

Effects of probiotics combined with early enteral nutrition on endothelin-1 and C-reactive protein levels and prognosis in patients with severe traumatic brain injury.

OBJECTIVE: To investigate the effects of probiotics combined with early enteral nutrition on levels of endothelin-1 (ET-1), C-reactive protein (CRP), and inflammatory factors, and on the prognosis of patients with severe traumatic brain injury (TBI). METHODS: We enrolled 76 adults with severe TBI. The patients were divided randomly into two equal groups administered enteral nutrition with and without probiotics, respectively. Demographic and clinical data including age, sex, Glasgow Coma Scale score, Sequential Organ Failure Score, Acute Physiology, Chronic Health Score, hospitalization, mortality, and infections were recorded. RESULTS: Serum levels of inflammatory factors gradually decreased with increasing treatment time in both groups. However, ET-1 at 15 days, and interleukin (IL)-6, IL-10, tumor necrosis factor (TNF)-α, and CRP at 7 and 15 days decreased significantly more in the combined treatment group. Hospitalization duration and pulmonary infection rates were also significantly reduced in the combined compared with the enteral nutrition alone group. GCS scores at 15 days were significantly lower in the combined compared with the enteral nutrition group. CONCLUSION: Probiotics combined with early enteral nutrition could reduce serum levels of ET-1, CRP, and IL-6, IL-10, and TNF-α, and could thus improve the recovery of patients with severe TBI.

Induction of CD8 T cell cytotoxicity by fecal bacteria from healthy individuals and colorectal cancer patients.

Commensal microbiota modulates the anti-tumor immune response and alters the tumor infiltration of T cells in numerous human malignancies. Moreover, the existence of commensals and microbial metabolites has been directly observed inside numerous epithelial tumors. Their effects on the host immune system, independent of the pre-existing malignancy, are not completely understood. To resolve this issue, we compared immune modulatory roles of the fecal bacteria from healthy individuals and the fecal bacteria from colorectal cancer (CRC) patients. Peripheral blood mononuclear cells that were provided by healthy donors were used as study systems. Overall, fecal bacteria could potently activate the degranulation and cytotoxicity of CD8+ T cells. Interestingly, fecal bacteria from CRC patients in general induced higher degranulation and higher cytotoxicity than fecal bacteria from healthy individuals. These effects were dependent on the presence of antigen-presenting cells, such as monocytes and B cells, as fecal bacteria added directly to isolated CD8+ T cells failed to induce high cytotoxicity. Additionally, fecal bacteria from CRC patients induced stronger upregulation of CD80 and NOS2 expression in monocytes than fecal bacteria from healthy individuals. On the other hand, the viability of CD8+ T cells was significantly reduced with increasing levels of bacterial stimulation. Overall, we demonstrated that fecal bacteria from CRC patients could upregulate degranulation and cytotoxicity of CD8+ T cells in a manner that was dependent on antigen-presenting cells, and was more proinflammatory than fecal bacteria from healthy individuals.