H as chronic obstructive pulmonary disease, emphysema, or asthma. Therefore we

H as chronic obstructive pulmonary disease, emphysema, or asthma. Therefore we used self-reported pulmonary medications as a surrogate BTZ-043 web marker for chronic lung disease. Pulmonary medications included beta agonists, leukotriene inhibitors, inhaled corticosteroids, combination inhalers, and other pulmonary medications such as ipatropium, cromolyn, aminophylline and theophylline.Identification of Sepsis EventsWe sought and reviewed all hospitalizations attributed by participants to a serious infection. Definitions for serious infections were based upon infection taxonomies developed by Angus, et al. [2] Two trained abstractors independently reviewed all relevant medical records to confirm 1) the presence of a serious infection on initial hospital presentation, and 2) the relevance of the serious infection as a major reason for hospitalization. The abstractors then identified clinical and laboratory information from the first 28-hours of hospitalization as well as outcomes for the hospitalization. The presence of a serious infection was based upon review of physician Emergency Department, hospital admission and hospital discharge records. We did not use laboratory, microbiological or radiographic information to define serious infections because these test results often have unclear connections with the overall clinical impression or course. We used the international consensus definition of sepsis, consisting of presentation to the hospital with an infection plus two or more systemic inflammatory response syndrome (SIRS) criteria. [1] 15900046 SIRS criteria included 1) heart rate .90 beats/ minute, 2) fever (temperature .38.3uC or ,36uC), 3) tachypnea (.20 breaths/min) or PCO2,32 mmHg, and 4) leukocytosisChronic Medical Conditions and Risk of SepsisData AnalysisWe compared demographic and clinical characteristics between sepsis and non-sepsis groups using the log-rank test for equality across strata. We used Cox proportional hazards regression to calculate hazard ratios and 95 confidence intervals for the association between each demographic and clinical factor and incident sepsis. For the Cox regression models, we defined persontime at risk as the time (days) from in-person examination to the first incidence of sepsis or the last follow-up interview, whichever came first. For the purchase ML 240 associations with each chronic medical condition, we adjusted the models for age, sex, race, education, geographic region, income, and smoking status. To assess the effect of comorbid burden upon sepsis risk, we created a Cox model evaluating the association between the number of chronic medical conditions and incident sepsis, adjusting for age, sex, race, geographic region, income, education and tobacco use. We confirmed the proportional hazards relationship for all regression models.were associated with increased incident sepsis risk, the risk was decreased with heavy or moderate alcohol use. All of the chronic medical conditions included in the analysis exhibited significant adjusted associations with incident sepsis. Chronic lung disease and chronic kidney disease exhibited the strongest adjusted associations with incident sepsis. (Table 3) The risk of incident sepsis was associated with the number of chronic medical conditions (p-trend ,0.001). (Figure 1). In a sensitivity analysis, we repeated the analysis excluding 1,157 participants with reported serious infection hospitalizations that had not yet been reviewed or adjudicated, a figure expected to yield an additiona.H as chronic obstructive pulmonary disease, emphysema, or asthma. Therefore we used self-reported pulmonary medications as a surrogate marker for chronic lung disease. Pulmonary medications included beta agonists, leukotriene inhibitors, inhaled corticosteroids, combination inhalers, and other pulmonary medications such as ipatropium, cromolyn, aminophylline and theophylline.Identification of Sepsis EventsWe sought and reviewed all hospitalizations attributed by participants to a serious infection. Definitions for serious infections were based upon infection taxonomies developed by Angus, et al. [2] Two trained abstractors independently reviewed all relevant medical records to confirm 1) the presence of a serious infection on initial hospital presentation, and 2) the relevance of the serious infection as a major reason for hospitalization. The abstractors then identified clinical and laboratory information from the first 28-hours of hospitalization as well as outcomes for the hospitalization. The presence of a serious infection was based upon review of physician Emergency Department, hospital admission and hospital discharge records. We did not use laboratory, microbiological or radiographic information to define serious infections because these test results often have unclear connections with the overall clinical impression or course. We used the international consensus definition of sepsis, consisting of presentation to the hospital with an infection plus two or more systemic inflammatory response syndrome (SIRS) criteria. [1] 15900046 SIRS criteria included 1) heart rate .90 beats/ minute, 2) fever (temperature .38.3uC or ,36uC), 3) tachypnea (.20 breaths/min) or PCO2,32 mmHg, and 4) leukocytosisChronic Medical Conditions and Risk of SepsisData AnalysisWe compared demographic and clinical characteristics between sepsis and non-sepsis groups using the log-rank test for equality across strata. We used Cox proportional hazards regression to calculate hazard ratios and 95 confidence intervals for the association between each demographic and clinical factor and incident sepsis. For the Cox regression models, we defined persontime at risk as the time (days) from in-person examination to the first incidence of sepsis or the last follow-up interview, whichever came first. For the associations with each chronic medical condition, we adjusted the models for age, sex, race, education, geographic region, income, and smoking status. To assess the effect of comorbid burden upon sepsis risk, we created a Cox model evaluating the association between the number of chronic medical conditions and incident sepsis, adjusting for age, sex, race, geographic region, income, education and tobacco use. We confirmed the proportional hazards relationship for all regression models.were associated with increased incident sepsis risk, the risk was decreased with heavy or moderate alcohol use. All of the chronic medical conditions included in the analysis exhibited significant adjusted associations with incident sepsis. Chronic lung disease and chronic kidney disease exhibited the strongest adjusted associations with incident sepsis. (Table 3) The risk of incident sepsis was associated with the number of chronic medical conditions (p-trend ,0.001). (Figure 1). In a sensitivity analysis, we repeated the analysis excluding 1,157 participants with reported serious infection hospitalizations that had not yet been reviewed or adjudicated, a figure expected to yield an additiona.