BACKGROUND AND OBJECTIVES: Phlebotomy excess contributes to anemia in PICU patients and increases the likelihood of red blood cell transfusion, which is associated with risk of adverse outcomes. Excessive phlebotomy reduction (EPR) strategies may reduce the need for transfusion, but have not been evaluated in a PICU population. We hypothesized that EPR strategies, facilitated by implementation science methods, would decrease excess blood drawn and reduce transfusion frequency. METHODS: Quantitative and qualitative methods were used. Patient and blood draw data were collected with survey and focus group data to evaluate knowledge and attitudes before and after EPR intervention. The Consolidated Framework for Implementation Research was used to interpret qualitative data. Multivariate regression was employed to adjust for potential confounders for blood overdraw volume and transfusion incidence.
Objective: Our goal was to identify risk factors for acute kidney injury (AKI) in children surviving cardiac arrest (CA). Design: Retrospective analysis of a public-access dataset. Setting: Fifteen children’s hospitals associated with the Pediatric Emergency Care Applied Research Network. Patients: Two hundred ninety-six subjects between 1 day and 18 years of age who experienced in-hospital or out-of-hospital CA between July 1, 2003, and December 31, 2004. Interventions: None. Measurements and Main Results: Our primary outcome was development of AKI as defined by the Acute Kidney Injury Network (AKIN) staged criteria. An ordinal logistic model was developed using 8 candidate variables. We found 6 critical explanatory variables, including total number of epinephrine doses, post-CA blood pressure, arrest location, presence of a chronic lung condition, pH nadir, and presence of an abnormal baseline creatinine. Conclusions: This study is the first to identify risk factors for AKI in children after CA. Our findings regarding the impact of epinephrine dosing are of particular interest and suggest potential for epinephrine toxicity with regard to AKI. The ability to identify and potentially modify risk factors for AKI after CA may lead to improved morbidity and mortality in this challenging population. Key Words: cardiac arrest; children; pediatric; outcome; acute kidney injury; epinephrine.
To understand how the nature of scientific collaboration between individuals and sites in team-based research initiatives affect collaboration and research output, we examined four waves of prospective survey data to measure collaboration across investigators, disciplines, and sites to measure structural determinants of research success. 116 investigators in the five sites of the NIH-funded U54 Transdisciplinary Research on Energetics and Cancer (TREC) initiative were surveyed about their research ties with a 2011 baseline measure and followed by three additional iterations and augmented by bibliometric data. Social network analysis describes the changing structure of contact and cooperation. We found that the network structure of a team science project affects the nature and rate of publications, implying that funded projects vary in research output based on how investigators interact with each other and that the design of scientific research projects affects research output by determining levels of contact between actual and potential collaborators. Keywords: cancer; research; transdisciplinarity; team science; network models.
Purpose To quantify the coinciding improvement in the clinical diagnosis of sepsis, its documentation in the electronic health records, and subsequent medical coding of sepsis for billing purposes in recent years. Methods We examined 98,267 hospitalizations in 66,208 patients who met systemic inflammatory response syndrome criteria at a tertiary care center from 2008 to 2012. We used g-computation to estimate the causal effect of the year of hospitalization on receiving an International Classification of Diseases, Ninth Revision, Clinical Modification discharge diagnosis code for sepsis by estimating changes in the probability of getting diagnosed and coded for sepsis during the study period. Results When adjusted for demographics, Charlson-Deyo comorbidity index, blood culture frequency per hospitalization, and intensive care unit admission, the causal risk difference for receiving a discharge code for sepsis per 100 hospitalizations with systemic inflammatory response syndrome, had the hospitalization occurred in 2012, was estimated to be 3.9% (95% confidence interval [CI], 3.8%–4.0%), 3.4% (95% CI, 3.3%–3.5%), 2.2% (95% CI, 2.1%–2.3%), and 0.9% (95% CI, 0.8%–1.1%) from 2008 to 2011, respectively. Conclusions Patients with similar characteristics and risk factors had a higher of probability of getting diagnosed, documented, and coded for sepsis in 2012 than in previous years, which contributed to an apparent increase in sepsis incidence. Keywords: Causality, ICD-9-CM, Sepsis, Systemic inflammatory response syndrome, Risk difference
An accelerometer, a wearable motion sensor on the hip or wrist, is becoming a popular tool in clinical and epidemiological studies for measuring the physical activity. Such data provide a series of activity counts at every minute or even more often and displays a person’s activity pattern throughout a day. Unfortunately, the collected data can include irregular missing intervals because of noncompliance of participants and therefore make the statistical analysis more challenging. The purpose of this study is to develop a novel imputation method to handle the multivariate count data, motivated by the accelerometer data structure. We specify the predictive distribution of the missing data with a mixture of zero-inflated Poisson and Log-normal distribution, which is shown to be effective to deal with the minute-by-minute autocorrelation as well as under- and over-dispersion of count data. The imputation is performed at the minute level and follows the principles of multiple imputation using a fully conditional specification with the chained algorithm. To facilitate the practical use of this method, we provide an R package accelmissing. Our method is demonstrated using 2003-2004 National Health and Nutrition Examination Survey data. Keywords Accelerometer, physical activity, missing count data, multiple imputation, zero-inflated model, Poisson log-normal
Background. The long-term and cumulative effect of multiple episodes of bacteremia and sepsis across multiple hospitalizations on the development of cardiovascular (CV) events is uncertain.
Purpose Past studies of sepsis epidemiology did not address misclassification bias due to imperfect verification of sepsis detection methods to estimate the true prevalence. Methods We examined 273,126 hospitalizations from 2008 to 2012 at a tertiary-care center to develop surveillance-aimed sepsis detection criteria, based on the presence of the sepsis-explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes (995.92 or 785.52), blood culture orders, and antibiotics administration. We used Bayesian multinomial latent class models to estimate the true prevalence of sepsis, while adjusting for the imperfect sensitivity and specificity and the conditional dependence among the individual criteria. Results The apparent annual prevalence of sepsis hospitalizations based on explicit International Classification of Diseases, Ninth Revision, Clinical Modification codes were 1.5%, 1.4%, 1.6%, 2.2%, and 2.5% for the years 2008 to 2012. Bayesian posterior estimates for the true prevalence of sepsis suggested that it remained stable from 2008, 19.2% (95% credible interval [CI]: 17.9%, 22.9%), to 2012, 17.8% (95% CI: 16.8%, 20.2%). The sensitivity of sepsis-explicit codes, however, increased from 7.6% (95% CI: 6.4%, 8.4%) in 2008 to 13.8% (95% CI: 12.2%, 14.9%) in 2012. Conclusions The true prevalence of sepsis remained high, but stable despite an increase in the sensitivity of sepsis-explicit codes in administrative data.
We apply a specialized Bayesian method that helps us deal with the methodological challenge of unobservedheterogeneity among immigrant voters. Our approach is based on \emphgeneralized linear mixed Dirichlet models (GLMDM) whererandom effects are specified semiparametrically using a Dirichlet process mixture prior that has been shown to account forunobserved grouping in the data. Such models are drawn from Bayesian nonparametrics to help overcome objections handling latenteffects with strongly informed prior distributions. Using 2009 German voting data of immigrants, we show that for difficultproblems of missing key covariates and unexplained heterogeneity this approach provides (1) overall improved model fit, (2)smaller standard errors on average, and (3) less bias from omitted variables. As a result, the GLMDM changed our substantiveunderstanding of the factors affecting immigrants’ turnout and vote choice. Once we account for unobserved heterogeneity amongimmigrant voters, whether a voter belongs to the first immigrant generation or not is much less important than the extantliterature suggests. When looking at vote choice we also found that an immigrant’s degree of structural integration does notaffect the vote in favor of the CDU/CSU, a party which is traditionally associated with restrictive immigration policy.