Dennis G. Cochrane

Cardiac events in New Jersey after the September 11, 2001, terrorist attack

The higher stress associated with the World Trade Center (WTC) attacks on September 11, 2001, may have resulted in more cardiac events particularly in those living in close proximity. Our goal was to determine if there was an increase in cardiac events in a subset of emergency departments (EDs) within a 50-mi radius of the WTC. We performed a retrospective analysis of consecutive patients seen by ED physicians in 16 EDs for the 60 days before and after September 11 in 2000–2002. We determined the number of patients admitted to an inpatient bed with a primary or secondary diagnosis of acute myocardial infarction (MI) or tachyarrhythmia. In each year, we compared patient visits for the 60 days before and after September 11 using the chi-square statistic. For the 360 days during the 3 years, there were 571,079 patient visits in the database of which 110,766 (19.4%) were admitted. Comparing the 60 days before and after September 11, 2001, we found a statistically significant increase in patients with MIs (79 patients before versus 118 patients after, P=.01), representing an increase of 49%. There were no statistically significant differences for MIs in 2000 and 2002 and in tachyarrhythmias for all three years. For the 60-day period after September 11, 2001, we found a statistically significant increase in the number of patients presenting with acute MI but no increase in patients admitted with tachyarrhythmias.

Delphi Consensus on the Feasibility of Translating the ACEP Clinical Policies Into Computerized Clinical Decision Support

Clinical practice guidelines are developed to reduce variations in clinical practice, with the goal of improving health care quality and cost. However, evidence-based practice guidelines face barriers to dissemination, implementation, usability, integration into practice, and use. The American College of Emergency Physicians (ACEP) clinical policies have been shown to be safe and effective and are even cited by other specialties. In spite of the benefits of the ACEP clinical policies, implementation of these clinical practice guidelines into physician practice continues to be a challenge. Translation of the ACEP clinical policies into real-time computerized clinical decision support systems could help address these barriers and improve clinician decision making at the point of care. The investigators convened an emergency medicine informatics expert panel and used a Delphi consensus process to assess the feasibility of translating the current ACEP clinical policies into clinical decision support content. This resulting consensus document will serve to identify limitations to implementation of the existing ACEP Clinical Policies so that future clinical practice guideline development will consider implementation into clinical decision support at all stages of guideline development.

Increases in Heart Failure Visits After Christmas and New Year's Day

The authors hypothesized increased emergency department (ED) visits for heart failure (HF) during a 2-week Christmas holiday period similar to a recent study showing increased cardiac death rates. A retrospective analysis was performed from a database of 18 EDs in New Jersey and New York from January 1, 1996, to November 30, 2004, analyzing HF visits from December 1 to January 31. The authors compared the mean daily visits for the 2-week holiday period of December 25 to January 7, as well as December 26 to December 30 and January 2 to January 5, using the Student t test. A total of 4.7 million patients were studied, 65,646 with an ED diagnosis of HF and 11,525 during January and December. There was a 23% (95% confidence interval [CI], 14%-31%; P<.001) increase in daily visits for December 25 to January 7 and a 33% (95% CI, 16%-51%; P=.007) and 30% (95% CI, 22%-38%; P<.01) increase in the 4 days following Christmas and New Years, respectively. The authors found a significant increase in daily HF visits in the 2-week holiday period and the 4 days following the holidays, even greater than that reported for sudden cardiac death.

Real-time biosurveillance using an existing emergency department electronic medical record database

S i121 Jersey EDs, with participants consecutive patients seen by ED physicians January 1998 through July 2002. Based on ICD-9 codes, syndromic groups were developed for the following categories: any gastrointestinal, diarrhea, respiratory, asthma, chest pain, fever, skin, headache, and weakness. We then generated daily counts of patients by category and generated time series graphs to display the incidence of disease for these syndromic groups over the 4.5-year period. We also generated similar counts and graphs for the same syndromic groups based on the physician’s choice of charting template rather than ICD-9 code and compared the results. There were 3.2 million patient visits in the database. Visual inspection of the time series graphs showed definite seasonal peaks in the diarrhea, respiratory, asthma, fever, and skin syndromic groups. There was good agreement between ICD-9 codes and templates. The existing ED database identified seasonal peaks in the incidence of several disease syndromes. Tracking physician charting template usage could potentially identify these patterns in real time. This ED database may be able to provide early warning of disease outbreaks and some types of bioterrorist attacks. The Use of Hospital Emergency Department Chief Complaint Data as a “Near” Real-Time Marker for Assessing Public Health Risk of Infectious Disease Outbreak Ronald J. Shannon, Michael Davisson, Trang Nguyen, Carolyn Stetson, and Kevin Jones New York State Department of Health, Division of Epidemiology Since September 11, 2001, and the corresponding anthrax attacks, there has been considerable interest in developing pre-event surveillance methods that would be used for early detection of a bioterrorist event and prevention of widespread morbidity and mortality. Traditionally, active surveillance mechanisms to detect disease outbreaks consist of confirmatory laboratory testing after preliminary diagnosis from a physician. In many cases, the confirmation of infectious disease takes days of testing, many hours of epidemiological analysis, and significant public health resources at the local level before an outbreak is finally diagnosed. Our communities are at significant risk unless public health authorities can develop a preevent early warning system with a high degree of specificity and sensitivity for outbreak detection when patients present themselves to the emergency department or their primary health care provider. A pre-event early warning system depends on quality, “near” real-time data from the medical community. Potential sources of this information are (1) hospital emergency department encounters, (2) outpatient clinic visits, (3) pharmacy data (over the counter and prescription). All data sources have varying degrees of quality, but the hospital emergency department registration information (chief complaint at initial visit) is determined to provide the nearest real-time means for use in a pre-event surveillance system. But, is chief complaint information as reliable as International Classification of Diseases, 9th Revision (ICD-9)–coded discharge diagnosis in predicting an early event? ICD-9 diagnosis is considered to be the best indicator of patient diagnosis, but is not readily available for epidemiological analysis until 3 to 5 days after initial visit. The New York State Department of Health and Emergency Medical Associates of New Jersey Research Foundation (EMARF) have completed a study comparing emergency department chief complaint data with ICD-9 discharge codes from 2.7 million patient encounters presenting to 15 emergency departments in New Jersey to determine the feasibility of using chief complaint for pre-event surveillance. Preliminary findings show a high specificity and sensitivity comparing chief complaint data to ICD9-coded discharge diagnosis. Connecticut Hospital Admissions Syndromic Surveillance Zygmunt Dembek, Myrth Myers, Kenneth Carley, and James Hadler Connecticut Department of Public Health

Circadian pattern of intubation rates in ED patients with congestive heart failure

PURPOSE A previous study showed that pulmonary edema patients presenting between noon and 4 pm have the highest rates of myocardial infarction and death. We hypothesized that the highest intubation rates would also occur at these times. BASIC PROCEDURES We performed a retrospective cohort study of consecutive patients seen by emergency department physicians in 15 hospital emergency departments (1996-2003). MAIN FINDINGS Of 3.6 million visits in the database, 39,795 (1.1%) patients had congestive heart failure. We found statistically significant circadian variations in intubation rates. Patients arriving between midnight and 4 am had the highest intubation rates (4.1%), and those arriving between noon and 4 pm had the lowest (1.2%) (difference, 2.9%; 95% confidence interval, 2.4%-3.4%; P < .0001). CONCLUSION We found significant circadian variation in intubation rates, with a marked increase around midnight. Pathological mechanisms causing patients with congestive heart failure to require intubation may differ from those resulting in myocardial infarction or death.

Emergency Department Visits for Gastroenteritis Before and After Rotavirus Vaccine Implementation in 2006.

Objectives Gastroenteritis (GE) accounts for a significant number of emergency department (ED) visits in children. Several studies since the introduction of a new rotavirus vaccine in 2006 have found decreases in rotavirus illness. We sought to determine in a large multicenter ED database whether there was also a decrease in ED visits in young children for GE. Methods Design: Retrospective cohort of ED visits. Setting: 28 EDs with annual visits between 22,000 and 82,000. Population: Consecutive patients between January 1, 1996, and December 31, 2011. Protocol: We identified GE visits using International Classification of Diseases 9th revision (ICD-9) codes. For each year, less than 5 years, we determined the average daily percent of total ED visits for GE. We calculated the decreases from 2005 to 2011 in the average daily percent GE visits for each year of life and their 95% confidence intervals. Results There were 7,740,823 total visits in the database, and 811,317 (10.5%) are younger than 5 years. The annual percent of GE visits rose for all years of life from 1999 to 2005 and then decreased from 2005 to 2011. The decreases from 2005 to 2011 were greatest in the earliest years of life ranging from 41% in the first year of life to 15% in the fifth year of life. Conclusions We found a decrease in average daily ED visits for GE in each year of life for those younger than 5 years after the introduction of the rotavirus vaccine. This was most pronounced during the earliest years of life.

Using n-Grams for Syndromic Surveillance in a Turkish Emergency Department Without English Translation: A Feasibility Study

Introduction: Syndromic surveillance is designed for early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which are generally recorded in the local language. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories. The n-gram classifier is created by using text fragments to measure associations between chief complaints (CC) and a syndromic grouping of ICD codes. Objectives: The objective was to create a Turkish n-gram CC classifier for the respiratory syndrome and then compare daily volumes between the n-gram CC classifier and a respiratory ICD-10 code grouping on a test set of data. Methods: The design was a feasibility study based on retrospective cohort data. The setting was a university hospital emergency department (ED) in Turkey. Included were all ED visits in the 2002 database of this hospital. Two of the authors created a respiratory grouping of International Classification of Diseases, 10th Revision ICD-10-CM codes by consensus, chosen to be similar to a standard respiratory (RESP) grouping of ICD codes created by the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE), a project of the Centers for Disease Control and Prevention. An n-gram method adapted from AT&T Labs’ technologies was applied to the first 10 months of data as a training set to create a Turkish CC RESP classifier. The classifier was then tested on the subsequent 2 months of visits to generate a time series graph and determine the correlation with daily volumes measured by the CC classifier versus the RESP ICD-10 grouping. Results: The Turkish ED database contained 30,157 visits. The correlation (R2) of n-gram versus ICD-10 for the test set was 0.78. Conclusion: The n-gram method automatically created a CC RESP classifier of the Turkish CCs that performed similarly to the ICD-10 RESP grouping. The n-gram technique has the advantage of systematic, consistent, and rapid deployment as well as language independence.

Detecting the start of the flu season

We have combined two methods to detect anomalies in a time series - in this case in emergency department visit data. The n-gram method applies an existing ICD classifier to a set of emergency department (ED) visits for which both the chief complaint (CC) and ICD code are known. A collection of CC substrings (or n-grams), with associated probabilities, are automatically generated from the training data. This information becomes a CC classifier which is then used to find a classification probability for each patient. The output of this classifier can be used to build volume predictions for a syndromic group or can be combined with a selected threshold to provide syndromic determinations on a per-patient basis. Once the daily volume predictions have been calculated using the n-grams, the HWR anomaly detection algorithm is applied, which alerts both for unusual values and for changes in the overall behavior of the time series in question. The earliest alert was generated by the series of volume predicted by flu n-grams as a proportion of total daily visits.