Timothy S. Chang

Childhood asthma clusters and response to therapy in clinical trials

BACKGROUND Childhood asthma clusters, or subclasses, have been developed by computational methods without evaluation of clinical utility. OBJECTIVE To replicate and determine whether childhood asthma clusters previously identified computationally in the Severe Asthma Research Program (SARP) are associated with treatment responses in Childhood Asthma Research and Education (CARE) Network clinical trials. METHODS A cluster assignment model was determined by using SARP participant data. A total of 611 participants 6 to 18 years old from 3 CARE trials were assigned to SARP pediatric clusters. Primary and secondary outcomes were analyzed by cluster in each trial. RESULTS CARE participants were assigned to SARP clusters with high accuracy. Baseline characteristics were similar between SARP and CARE children of the same cluster. Treatment response in CARE trials was generally similar across clusters. However, with the caveat of a smaller sample size, children in the early-onset/severe-lung function cluster had best response with fluticasone/salmeterol (64% vs 23% 2.5× fluticasone and 13% fluticasone/montelukast in the Best ADd-on Therapy Giving Effective Responses trial; P = .011) and children in the early-onset/comorbidity cluster had the least clinical efficacy to treatments (eg, -0.076% change in FEV1 in the Characterizing Response to Leukotriene Receptor Antagonist and Inhaled Corticosteroid trial). CONCLUSIONS In this study, we replicated SARP pediatric asthma clusters by using a separate, large clinical trials network. Early-onset/severe-lung function and early-onset/comorbidity clusters were associated with differential and limited response to therapy, respectively. Further prospective study of therapeutic response by cluster could provide new insights into childhood asthma treatment.

Estimating Wisconsin Asthma Prevalence Using Clinical Electronic Health Records and Public Health Data

OBJECTIVES We compared a statewide telephone health survey with electronic health record (EHR) data from a large Wisconsin health system to estimate asthma prevalence in Wisconsin. METHODS We developed frequency tables and logistic regression models using Wisconsin Behavioral Risk Factor Surveillance System and University of Wisconsin primary care clinic data. We compared adjusted odds ratios (AORs) from each model. RESULTS Between 2007 and 2009, the EHR database contained 376,000 patients (30,000 with asthma), and 23,000 (1850 with asthma) responded to the Behavioral Risk Factor Surveillance System telephone survey. AORs for asthma were similar in magnitude and direction for the majority of covariates, including gender, age, and race/ethnicity, between survey and EHR models. The EHR data had greater statistical power to detect associations than did survey data, especially in pediatric and ethnic populations, because of larger sample sizes. CONCLUSIONS EHRs can be used to estimate asthma prevalence in Wisconsin adults and children. EHR data may improve public health chronic disease surveillance using high-quality data at the local level to better identify areas of disparity and risk factors and guide education and health care interventions.

Sparse modeling of spatial environmental variables associated with asthma

Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsins Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patients home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsins geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors.

Machine Learning Amplifies the Effect of Parental Family History of Alzheimer’s Disease on List Learning Strategy

Identification of preclinical Alzheimers disease (AD) is an essential first step in developing interventions to prevent or delay disease onset. In this study, we examine the hypothesis that deeper analyses of traditional cognitive tests may be useful in identifying subtle but potentially important learning and memory differences in asymptomatic populations that differ in risk for developing Alzheimers disease. Subjects included 879 asymptomatic higher-risk persons (middle-aged children of parents with AD) and 355 asymptotic lower-risk persons (middle-aged children of parents without AD). All were administered the Rey Auditory Verbal Learning Test at baseline. Using machine learning approaches, we constructed a new measure that exploited finer differences in memory strategy than previous work focused on serial position and subjective organization. The new measure, based on stochastic gradient descent, provides a greater degree of statistical separation (p = 1.44 × 10-5) than previously observed for asymptomatic family history and non-family history groups, while controlling for apolipoprotein epsilon 4, age, gender, and education level. The results of our machine learning approach support analyzing memory strategy in detail to probe potential disease onset. Such distinct differences may be exploited in asymptomatic middle-aged persons as a potential risk factor for AD.

Hemodilution for Acute Ischemic Stroke

Ischemic stroke involves focal hypoperfusion of the central nervous system. Hemodilution could theoretically improve perfusion to the affected area and, therefore, reduce infarct size. To assess the effects of hemodilution on clinical outcomes in patients with acute ischemic stroke. We searched the Cochrane Stroke Group Trials Register (February 2014), the Cochrane Central Register of Controlled Trials (Issue 1, 2014), MEDLINE (January 2008 to October 2013), and EMBASE (January 2008 to October 2013). We also searched trials registers, scanned reference lists, and contacted authors. We included randomized trials of hemodilution treatment in acute ischemic stroke, started within 72 hours …

Personalizing medicine: beyond race.

Instead of using race as a shorthand for factors that directly influence health, researchers should investigate those factors themselves.