scholarly journals Sulfur Dioxide and Emergency Department Visits for Stroke and Seizure

2012 ◽  
Vol 2012 ◽  
pp. 1-7
Author(s):  
Mieczysław Szyszkowicz ◽  
Eugeniusz Porada ◽  
Neil Tremblay ◽  
Eric Grafstein
Keyword(s):  
Emergency Department ◽  
Ischemic Stroke ◽  
Sulfur Dioxide ◽  
Regression Models ◽  
Conditional Logistic Regression ◽  
Emergency Department Visits ◽  
Cerebrovascular Accidents ◽  
Logistic Regression Models ◽  
Female Patients ◽  
Ed Visits

The purpose of this study was to assess an association between ambient sulfur dioxide and the number of emergency department (ED) visits for ischemic stroke and seizure. The study used data collected in a Vancouver (Canada) hospital in the years 1999–2003. Daily ED visits diagnosed as ministroke, stroke, or seizure were investigated using the case-crossover technique. Conditional logistic regression models were applied to estimate the odds ratios (ORs) and their respective 95% confidence intervals (CIs). The models included temperature and relative humidity in the form of natural splines. The results were reported for an increase in interquartile range ((IQR),IQR=1.9ppb for SO2). Positive and statistically significant associations were obtained for SO2and ischemic stroke for all patients (OR=1.12; CI 1.02, 1.23; lag 3) and for female patients (OR=1.17; CI 1.01, 1.33; lag 0). In the case of ED visits for seizure, for female patients the results were also statistically significant (OR=1.15; CI 1.02, 1.28; lag 1 andOR=1.18; CI 1.05, 1.32; lag 2). These findings suggest that cases of ischemic cerebrovascular accidents are associated with acute exposure to ambient sulfur dioxide.

scholarly journals 616. Predicting Misdiagnoses of Infectious Disease in Emergency Department Visits

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S411-S411
Author(s):  
Alec B Chapman ◽  
Kelly Peterson ◽  
Wathsala Widanagamaachchi ◽  
Makoto M Jones
Keyword(s):  
Infectious Disease ◽  
Emergency Department ◽  
Logistic Regression ◽  
Regression Models ◽  
Emergency Department Visits ◽  
Care Process ◽  
Logistic Regression Models ◽  
Therapeutic Decisions ◽  
Ed Visits ◽  
Testing Set

Abstract Background Diagnostic error leads to delays of care and mistaken therapeutic decisions that can cascade in a downward spiral. Thus, it is important to make accurate diagnostic decisions early on in the clinical care process, such as in the emergency department (ED). Clinical data from the Electronic Health Record (EHR) could identify cases where an initial diagnosis appears unusual in context. This capability could be developed into a quality measure for feedback. To that end, we trained a multiclass machine learning classifier to predict infectious disease diagnoses following an ED visit. Methods To train and evaluate our classifier, we sampled ED visits between December 31, 2016, and December 31, 2019, from Veterans Affairs (VA) Corporate Data Warehouse (CDW). Data elements used for prediction included lab orders and results, medication orders, radiology procedures, and vital signs. A multiclass XGBoost classifier was trained to predict one of five infectious disease classes for each ED visit based on the clinical variables extracted from CDW. Our model was trained on an enriched sample of 916,562 ED visits and evaluated on a non-enriched blind testing set of 356,549 visits. We compared our model against an ensemble of univariate Logistic Regression models as a baseline. Our model was trained to predict for an ED visit one of five infectious disease classes or “No Infection”. Labels were assigned to each ED visit based on ICD-9/10-CM diagnosis codes used elsewhere and other structured EHR data associated with a patient between 24 hours prior to an ED visit and 48 hours after. Results Classifier performance varied across each of the five disease classes (Table 1). The classifier achieved the highest F1 and AUC for UTI, the lowest F1 for Sepsis, and the lowest AUC for URI. We compared the average precision, recall and F1 scores of the multiclass XGBoost with the ensemble of Logistic Regression models (Table 2). XGBoost achieved higher scores in all three metrics. Table 1. Classification performance XGBoost testing set performance in each disease class, visits with no labels, and macro average. The infectious disease classes with the highest score in each metric are shown in bold. Table 2. Baseline comparison Macro average scores for XGBoost and baseline classifiers. Conclusion We trained a model to predict infectious disease diagnoses in the Emergency Department setting. Future work will further explore this technique and combine our supervised classifier with additional signs of medical error such as increased mortality or anomalous treatment patterns in order to study medical misdiagnosis. Disclosures All Authors: No reported disclosures

scholarly journals Lagged Association of Ambient Outdoor Air Pollutants with Asthma-Related Emergency Department Visits within the Pittsburgh Region

2020 ◽  
Vol 17 (22) ◽  
pp. 8619
Author(s):  
Brandy M. Byrwa-Hill ◽  
Arvind Venkat ◽  
Albert A. Presto ◽  
Judith R. Rager ◽  
Deborah Gentile ◽  
...  
Keyword(s):  
Emergency Department ◽  
Air Pollutants ◽  
Conditional Logistic Regression ◽  
Fine Particulate Matter ◽  
Emergency Department Visits ◽  
Outdoor Air ◽  
Asthma Morbidity ◽  
Fine Particulate ◽  
Ed Visits ◽  
Lagged Effects

Asthma affects millions of people globally and is especially concerning in populations living with poor air quality. This study examines the association of ambient outdoor air pollutants on asthma-related emergency department (ED) visits in children and adults throughout the Pittsburgh region. A time-stratified case-crossover design is used to analyze the lagged effects of fine particulate matter (PM2.5) and gaseous pollutants, e.g., ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) on asthma-related ED visits (n = 6682). Single-, double-, and multi-pollutant models are adjusted for temperature and analyzed using conditional logistic regression. In children, all models show an association between O3 and increased ED visits at lag day 1 (OR: 1.12, 95% CI, 1.03–1.22, p < 0.05) for the double-pollutant model (OR: 1.10, 95% CI: 1.01-1.20, p < 0.01). In adults, the single-pollutant model shows associations between CO and increased ED visits at lag day 5 (OR: 1.13, 95% CI, 1.00–1.28, p < 0.05) and average lag days 0–5 (OR: 1.22, 95% CI: 1.00–1.49, p < 0.05), and for NO2 at lag day 5 (OR: 1.04, 95% CI: 1.00–1.07, p < 0.05). These results show an association between air pollution and asthma morbidity in the Pittsburgh region and underscore the need for mitigation efforts to improve public health outcomes.

scholarly journals Characteristics of youth who leave the emergency department without being seen following sports-related concussion

Concussion ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. CNC68
Author(s):  
Jacquelyn J Deichman ◽  
Janessa M Graves ◽  
Tracy A Klein ◽  
Jessica L Mackelprang
Keyword(s):  
Emergency Department ◽  
Regression Models ◽  
Head Injuries ◽  
Patient Characteristics ◽  
Injury Surveillance System ◽  
Logistic Regression Models ◽  
Left Without Being Seen ◽  
National Estimates ◽  
Rising Incidence ◽  
Ed Visits

Aim: Despite the rising incidence of emergency department (ED) visits for sports-related concussion, the frequency and characteristics of youth leaving before being seen are unknown. Methodology: National estimates of ED visits for sports-related head injuries among youth (10–18 years) were generated for 2006–2017 using the National Electronic Injury Surveillance System. Logistic regression models estimated the odds of leaving without being seen across patient characteristics and time. Results: From 2006 to 2017, 985,966 (95% CI: 787,296–1,184,637) ED visits were identified for sports-related concussions, of which 5015 (95% CI: 3024–7006) left without being seen. Conclusion: Youth with sports-related concussion must receive timely care and ED improvements may reduce rates of leaving without being seen.

scholarly journals Ambient Sulphur Dioxide and Female ED Visits for Migraine

2012 ◽  
Vol 2012 ◽  
pp. 1-5 ◽  
Author(s):  
Mieczyslaw Szyszkowicz ◽  
Eugeniusz Porada
Keyword(s):  
Logistic Regression ◽  
Sulphur Dioxide ◽  
Regression Models ◽  
Conditional Logistic Regression ◽  
Age Group ◽  
Logistic Regression Models ◽  
Two Cities ◽  
Ed Visits ◽  
Study Case ◽  
Positive Correlations

Ambient sulphur dioxide (SO2) concentrations may affect the number of female emergency department (ED) visits for migraine. ED visits diagnosed as migraine among females in two cities in Canada, Toronto (N=704) and Ottawa (N=3,358), were analyzed. In the study case-crossover design was used. Conditional logistic regression was realized to estimate odds ratios (ORs) and their 95% confidence intervals (CIs) relative to an increase in an interquartile range (IQR, in Toronto IQR=2.9 ppb, in Ottawa IQR=3.9 ppb) of sulphur dioxide. In the constructed conditional logistic regression models, temperature and relative humidity were adjusted in the form of natural splines. In Toronto positive and statistically significant associations of sulphur dioxide with migraine ED visits were obtained: all ages, OR=1.04 (95% CI: 1.00, 1.08); age group [15,  50], OR=1.05 (95% CI: 1.01, 1.09). In Ottawa positive correlations were observed: all ages, OR=1.05 (95% CI: 0.97, 1.13); age group [15,  50], OR=1.06 (95% CI: 0.97, 1.15). The results suggest that female migraine may be affected by ambient sulphur dioxide.

scholarly journals Emergency Department Visits During Outpatient Parenteral Antimicrobial Therapy: A Cohort Study

2017 ◽  
Vol 4 (suppl_1) ◽  
pp. S333-S334
Author(s):  
So Lim Kim ◽  
Angela Everett ◽  
Susan J Rehm ◽  
Steven Gordon ◽  
Nabin Shrestha
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Hospital Admission ◽  
Vascular Access ◽  
Antimicrobial Therapy ◽  
Cleveland Clinic ◽  
Emergency Department Visits ◽  
Outpatient Parenteral Antimicrobial Therapy ◽  
Ed Visits ◽  
Access Complications

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) carries risk of vascular access complications, antimicrobial adverse effects, and worsening of infection. Both OPAT-related and unrelated events may lead to emergency department (ED) visits. The purpose of this study was to describe adverse events that result in ED visits and risk factors associated with ED visits during OPAT. Methods OPAT courses between January 1, 2013 and December 31, 2016 at Cleveland Clinic were identified from the institution’s OPAT registry. ED visits within 30 days of OPAT initiation were reviewed. Reasons and potential risk factors for ED visits were sought in the medical record. Results Among 11,440 OPAT courses during the study period, 603 (5%) were associated with 1 or more ED visits within 30 days of OPAT initiation. Mean patient age was 58 years and 57% were males. 379 ED visits (49%) were OPAT-related; the most common visit reason was vascular access complication, which occurred in 211 (56%) of OPAT-related ED visits. The most common vascular access complications were occlusion and dislodgement, which occurred in 99 and 34 patients (47% and 16% of vascular access complications, respectively). In a multivariable logistic regression model, at least one prior ED visit in the preceding year (prior ED visit) was most strongly associated with one or more ED visits during an OPAT course (OR 2.96, 95% CI 2.38 – 3.71, p-value &lt; 0.001). Other significant factors were younger age (p 0.01), female sex (p 0.01), home county residence (P &lt; 0.001), and having a PICC (p 0.05). 549 ED visits (71%) resulted in discharge from the ED within 24 hours, 18 (2%) left against medical advice, 46 (6%) were observed up to 24 hours, and 150 ED visits (20%) led to hospital admission. Prior ED visit was not associated with hospital admission among patients who visited the ED during OPAT. Conclusion OPAT-related ED visits are most often due to vascular access complications, especially line occlusions. Patients with a prior ED visit in the preceding year have a 3-fold higher odds of at least one ED visit during OPAT compared with patients without a prior ED visit. A strategy of managing occlusions at home and a focus on patients with prior ED visits could potentially prevent a substantial proportion of OPAT-related ED visits. Disclosures All authors: No reported disclosures.

scholarly journals Association of ADH1B polymorphism and alcohol consumption with increased risk of intracerebral hemorrhagic stroke

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chun-Hsiang Lin ◽  
Oswald Ndi Nfor ◽  
Chien-Chang Ho ◽  
Shu-Yi Hsu ◽  
Disline Manli Tantoh ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Alcohol Consumption ◽  
Genetic Variants ◽  
Regression Models ◽  
Hemorrhagic Stroke ◽  
Alcohol Exposure ◽  
Modifiable Risk Factors ◽  
Aldehyde Dehydrogenase 2 ◽  
Logistic Regression Models ◽  
Increased Risk

Abstract Background Alcohol consumption is one of the modifiable risk factors for intracerebral hemorrhage, which accounts for approximately 10–20% of all strokes worldwide. We evaluated the association of stroke with genetic polymorphisms in the alcohol metabolizing genes, alcohol dehydrogenase 1B (ADH1B, rs1229984) and aldehyde dehydrogenase 2 (ALDH2, rs671) genes based on alcohol consumption. Methods Data were available for 19,500 Taiwan Biobank (TWB) participants. We used logistic regression models to test for associations between genetic variants and stroke. Overall, there were 890 individuals with ischemic stroke, 70 with hemorrhagic stroke, and 16,837 control individuals. Participants with ischemic but not hemorrhagic stroke were older than their control individuals (mean  ±  SE, 58.47 ± 8.17 vs. 48.33 ± 10.90 years, p  <  0.0001). ALDH2 rs671 was not associated with either hemorrhagic or ischemic stroke among alcohol drinkers. However, the risk of developing hemorrhagic stroke was significantly higher among ADH1B rs1229984 TC  +  CC individuals who drank alcohol (odds ratio (OR), 4.85; 95% confidence interval (CI) 1.92–12.21). We found that the test for interaction was significant for alcohol exposure and rs1229984 genotypes (p for interaction  =  0.016). Stratification by alcohol exposure and ADH1B rs1229984 genotypes showed that the risk of developing hemorrhagic stroke remained significantly higher among alcohol drinkers with TC  +  CC genotype relative to those with the TT genotype (OR, 4.43, 95% CI 1.19–16.52). Conclusions Our study suggests that the ADH1B rs1229984 TC  +  CC genotype and alcohol exposure of at least 150 ml/week may increase the risk of developing hemorrhagic stroke among Taiwanese adults.

scholarly journals Predictors of Urgent Cancer Care Clinic and Emergency Department Visits for Individuals Diagnosed with Cancer

2021 ◽  
Vol 28 (3) ◽  
pp. 1773-1789
Author(s):  
Kathleen Decker ◽  
Pascal Lambert ◽  
Katie Galloway ◽  
Oliver Bucher ◽  
Marshall Pitz ◽  
...  
Keyword(s):  
Emergency Department ◽  
Mixed Models ◽  
Cancer Care ◽  
Median Number ◽  
Emergency Department Visits ◽  
Planning Service ◽  
Care Clinic ◽  
Future Studies ◽  
Acute Complications ◽  
Ed Visits

In 2013, CancerCare Manitoba (CCMB) launched an urgent cancer care clinic (UCC) to meet the needs of individuals diagnosed with cancer experiencing acute complications of cancer or its treatment. This retrospective cohort study compared the characteristics of individuals diagnosed with cancer that visited the UCC to those who visited an emergency department (ED) and determined predictors of use. Multivariable logistic mixed models were run to predict an individual’s likelihood of visiting the UCC or an ED. Scaled Brier scores were calculated to determine how greatly each predictor impacted UCC or ED use. We found that UCC visits increased up to 4 months after eligibility to visit and then decreased. ED visits were highest immediately after eligibility and then decreased. The median number of hours between triage and discharge was 2 h for UCC visits and 9 h for ED visits. Chemotherapy had the strongest association with UCC visits, whereas ED visits prior to diagnosis had the strongest association with ED visits. Variables related to socioeconomic status were less strongly associated with UCC or ED visits. Future studies would be beneficial to planning service delivery and improving clinical outcomes and patient satisfaction.

scholarly journals Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Sean D. Young ◽  
Qingpeng Zhang ◽  
Jiandong Zhou ◽  
Rosalie Liccardo Pacula
Keyword(s):  
Emergency Department ◽  
Prescription Drug ◽  
Drug Utilization ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Treatment Preferences ◽  
Internet Search ◽  
Utilization Data ◽  
Ed Visits ◽  
Medicaid Prescription

AbstractThe primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.

scholarly journals Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lauren Alexis De Crescenzo ◽  
Barbara Alison Gabella ◽  
Jewell Johnson
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Interrupted Time Series ◽  
Emergency Department Visits ◽  
Control And Prevention ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

scholarly journals Predicting emergency department visits in a large teaching hospital

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Nathan Singh Erkamp ◽  
Dirk Hendrikus van Dalen ◽  
Esther de Vries
Keyword(s):  
Emergency Department ◽  
The Netherlands ◽  
Teaching Hospital ◽  
Time Trend ◽  
Prediction Models ◽  
Emergency Department Visits ◽  
Weather Data ◽  
Negative Consequences ◽  
Weather Variables ◽  
Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

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