scholarly journals Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis

10.2196/21562 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e21562 ◽  
Author(s):  
Joann G Elmore ◽  
Pin-Chieh Wang ◽  
Kathleen F Kerr ◽  
David L Schriger ◽  
Douglas E Morrison ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Health System ◽  
Prediction Interval ◽  
Emergency Department Visits ◽  
Current Season ◽  
Number Of Patients ◽  
Clinical Awareness

Background Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. Objective The aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. Methods A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. Results The percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. Conclusions A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

scholarly journals Excess Patient Visits for Cough and Pulmonary Disease at a Large US Health System in the Months Prior to the COVID-19 Pandemic: Time-Series Analysis (Preprint)

2020 ◽  
Author(s):  
Joann G Elmore ◽  
Pin-Chieh Wang ◽  
Kathleen F Kerr ◽  
David L Schriger ◽  
Douglas E Morrison ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Health System ◽  
Prediction Interval ◽  
Emergency Department Visits ◽  
Current Season ◽  
Number Of Patients ◽  
Clinical Awareness

BACKGROUND Accurately assessing the regional activity of diseases such as COVID-19 is important in guiding public health interventions. Leveraging electronic health records (EHRs) to monitor outpatient clinical encounters may lead to the identification of emerging outbreaks. OBJECTIVE The aim of this study is to investigate whether excess visits where the word “cough” was present in the EHR reason for visit, and hospitalizations with acute respiratory failure were more frequent from December 2019 to February 2020 compared with the preceding 5 years. METHODS A retrospective observational cohort was identified from a large US health system with 3 hospitals, over 180 clinics, and 2.5 million patient encounters annually. Data from patient encounters from July 1, 2014, to February 29, 2020, were included. Seasonal autoregressive integrated moving average (SARIMA) time-series models were used to evaluate if the observed winter 2019/2020 rates were higher than the forecast 95% prediction intervals. The estimated excess number of visits and hospitalizations in winter 2019/2020 were calculated compared to previous seasons. RESULTS The percentage of patients presenting with an EHR reason for visit containing the word “cough” to clinics exceeded the 95% prediction interval the week of December 22, 2019, and was consistently above the 95% prediction interval all 10 weeks through the end of February 2020. Similar trends were noted for emergency department visits and hospitalizations starting December 22, 2019, where observed data exceeded the 95% prediction interval in 6 and 7 of the 10 weeks, respectively. The estimated excess over the 3-month 2019/2020 winter season, obtained by either subtracting the maximum or subtracting the average of the five previous seasons from the current season, was 1.6 or 2.0 excess visits for cough per 1000 outpatient visits, 11.0 or 19.2 excess visits for cough per 1000 emergency department visits, and 21.4 or 39.1 excess visits per 1000 hospitalizations with acute respiratory failure, respectively. The total numbers of excess cases above the 95% predicted forecast interval were 168 cases in the outpatient clinics, 56 cases for the emergency department, and 18 hospitalized with acute respiratory failure. CONCLUSIONS A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities. This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.

scholarly journals In-hospital mortality in the emergency department: clinical and etiological differences between early and late deaths among patients awaiting admission

2021 ◽  
Vol 8 (4) ◽  
pp. 325-332
Author(s):  
Gabriele Valli ◽  
Elisabetta Galati ◽  
Francesca De Marco ◽  
Chiara Bucci ◽  
Paolo Fratini ◽  
...  
Keyword(s):  
Emergency Department ◽  
Respiratory Failure ◽  
Acute Respiratory Failure ◽  
Causes Of Death ◽  
Vital Signs ◽  
Medical Visit ◽  
The Past ◽  
Number Of Patients ◽  
Critical Patients ◽  
Positive Correlations

Objective Given that there are no studies on diseases that occur by waiting for hospitalization, we aimed to evaluate the main causes of death in the emergency room (ER) and their relationship with overcrowding.Methods Patients who died in the ER in the past 2 years (pediatrics and trauma victims excluded) were divided into two groups: patients who died within 6 hours of arrival (emergency department [ED] group) and patients who died later (LD group). We compared the causes of death, total vital signs, diagnostic tests performed, and therapy between the groups. We assessed for possible correlation between the number of monthly deaths per group and four variables of overcrowding: number of patients treated per month, waiting time before medical visit (W-Time), mean intervention time (I-Time), and number of patients admitted to the ward per month (NPA).Results During the two years, 175 patients had died in our ER (52% in ED group and 48% in LD group). The total time spent in the ER was, respectively, 2.9±0.2 hours for ED group and 17.9± 1.5 hours for LD group. The more frequent cause of death was cardiovascular syndrome (30%) in ED group and sepsis (27%) and acute respiratory failure (27%) in LD group. Positive correlations between number of monthly deaths and W-Time (R2 0.51, P<0.001), I-Time (R2 0.73, P< 0.0001), and NPA (R2 0.37, P<0.01) were found only in LD group.Conclusion Patients with sepsis and acute respiratory failure die after a long stay in the ER, and the risk increases with overcrowding. A fast-track pathway should be considered for hospital admission of critical patients.

scholarly journals Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shan Jiang ◽  
Joshua L. Warren ◽  
Noah Scovronick ◽  
Shannon E. Moss ◽  
Lyndsey A. Darrow ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Health Outcomes ◽  
Simulation Study ◽  
Heat Exposure ◽  
Emergency Department Visits ◽  
Extreme Heat ◽  
Logic Regression ◽  
Adverse Health ◽  
Adverse Health Outcomes

Abstract Background Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations. Methods Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area. Results For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient. Conclusion Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

scholarly journals Forecasting emergency department presentations

2007 ◽  
Vol 31 (1) ◽  
pp. 83 ◽  
Author(s):  
Robert Champion ◽  
Leigh D Kinsman ◽  
Geraldine A Lee ◽  
Kevin A Masman ◽  
Elizabeth A May ◽  
...  
Keyword(s):  
Emergency Department ◽  
Time Series ◽  
Time Series Analysis ◽  
Software Package ◽  
Exponential Smoothing ◽  
Statistical Forecasting ◽  
Forecasting Methods ◽  
Number Of Patients ◽  
Forecasting Performance ◽  
Optimal Forecasting

Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.

scholarly journals The Use of Dexmedetomidine in the Emergency Department: A Cohort Study

2021 ◽  
Vol 22 (5) ◽  
pp. 1202-1209
Author(s):  
Joseph Sinnott ◽  
Christopher Holthaus ◽  
Enyo Ablordeppey ◽  
Brian Wessman ◽  
Brian Roberts ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Event ◽  
Cohort Study ◽  
Respiratory Failure ◽  
Adverse Events ◽  
Acute Respiratory Failure ◽  
Tertiary Care ◽  
Multivariable Logistic Regression Model ◽  
Non Invasive Ventilation ◽  
Improved Outcomes

Introduction: Management of sedation, analgesia, and anxiolysis are cornerstone therapies in the emergency department (ED). Dexmedetomidine (DEX), a central alpha-2 agonist, is increasingly being used, and intensive care unit (ICU) data demonstrate improved outcomes in patients with respiratory failure. However, there is a lack of ED-based data. We therefore sought to: 1) characterize ED DEX use; 2) describe the incidence of adverse events; and 3) explore factors associated with adverse events among patients receiving DEX in the ED. Methods: This was a single-center, retrospective, cohort study of consecutive ED patients administered DEX (January 1, 2017–July 1, 2019) at an academic, tertiary care ED with an annual census of ~90,000 patient visits. All included patients (n= 103) were analyzed for characterization of DEX use in the ED. The primary outcome was a composite of adverse events, bradycardia and hypotension. Secondary clinical outcomes included ventilator-, ICU-, and hospital-free days, and hospital mortality. To examine for variables associated with adverse events, we used a multivariable logistic regression model. Results: We report on 103 patients. Dexmedetomidine was most commonly given for acute respiratory failure, including sedation for mechanical ventilation (28.9%) and facilitation of non-invasive ventilation (17.4%). Fifty-four (52.4%) patients experienced the composite adverse event, with hypotension occurring in 41 patients (39.8%) and bradycardia occurring in 18 patients (17.5%). Dexmedetomidine was stopped secondary to an adverse event in eight patients (7.8%). Duration of DEX use in the ED was associated with an increase adverse event risk (adjusted odds ratio, 1.004; 95% confidence interval, 1.001, 1.008). Conclusion: Dexmedetomidine is most commonly administered in the ED for patients with acute respiratory failure. Adverse events are relatively common, yet DEX is discontinued comparatively infrequently due to adverse events. Our results suggest that DEX could be a viable option for analgesia, anxiolysis, and sedation in ED patients.

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