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Study objective We use a national emergency medicine clinical quality registry to describe recent trends in emergency department (ED) visitation overall and for select emergency conditions. Methods Data were drawn from the Clinical Emergency Department Registry, including 164 ED sites across 35 states participating in the registry with complete data from January 2019 through November 15, 2020. Overall ED visit counts, as well as specific emergency medical conditions identified by International Classification of Diseases, Tenth Revision, Clinical Modification code (myocardial infarction, cerebrovascular accident, cardiac arrest/ventricular fibrillation, and venous thromboembolisms), were tabulated. We plotted biweekly visit counts overall and across specific geographic regions. Results The largest declines in visit counts occurred early in the pandemic, with a nadir in April 46% lower than the 2019 monthly average. By November, overall ED visit counts had increased, but were 23% lower than prepandemic levels. The proportion of all ED visits that were for the select emergency conditions increased early in the pandemic; however, total visit counts for acute myocardial infarction and cerebrovascular disease have remained lower in 2020 compared with 2019. Despite considerable geographic and temporal variation in the trajectory of the coronavirus disease 2019 outbreak, the overall pattern of ED visits observed was similar across regions and time. Conclusion The persistent decline in ED visits for these time-sensitive emergency conditions raises the concern that coronavirus disease 2019 may continue to impede patients from seeking essential care. Efforts thus far to encourage individuals with concerning signs and symptoms to seek emergency care may not have been sufficient.

Additional file 1: Figure A1. Monthly cohort definition for AMI, HF or Pneumonia Readmissions and Observation Stays (Pg.2). Figure A2. Hierarchy for multiple post-discharge care events (Pg. 2). Table A1. Cumulative numbers and percentages of final action inpatient claims uploaded to the IDR for all conditions with a discharge date in January 2013 and July 2013 (as of December 2014) (Pg.3). Figure A3: Modeling approach (Pg.3). Figure A4. Timing of calculating monthly outcomes (Pg.4). Table A2. Final model specification for prediction of number of admissions, readmission rate, and observation-stay rate in AMI cohort (Pg.4). Table A3: Specifications of the final real-time reporting models for Heart failure and pneumonia (Pg.6). Table A4: Results of look-back validation where we compare the rates estimated (for the months July 2016 through December 2016) in February 2017 using RTR models with the final rates later observed using data downloaded from the IDR in February 2018 for AMI, HF and Pneumonia (Pg.7). Figure A5. Prediction and look-back validation for heart failure admission, readmission and observation stays (July 2016–December 2016) (Pg.8). Figure A6. Prediction and look-back validation for pneumonia admission, readmission and observation stays (July 2016–December 2016 (Pg.9).

The determination of a coronary stenosis and its severity in current clinical workflow is typically accomplished manually via physician visual assessment (PVA) during invasive coronary angiography. While PVA has shown large inter-rater variability, the more reliable and accurate alternative of Quantitative Coronary Angiography (QCA) is challenging to perform in real-time due to the busy workflow in cardiac catheterization laboratories. We propose a deep learning approach based on Convolutional Neural Networks (CNN) that automatically characterizes and analyzes coronary stenoses in real-time by automating clinical tasks performed during QCA. Our deep learning methods for localization, segmentation and classification of stenosis in still-frame invasive coronary angiography (ICA) images of the right coronary artery (RCA) achieve performance of 72.7% localization accuracy, 0.704 dice coefficient and 0.825 C-statistic in each respective task. Integrated in an end-to-end approach, our model's performance shows statistically significant improvement in false discovery rate over the current standard in real-time clinical stenosis assessment, PVA. To the best of the authors' knowledge, this is the first time an automated machine learning system has been developed that can implement tasks performed in QCA, and the first time an automated machine learning system has demonstrated significant improvement over the current clinical standard for rapid RCA stenosis analysis.

Artificial bee colony (ABC) algorithm is a kind of artificial colony algorithm simulating the behavior of bees swarm intelligence optimization algorithm. Because it has less control parameters, easy to implement, the advantages of simple calculation, has been more and more researchers focus on. But the basic artificial colony algorithm still has the shortcomings, such as premature convergence, easy to fall into local optimum and evolution, such as the late slower convergence shortcomings. In view of the ABC algorithm search equation of the shortcoming of weak local searching ability of the variation equation by differential evolution algorithm and evolution equation of particle swarm optimization (PSO) algorithm, puts forward two kinds of artificial colony algorithm based on improved search equation, respectively is the artificial colony algorithm based on the optimal solution to guide and mixed artificial colony algorithm, experimental simulation show that the improved algorithm in the optimal solution and precision of algorithm convergence ability has a certain degree of improvement or enhancement.

BackgroundPrior claims analyses suggest that the use of intravenous inotropic therapy for patients hospitalized with heart failure varies substantially by hospital. Whether differences in the clinical characteristics of the patients explain observed differences in the use of inotropic therapy is not known.Methods and resultsWe sought to characterize institutional variation in inotrope use among patients hospitalized with heart failure before and after accounting for clinical factors of patients. Hierarchical generalized linear regression models estimated risk-standardized hospital-level rates of inotrope use within 209 hospitals participating in Get With The Guidelines-Heart Failure (GWTG-HF) registry between 2005 and 2011. The association between risk-standardized rates of inotrope use and clinical outcomes was determined. Overall, an inotropic agent was administered in 7691 of 126 564 (6.1%) heart failure hospitalizations: dobutamine 43%, dopamine 24%, milrinone 17%, or a combination 16%. Patterns of inotrope use were stable during the 7-year study period. Use of inotropes varied significantly between hospitals even after accounting for patient and hospital characteristics (median risk-standardized hospital rate, 5.9%; interquartile range, 3.7%-8.6%; range, 1.3%-32.9%). After adjusting for case-mix and hospital structural differences, model intraclass correlation indicated that 21% of the observed variation in inotrope use was potentially attributable to random hospital effects (ie, institutional preferences). Hospitals with higher risk-standardized inotrope use had modestly longer risk-standardized length of stay (P=0.005) but had no difference in risk-standardized inpatient mortality (P=0.12).ConclusionsUse of intravenous inotropic agents during hospitalization for heart failure varies significantly among US hospitals even after accounting for patient and hospital factors.

Assembly sequence planning plays an important role in the product development process. It is an important factor that determines quality and cost of the product assembly. Cost in assembly can be reduced by the implementation of generating automatic product assembly sequences, and selecting the optimum sequence in product assembly process. Assembly sequence planning (ASP) is combinatorial problem. In recent years, some soft computing & intelligent algorithms have been used to solve ASP problems, and some achievements are arrived at. However, there are limitations for ASP. GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non-optimization of final result for global variable. For simulated annealing algorithms, the principle of generating new sequence is exchanging position of the randomly selected two parts. Obviously, for complex products, a number of non-feasible solutions may appear, and the efficiency is low. In view of these limitations, the approaches of genetic simulated annealing algorithm (GSAA), ant colony optimization (ACO) algorithm and so on are used for the optimization of ASP. In this paper, the following contents about these two algorithms and the comparison are included. Firstly, the relevant researches on assembly sequence planning and the application of GA and SA are summarized. Next, the idea of two algorithms into genetic simulated annealing algorithm and ant colony optimization algorithm are put forward individually. Thirdly, a case study is presented to validate the proposed two methods. The advantages and disadvantages are presented. At last, the work of this paper is summarized and the future works are given.

Assembly sequence planning (ASP) is a crucial design step in the product development process which plays an important role in the fields of CAD/CAM design issues, the cost of assembly/manufacturing, as well as the selection of equipment. Whereas, ASP is an extremely diverse, large scale and highly constrained combinatorial problem, and it is difficult to find an optimal/near-optimal solution in an acceptable time. Numerous researchers have employed soft computing methods such as genetic algorithms (GA) and simulated annealing (SA) algorithms to go towards the assembly sequence features of speed and flexibility. As regards the large constraint assembly problems, however, traditional GAs depend on the initial sequence heavily, which results in the premature convergence in iterative operations. As for SA algorithms, it may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution-searching process. Considering the limitations above, two heuristics algorithms for ASP are presented. The proposed novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for ASP is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity. Furthermore, the GSAA and ACO are applied in a vice ASP, the results of which are compared in respect of the quality of solution and the efficiency of searching process. At last, the advantages and disadvantages of GSAA and ACO are pointed out by comparison, which gives some useful hint on future research.

Appendix A. List of ICD-9 and Standard Charge Codes for Identifying Percutaneous Coronary Intervention and Coronary Artery Bypass Graft Procedures. Appendix B. Standard Charge Codes for Identifying ICU/CCU and Catheterization. Table S1. Characteristics of Patients Hospitalized with Acute Myocardial Infarction by Hospital Risk-Standardized In-Hospital Cost Levels. Table S2. In-Hospital Resource Utilization: Mean Per-Patient Cost in Each Department. Table S3. In-Hospital Resource Utilization: Mean per Patient Number of Items. Table S4. Patient Costs by In-Hospital Cost Ranks and Vital Status. Table S5. Mean Per Patient Post-Acute-Care Payment at Different Care Settings. Table S6. Percentage of Patients Using Various Post-Acute-Care Services by Cost Groups. Figure S1. Flowchart of Exclusion Criteria. Figure S2. Number of Items Ordered during a Hospitalization by In-Hospital Cost Tertile. Figure S3. Mean In-hospital Resource Utilization per User by In-Hospital Cost Tertiles. Figure S4. Relative Contribution of Service Categories to In-Hospital Costs by In-Hospital Cost Tertiles. Figure S5. Correlation Between Hospital Risk-Standardized In-Hospital Cost ($) and Hospital Risk-Standardized Post-Acute Payments ($). Figure S6.a In-Hospital Spending Among Survivors. b In-Hospital Spending Among the Expired. Figure S7. Percentage of Patients Using Different Post-Acute-Care Services by In-Hospital Cost Tertiles. Figure S8. Mean User Post-Acute Payments by In-hospital Cost Tertiles. Figure S9. Relative Contribution of Service Categories to Post-Acute Payments by In-Hospital Cost Tertiles. (DOCX 178 kb)

Background: Heart failure as recognized and treated in typical practice may represent a complex condition that defies discrete categorizations. To illuminate this complexity, we examined treatment strategies for patients hospitalized and treated for decompensated heart failure. We focused on the receipt of medications appropriate for other acute conditions associated with shortness of breath including acute asthma, pneumonia, and exacerbated chronic obstructive pulmonary disease. Methods and Results: Using Premier Perspective®, we studied adults hospitalized with a principal discharge diagnosis of heart failure and evidence of acute heart failure treatment from 2009-2010 at 370 US hospitals. We determined treatment with acute respiratory therapies during the initial 2 days of hospitalization and daily during hospital days 3-5. We also calculated adjusted odds of in-hospital death, admission to the intensive care unit, and late intubation (intubation after hospital day 2). Among 164,494 heart failure hospitalizations, 53% received acute respiratory therapies during the first 2 hospital days: 37% received short-acting inhaled bronchodilators, 33% received antibiotics, and 10% received high-dose corticosteroids. Of these 87,319 hospitalizations, over 60% continued receiving respiratory therapies after hospital day 2. Respiratory treatment was more frequent among the 60,690 hospitalizations with chronic lung disease. Treatment with acute respiratory therapy during the first 2 hospital days was associated with higher adjusted odds of all adverse outcomes. Conclusions: Acute respiratory therapy is administered to more than half of patients hospitalized with and treated for decompensated heart failure. Heart failure is therefore regularly treated as a broader cardiopulmonary syndrome rather than as a singular cardiac condition.

Appendix A. List of ICD-9 and Standard Charge Codes for Identifying Percutaneous Coronary Intervention and Coronary Artery Bypass Graft Procedures. Appendix B. Standard Charge Codes for Identifying ICU/CCU and Catheterization. Table S1. Characteristics of Patients Hospitalized with Acute Myocardial Infarction by Hospital Risk-Standardized In-Hospital Cost Levels. Table S2. In-Hospital Resource Utilization: Mean Per-Patient Cost in Each Department. Table S3. In-Hospital Resource Utilization: Mean per Patient Number of Items. Table S4. Patient Costs by In-Hospital Cost Ranks and Vital Status. Table S5. Mean Per Patient Post-Acute-Care Payment at Different Care Settings. Table S6. Percentage of Patients Using Various Post-Acute-Care Services by Cost Groups. Figure S1. Flowchart of Exclusion Criteria. Figure S2. Number of Items Ordered during a Hospitalization by In-Hospital Cost Tertile. Figure S3. Mean In-hospital Resource Utilization per User by In-Hospital Cost Tertiles. Figure S4. Relative Contribution of Service Categories to In-Hospital Costs by In-Hospital Cost Tertiles. Figure S5. Correlation Between Hospital Risk-Standardized In-Hospital Cost ($) and Hospital Risk-Standardized Post-Acute Payments ($). Figure S6.a In-Hospital Spending Among Survivors. b In-Hospital Spending Among the Expired. Figure S7. Percentage of Patients Using Different Post-Acute-Care Services by In-Hospital Cost Tertiles. Figure S8. Mean User Post-Acute Payments by In-hospital Cost Tertiles. Figure S9. Relative Contribution of Service Categories to Post-Acute Payments by In-Hospital Cost Tertiles. (DOCX 178 kb)