BACKGROUND In 2013, New York began requiring hospitals to follow protocols for the early identification and treatment of sepsis. However, there is controversy about whether more rapid treatment of sepsis improves outcomes in patients. METHODS We studied data from patients with sepsis and septic shock that were reported to the New York State Department of Health from April 1, 2014, to June 30, 2016. Patients had a sepsis protocol initiated within 6 hours after arrival in the emergency department and had all items in a 3‐hour bundle of care for patients with sepsis (i.e., blood cultures, broad‐spectrum antibiotic agents, and lactate measurement) completed within 12 hours. Multilevel models were used to assess the associations between the time until completion of the 3‐hour bundle and risk‐adjusted mortality. We also examined the times to the administration of antibiotics and to the completion of an initial bolus of intravenous fluid. RESULTS Among 49,331 patients at 149 hospitals, 40,696 (82.5%) had the 3‐hour bundle completed within 3 hours. The median time to completion of the 3‐hour bundle was 1.30 hours (interquartile range, 0.65 to 2.35), the median time to the administration of antibiotics was 0.95 hours (interquartile range, 0.35 to 1.95), and the median time to completion of the fluid bolus was 2.56 hours (interquartile range, 1.33 to 4.20). Among patients who had the 3‐hour bundle completed within 12 hours, a longer time to the completion of the bundle was associated with higher risk‐adjusted in‐hospital mortality (odds ratio, 1.04 per hour; 95% confidence interval [CI], 1.02 to 1.05; P<0.001), as was a longer time to the administration of antibiotics (odds ratio, 1.04 per hour; 95% CI, 1.03 to 1.06; P<0.001) but not a longer time to the completion of a bolus of intravenous fluids (odds ratio, 1.01 per hour; 95% CI, 0.99 to 1.02; P=0.21). CONCLUSIONS More rapid completion of a 3‐hour bundle of sepsis care and rapid administration of antibiotics, but not rapid completion of an initial bolus of intravenous fluids, were associated with lower risk‐adjusted in‐hospital mortality. (Funded by the National Institutes of Health and others.)

Supplement to: Time to treatment and mortality during mandated emergency care for sepsis.

1998 국민건강·영양조사 - 계절별 영양조사

Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review.

Background:The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. Objective:This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives. Methods:We extracted papers that applied deep learning (deep neural network) models to ECG data that were published between Jan. 1st of 2010 and Feb. 29th of 2020 from Google Scholar, PubMed, and the DBLP. We then analyzed each article according to three factors: tasks, models, and data. Finally, we discuss open challenges and unsolved problems in this area. Results: The total number of papers extracted was 191. Among these papers, 108 were published after 2019. Different deep learning architectures have been used in various ECG analytics tasks, such as disease detection/classification, annotation/localization, sleep staging, biometric human identification, and denoising. Conclusion: The number of works on deep learning for ECG data has grown explosively in recent years. Such works have achieved accuracy comparable to that of traditional feature-based approaches and ensembles of multiple approaches can achieve even better results. Specifically, we found that a hybrid architecture of a convolutional neural network and recurrent neural network ensemble using expert features yields the best results. However, there are some new challenges and problems related to interpretability, scalability, and efficiency that must be addressed. Furthermore, it is also worth investigating new applications from the perspectives of datasets and methods. Significance: This paper summarizes existing deep learning research using ECG data from multiple perspectives and highlights existing challenges and problems to identify potential future research directions.

Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

Objective: Accurate QRS complex detection is essential for electrocardiography (ECG) diagnosis. Many proposed algorithms don’t perform satisfactorily on noisy and arrhythmia ECGs. The purpose of this study is to develop a noise resistant and generalizable method to detect QRS complexes accurately. Methods: Two deep learning models based on multi-dilated convolutional blocks are proposed. One model (CNN) is mainly composed of convolutional blocks and Squeeze-and-Excitation networks (SENet). The other model (CRNN) contains a hybrid convolutional and recurrent neural network. With 5-fold cross-validation approach the models are trained and tested on four open-access ECG databases: the China Physiological Signal Challenge (2019) database (CPSCDB), the MIT-BIH Noise Stress Test Database (NSTDB), the MIT-BIH Arrhythmia Database (MITDB) and the QT Database (QTDB). Results: The F1 score of CNN model on CPSCDB, NSTDB, MITDB and QTDB are 0.9929, 0.9892, 0.9994 and 0.9998 respectively. The F1 score of CRNN model on these four databases are 0.9947, 0.9953, 0.9995 and 0.9998 respectively. The ensemble of both models scored the first place in the China Physiological Signal Challenge (2019). Conclusion: The proposed models achieve state-of-the-art performance in QRS complex detection and show good generalization on different databases. This work might help make better ECG diagnosis.

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QRS Complex Detection Using Novel Deep Learning Neural Networks

1The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 2The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China 3School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore 4School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China

An Open-Access ECG Database for Algorithm Evaluation of QRS Detection and Heart Rate Estimation

Objectives Early detection of sepsis is critical in clinical practice since each hour of delayed treatment has been associated with an increase in mortality due to irreversible organ damage. This study aimed to develop an explainable artificial intelligence model for early predicting sepsis by analyzing the electronic health record data from ICU provided by the PhysioNet/Computing in Cardiology Challenge 2019. Design Retrospective observational study. Setting We developed our model on the shared ICUs publicly data and verified on the full hidden populations for challenge scoring. Patients Public database included 40,336 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (hospital system A) and Emory University Hospital (hospital system B). A total of 24,819 patients from hospital systems A, B, and C (an unidentified hospital system) were sequestered as full hidden test sets. Interventions None. Measurements and main results A total of 168 features were extracted on hourly basis. Explainable artificial intelligence sepsis predictor model was trained to predict sepsis in real time. Impact of each feature on hourly sepsis prediction was explored in-depth to show the interpretability. The algorithm demonstrated the final clinical utility score of 0.364 in this challenge when tested on the full hidden test sets, and the scores on three separate test sets were 0.430, 0.422, and -0.048, respectively. Conclusions Explainable artificial intelligence sepsis predictor model achieves superior performance for predicting sepsis risk in a real-time way and provides interpretable information for understanding sepsis risk in ICU.

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

Wearable electrocardiogram (ECG) devices can provide real-time, long-term, non-invasive and comfortable ECG monitoring for premature beats (PB) assessment (typically presenting as premature ventricular contractions (PVC) and supraventricular premature beat (SPB)), which may foreshadow\n stroke or sudden cardiac death. However, the poor quality, introduced by the dry electrode in wearable ECG monitoring system, leads to the inefficient recognition of the existing PB detection technologies. Although many methods can achieve high recognition rate on current widely-used open-access\n clinical ECG databases, they still fail to work properly on dynamic ECG signals. This study presents an open-access ECG database comprises of 24-hour wearable ECG recordings. The database is used for the 3rd China Physiological Signal Challenge (CPSC 2020), where participants are expected\n to recognize PVC and SPB from these recordings. All the approved algorithms are evaluated by scoring standards and regulations defined in terms of PVC detection and SPB detection, respectively.

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

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Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization

Objective: This paper presents a novel heart sound segmentation algorithm based on Temporal-Framing Adaptive Network (TFAN), including state transition loss and dynamic inference. Methods : In contrast to previous state-of-the-art approaches, TFAN does not require any prior knowledge of the state duration of heart sounds and is therefore likely to generalize to non sinus rhythm. TFAN was trained on 50 recordings randomly chosen from Training set A of the 2016 PhysioNet/Computer in Cardiology Challenge and tested on the other 12 independent databases (2,099 recordings and 52,180 beats). And further testing of performance was conducted on databases with three levels of increasing difficulty (LEVEL-I, -II and -III). Results: TFAN achieved a superior $F_1$ score for all 12 databases except for ‘Test-B,’ with an average of 96.72%, compared to 94.56% for logistic regression hidden semi-Markov model (LR-HSMM) and 94.18% for bidirectional gated recurrent neural network (BiGRNN). Moreover, TFAN achieved an overall $F_1$ score of 99.21%, 94.17%, 91.31% on LEVEL-I, -II and -III databases respectively, compared to 98.37%, 87.56%, 78.46% for LR-HSMM and 99.01%, 92.63%, 88.45% for BiGRNN. Conclusion: TFAN therefore provides a substantial improvement on heart sound segmentation while using less parameters compared to BiGRNN. Significance: The proposed method is highly flexible and likely to apply to other non-stationary time series. Further work is required to understand to what extent this approach will provide improved diagnostic performance, although it is logical to assume superior segmentation will lead to improved diagnostics.

Xingyao Wang

Papers

An Open-Access ECG Database for Algorithm Evaluation of QRS Detection and Heart Rate Estimation

An Explainable Artificial Intelligence Predictor for Early Detection of Sepsis.

An Open-Access Long-Term Wearable ECG Database for Premature Ventricular Contractions and Supraventricular Premature Beat Detection

Early Prediction of Sepsis Using Multi-Feature Fusion Based XGBoost Learning and Bayesian Optimization

Temporal-Framing Adaptive Network for Heart Sound Segmentation Without Prior Knowledge of State Duration