Utility of intracranial pressure monitoring in patients with traumatic brain injuries: a propensity score matching analysis of TQIP data.

PURPOSE: Intracranial pressure monitoring (ICPM) is central to traumatic brain injury (TBI) management, but its utility is controversial. METHODS: The 2016-2017 TQIP database was queried for isolated TBI. Patients with ICPM [(ICPM (+)] were propensity-score matched (PSM) to those without ICPM [ICPM (-)] and divided into three age groups by years (< 18, 18-54, ≥  55). RESULTS: PSM yielded 2125 patients in each group. Patients aged < 18 years had a higher survival probability (p = 0.013) and decreased mortality (p = 0.016) in the ICPM (+) group. Complications were higher and LOS was longer in ICPM (+) patients aged 18-54 years and ≥ 55 years, but not in patients aged < 18 years. CONCLUSIONS: ICPM (+) is associated with a survival benefit without an increase in complications in patents aged < 18 years. In patients aged ≥ 18 years, ICPM (+) is associated with more complications and longer LOS without a survival benefit.

Fabricating 3-dimensional human brown adipose microtissues for transplantation studies.

Transplanting cell cultured brown adipocytes (BAs) represents a promising approach to prevent and treat obesity (OB) and its associated metabolic disorders, including type 2 diabetes mellitus (T2DM). However, transplanted BAs have a very low survival rate in vivo. The enzymatic dissociation during the harvest of fully differentiated BAs also loses significant cells. There is a critical need for novel methods that can avoid cell death during cell preparation, transplantation, and in vivo. Here, we reported that preparing BAs as injectable microtissues could overcome the problem. We found that 3D culture promoted BA differentiation and UCP-1 expression, and the optimal initial cell aggregate size was 100 µm. The microtissues could be produced at large scales via 3D suspension assisted with a PEG hydrogel and could be cryopreserved. Fabricated microtissues could survive in vivo for long term. They alleviated body weight and fat gain and improved glucose tolerance and insulin sensitivity in high-fat diet (HFD)-induced OB and T2DM mice. Transplanted microtissues impacted multiple organs, secreted protein factors, and influenced the secretion of endogenous adipokines. To our best knowledge, this is the first report on fabricating human BA microtissues and showing their safety and efficacy in T2DM mice. The proposal of transplanting fabricated BA microtissues, the microtissue fabrication method, and the demonstration of efficacy in T2DM mice are all new. Our results show that engineered 3D human BA microtissues have considerable advantages in product scalability, storage, purity, safety, dosage, survival, and efficacy.

Influence function methods to assess the effectiveness of influenza vaccine with survey data.

OBJECTIVE: To examine a robust relative risk (RR) estimation for survey data analysis with ideal inferential properties under various model assumptions. DATA SOURCES: We employed secondary data from the Household Component of the 2000-2016 US Medical Expenditure Panel Survey (MEPS). STUDY DESIGN: We investigate a broad range of data-balancing techniques by implementing influence function (IF) methods, which allows us to easily estimate the variability for the RR estimates in the complex survey setting. We conduct a simulation study of seasonal influenza vaccine effectiveness to evaluate these approaches and discuss techniques that show robust inferential performance across model assumptions. DATA COLLECTION/EXTRACTION METHODS: Demographic information, vaccine status, and self-administered questionnaire surveys were obtained from the longitudinal data files. We linked this information with medical condition files and medical event to extract the disease type and associated expenditures for each medical visit. We excluded individuals who were 18 years or younger at the beginning of each panel. PRINCIPAL FINDINGS: Under various model assumptions, the IF methods show robust inferential performance when the data-balancing procedures are incorporated. Once IF methods and data-balancing techniques are implemented, contingency table-based RR estimation yields a comparable result to the generalized linear model approach. We demonstrate the applicability of the proposed methods for complex survey data using 2000-2016 MEPS data. When employing these methods, we find a significant, negative association between vaccine effectiveness (VE) estimates and influenza-incurred expenditures. CONCLUSIONS: We describe and demonstrate a robust method for RR estimation and relevant inferences for influenza vaccine effectiveness using MEPS data. The proposed method is flexible and can be extended to weighted data for survey data analysis. Hence, these methods have great potential for health services research, especially when data are nonexperimental and imbalanced.

Changes in arachidonic acid (AA)- and linoleic acid (LA)-derived hydroxy metabolites and their interplay with inflammatory biomarkers in response to drastic changes in air pollution exposure.

BACKGROUND: Untargeted metabolomics analyses have indicated that fatty acids and their hydroxy derivatives may be important metabolites in the mechanism through which air pollution potentiates diseases. This study aimed to use targeted analysis to investigate how metabolites in arachidonic acid (AA) and linoleic acid (LA) pathways respond to short-term changes in air pollution exposure. We further explored how they might interact with markers of antioxidant enzymes and systemic inflammation. METHODS: This study included a subset of participants (n = 53) from the Beijing Olympics Air Pollution (BoaP) study in which blood samples were collected before, during, and after the Beijing Olympics. Hydroxy fatty acids were measured by liquid chromatography/mass spectrometry (LC/MS). Native total fatty acids were measured as fatty acid methyl esters (FAMEs) using gas chromatography. A set of chemokines were measured by ELISA-based chemiluminescent assay and antioxidant enzyme activities were analyzed by kinetic enzyme assays. Changes in levels of metabolites over the three time points were examined using linear mixed-effects models, adjusting for age, sex, body mass index (BMI), and smoking status. Pearson correlation and repeated measures correlation coefficients were calculated to explore the relationships of metabolites with levels of serum chemokines and antioxidant enzymes. RESULTS: 12-hydroxyeicosatetraenoic acid (12-HETE) decreased by 50.5% (95% CI: -66.5, -34.5; p < 0.0001) when air pollution dropped during the Olympics and increased by 119.4% (95% CI: 36.4, 202.3; p < 0.0001) when air pollution returned to high levels after the Olympics. In contrast, 13-hydroxyoctadecadienoic acid (13-HODE) elevated significantly (p = 0.023) during the Olympics and decreased nonsignificantly after the games (p = 0.104). Interleukin 8 (IL-8) correlated with 12-HETE (r = 0.399, BH-adjusted p = 0.004) and 13-HODE (r = 0.342, BH-adjusted p = 0.014) over the three points; it presented a positive and moderate correlation with 12-HETE during the Olympics (r = 0.583, BH-adjusted p = 0.002) and with 13-HODE before the Olympics (r = 0.543, BH-adjusted p = 0.008). CONCLUSION: AA- and LA-derived hydroxy metabolites are associated with air pollution and might interact with systemic inflammation in response to air pollution exposure.

A framework for stability-based module detection in correlation graphs.

Graphs can be used to represent the direct and indirect relationships between variables, and elucidate complex relationships and interdependencies. Detecting structure within a graph is a challenging problem. This problem is studied over a range of fields and is sometimes termed community detection, module detection, or graph partitioning. A popular class of algorithms for module detection relies on optimizing a function of modularity to identify the structure. In practice, graphs are often learned from the data, and thus prone to uncertainty. In these settings, the uncertainty of the network structure can become exaggerated by giving unreliable estimates of the module structure. In this work, we begin to address this challenge through the use of a nonparametric bootstrap approach to assessing the stability of module detection in a graph. Estimates of stability are presented at the level of the individual node, the inferred modules, and as an overall measure of performance for module detection in a given graph. Furthermore, bootstrap stability estimates are derived for complexity parameter selection that ultimately defines a graph from data in a way that optimizes stability. This approach is utilized in connection with correlation graphs but is generalizable to other graphs that are defined through the use of dissimilarity measures. We demonstrate our approach using a broad range of simulations and on a metabolomics dataset from the Beijing Olympics Air Pollution study. These approaches are implemented using bootcluster package that is available in the R programming language.

Estimating model-based nonnegative population marginal means in application to medical expenditures covered by different health care policies - A study on Medical Expenditure Panel Survey.

The medical care expenditure is historically an important public health issue, which greatly impacts the government's health policies as well as patients' financial and medical decisions. In population health research, we commonly discretize a numeric attribute to a few ordinal groups to examine population characteristics. Oftentimes, the population marginal mean estimation by the ANOVA approach is inflexible since it uses pre-defined grouping of the covariate. In this paper, we propose a method to estimate the population marginal mean using the B-spline-based regression in a manner of a generalized additive model as an alternative for the ANOVA. Since the medical expenditure is always nonnegative, a Bayesian approach is also implemented for the nonnegative constraint on the marginal mean estimates. The proposed method is flexible to estimate marginal means for user-specified grouping after model fitting in a post-hoc manner, a clear advantage over the ANOVA approach. We show that this method is inferentially superior to the ANOVA through theoretical investigations and an extensive Monte Carlo study. The real data analysis using Medical Expenditure Panel Survey data assisted by some visualization tools demonstrates an applicability of the proposed approach and leads us some interesting observations that may be relevant to public health discussions.

Estimation of the area under a curve via several B-spline-based regression methods and applications.

Estimating the area under a curve (AUC) is an important subject in many fields of medicine and science. The regression model using B-spline functions provides flexibility in curve fitting, making it suitable for AUC estimation with various types of nonlinear trends. Despite the versatility of the B-spline approach, comprehensive discussions regarding relevant AUC estimation techniques using B-spline functions and their comparison with existing methods cannot be found in extant literature. In this paper, we investigate AUC estimation using B-spline regression and B-spline regression with several penalties, as well as discuss corresponding inferences. We carry out an extensive Monte Carlo study to evaluate the performance of the proposed methods in various realistic pharmacokinetics and analytical chemistry data settings. We show that the proposed methods provide robust and reliable AUC estimation regardless of different types of nonlinear models from scientific and medical research areas. Our proposed method is appropriate for general AUC estimation since it does not require nonlinear model specifications and inference techniques corresponding to the specified model.