Predicting out of intensive care unit cardiopulmonary arrest or death using electronic medical record data.

BACKGROUND: Accurate, timely and automated identification of patients at high risk for severe clinical deterioration using readily available clinical information in the electronic medical record (EMR) could inform health systems to target scarce resources and save lives. METHODS: We identified 7,466 patients admitted to a large, public, urban academic hospital between May 2009 and March 2010. An automated clinical prediction model for out of intensive care unit (ICU) cardiopulmonary arrest and unexpected death was created in the derivation sample (50% randomly selected from total cohort) using multivariable logistic regression. The automated model was then validated in the remaining 50% from the total cohort (validation sample). The primary outcome was a composite of resuscitation events, and death (RED). RED included cardiopulmonary arrest, acute respiratory compromise and unexpected death. Predictors were measured using data from the previous 24 hours. Candidate variables included vital signs, laboratory data, physician orders, medications, floor assignment, and the Modified Early Warning Score (MEWS), among other treatment variables. RESULTS: RED rates were 1.2% of patient-days for the total cohort. Fourteen variables were independent predictors of RED and included age, oxygenation, diastolic blood pressure, arterial blood gas and laboratory values, emergent orders, and assignment to a high risk floor. The automated model had excellent discrimination (c-statistic=0.85) and calibration and was more sensitive (51.6% and 42.2%) and specific (94.3% and 91.3%) than the MEWS alone. The automated model predicted RED 15.9 hours before they occurred and earlier than Rapid Response Team (RRT) activation (5.7 hours prior to an event, p=0.003) CONCLUSION: An automated model harnessing EMR data offers great potential for identifying RED and was superior to both a prior risk model and the human judgment-driven RRT.

Ethnic disparities in asthma morbidity in Chicago.

RATIONALE: The role of ethnicity and socioeconomic status in explaining variations in asthma morbidity is unclear. OBJECTIVES: To describe the magnitude of ethnic disparities in asthma morbidity in Chicago and to determine whether differences in socioeconomic status explain these disparities. METHODS: We conducted a survey of 561 school-age children and 353 young adults with asthma and measured their self-reported ethnicity, socioeconomic status (using 11 variables), and asthma morbidity (symptom frequency, asthma-specific quality of life, and frequency of severe asthma exacerbations). MEASUREMENTS AND MAIN RESULTS: White children and adults had better asthma-specific quality of life and fewer severe asthma exacerbations compared to black and Hispanic children and adults. White children also had fewer days with asthma symptoms, but among adults there were no ethnic differences in the frequency of asthma symptoms. Socioeconomic status explained a large portion of the ethnic disparities in asthma quality of life but explained little of the disparities in other aspects of asthma morbidity. CONCLUSIONS: There are large disparities across ethnic groups in Chicago in asthma quality of life and in the frequency of severe exacerbations. Differences in socioeconomic status do not fully explain these disparities.

The protective effect of community factors on childhood asthma.

BACKGROUND: Asthma burden in the US is not evenly distributed. Although asthma prevalence varies widely across urban neighborhoods, little attention has been paid to the community as a key contributor. OBJECTIVE: To determine the effect of positive socio-environmental community factors on childhood asthma prevalence in Chicago. METHODS: From 2003 to 2005, an asthma screening survey was conducted among children attending Chicago Public/Catholic schools from kindergarten through eighth grade. One hundred five schools participated, yielding a stratified representation of 4 race-income groups. Positive community factors, such as social capital, economic potential, and community amenities, were assessed by using the Metro Chicago Information Center's Community Vitality Index. RESULTS: Of the surveys returned, 45,177 (92%) were geocoded into 287 neighborhoods. Neighborhoods were divided into quartile groups by asthma prevalence (mean, 8%, 12%, 17%, 25%). Community vitality (54% vs 44%; P < .0001) and economic potential (64% vs 38%; P < .0001) were significantly higher in neighborhoods with low asthma prevalence. Neighborhood interaction (36% vs 73%; P < .0001) and stability (40% vs 53%; P < .0001) were significantly higher in neighborhoods with high asthma prevalence. Overall, positive factors explained 21% of asthma variation. Childhood asthma increased as the black population increased in a community (P < .0001). Accordingly, race/ethnicity was controlled. In black neighborhoods, these factors remained significantly higher in neighborhoods with low asthma prevalence. When considered alongside socio-demographic/individual characteristics, overall community vitality as well as social capital continued to contribute significantly to asthma variation. CONCLUSION: Asthma prevalence in Chicago is strongly associated with socio-environmental factors thought to enrich a community. A deeper understanding of this impact may lend insight into interventions to reduce childhood asthma.

Asthma burden in a citywide, diverse sample of elementary schoolchildren in Chicago.

OBJECTIVE: The purposes of this study are to describe and develop preliminary models of the burden of diagnosed asthma and symptoms of possible undiagnosed asthma in a large, citywide, ethnically and socioeconomically diverse sample of Chicago elementary schoolchildren. We hypothesized that considering possible asthma would give a more complete picture of race/ethnic disparities in pediatric asthma. METHODS: We studied 35,583 students aged 6 to 12 years attending Chicago Public and Archdiocese elementary schools for the Chicago Initiative to Raise Asthma Health Equity (CHIRAH) study. The full enrollments of 105 schools were surveyed for asthma and possible undiagnosed asthma by the Brief Pediatric Asthma Screen Plus (BPAS+) respiratory symptoms. The child had to be 6 to 12 years old, the valid age range for the BPAS+. Questionnaires included the BPAS+, basic demographic information, and household asthma information; they were sent home with each schoolchild for completion by the parent and returned to school for collection and scoring. RESULTS: Overall, 13.9% of students had diagnosed asthma. For children aged 6 to 12 years, rates of diagnosed asthma varied from 13.1% to 14.5%, whereas the rates of possible undiagnosed asthma varied from 14.8% to 10.9%. The rate of diagnosed asthma was 21.2% for African Americans, 9.7% for whites, 11.8% for Hispanics, with similar rates of possible undiagnosed asthma. By multinomial logistic regression, African Americans were more than twice as likely and Hispanics were 1.57 times more likely than whites to have diagnosed asthma at all school district income levels and controlling for other household members with asthma, type of school, age of the child, gender, and language preference. The odds of African Americans being diagnosed with asthma rather than having possible asthma were 76% higher and for Hispanics were 46% higher compared with whites, at all school district income levels and controlling for other household members with asthma, type of school, age of the child, gender, and language preference. CONCLUSIONS: Our study confirms national disparities in diagnosed asthma by race/ethnicity. Respiratory symptoms consistent with possible undiagnosed asthma increase the total potential burden of asthma overall to more than one-quarter of the school enrollees. Among students with respiratory symptoms, African Americans, Hispanics (controlling for language), and families where another person has asthma are more likely to have diagnosed rather than possible asthma. Improved knowledge about asthma, recognition of symptoms, and access to high-quality care are necessary to ascertain how much of the possible undiagnosed asthma represents additional cases of asthma requiring treatment.