Validation of Medicare Advantage Claims for Long-Term Outcome Assessment in Low-Risk Aortic Valve Replacement.

Background: Although Medicare Advantage (MA) plans provide coverage to >50% of Medicare beneficiaries, it is unclear whether MA claims can be used similarly to Medicare Fee-For-Service (FFS) claims for clinical outcomes assessment. In this study, we evaluate the accuracy of claims algorithms previously validated in FFS to assess comorbidities and outcomes in MA patients after aortic valve replacement.

Methods: We compared the concordance of 11 claims-based covariates (diabetes, hypertension, atrial flutter/fibrillation, myocardial infarction) and outcomes (stroke, disabling stroke, transient ischemic attack, major vascular complication, bleeding, permanent pacemaker implantation, death) among FFS and MA patients with the covariates and adjudicated outcomes in the multinational Evolut Low-Risk Trial (2016-2018).

Pollution and ecological risk of heavy metals and arsenic in in road-deposited sediment and rainfall runoff of a coal port in Northern China.

Coastal ports are important areas for national economic growth. However, due to human activity and ongoing port development, heavy metals (HMs) and arsenic (As) have increased in the road-deposited sediment (RDS) and runoff of port roads, posing a threat to the environment and ecology. This study focused on a representative coal port in northern China to assess the characterization on occurrence and ecological risk of HMs (Zn, Ni, Cr, Cu, Pb, Cd, and Hg) and As in RDS and runoff in three typical functional areas, including office, storage yard, and dock.

Characteristics of heavy metal migration during pyrolysis of typical oily wastes and environmental risk assessment of pyrolysis residues.

Solid-phase residues from pyrolysis of oily wastes (OS) are widely used due to their rich pore structure and strong adsorption capacity. In this study, pyrolysis residues (OS-P) were obtained from the pyrolysis treatment of four typical OS in Karamay, Xinjiang. The results indicate that the crystalline substances in OS-P mainly were SiO, BaSO, and graphite.

Racial and ethnic differences in disease course Medicare expenditures for beneficiaries with dementia.

Background: Research on racial and ethnic disparities in costs of care during the course of dementia is sparse. We analyzed Medicare expenditures for beneficiaries with dementia to identify when during the course of care costs are the highest and whether they differ by race and ethnicity.

Methods: We analyzed data from the 2000-2016 Health and Retirement Study (HRS) linked with corresponding Medicare claims to estimate total Medicare expenditures for four phases: (1) the year before a dementia diagnosis, (2) the first year following a dementia diagnosis, (3) ongoing care for dementia after the first year, and (4) the last year of life.

Kochia scoparia L., a newfound candidate halophyte, for phytoremediation of cadmium-contaminated saline soils.

In recent years, heavy metal pollution in saline soil is increasingly severe due to the rapid development of industry and agriculture. Halophytes can survive at higher concentrations of salt and heavy metal, which make them suitable candidates for the phytoremediation of heavy metals in saline soils. In the present study, the halophyte plant Kochia scoparia (L.

Sustained intake of animal-sourced foods is associated with less stunting in young children.

The value of animal-sourced foods (ASFs) in providing key nutrients, particularly for child growth and where diets are of low quality, is understood mainly from cross-sectional assessment of current consumption. Longitudinal panel data from Nepal, Bangladesh and Uganda were used here to assess associations among previous (lagged) and contemporaneous ASF intake with linear growth of children aged 6-24 months. Lagged ASF consumption was significantly correlated with a 10% decline in stunting in Nepali children who consumed any ASF in the previous year, while current intake was associated with a 9% decline in stunting in Uganda.

Using mobile phone data helps estimate community-level food insecurity: Findings from a multi-year panel study in Nepal.

Household food insecurity remains a major policy challenge in low-income countries. Identifying accurate measures that are relatively easy to collect has long been an important priority for governments seeking to better understand and fund solutions for communities in remote settings. Conventional approaches based on surveys can be time-consuming and costly, while data derived from satellite imagery represent proxies focused on biological processes (such as rainfall and crop growth) lack granularity in terms of human behaviors.

Cultivar variations in cadmium and lead accumulation and distribution among 30 wheat (Triticum aestivum L.) cultivars.

In recent years, heavy metal pollution in agricultural soil in China has received public concern. The concept of low-accumulation cultivars (LACs) was proposed to minimize the influx of pollutants to the human food chain. Variations in Cd and Pb accumulation, distribution, and tolerance among 30 wheat (Triticum aestivum L.

Multi-tensor investigation of orbitofrontal cortex tracts affected in subcaudate tractotomy.

Subcaudate tractotomy (SCT) is a neurosurgical lesioning procedure that can reduce symptoms in medically intractable obsessive compulsive disorder (OCD). Due to the putative importance of the orbitofrontal cortex (OFC) in symptomatology, fibers that connect the OFC, SCT lesion, and either the thalamus or brainstem were investigated with two-tensor tractography using an unscented Kalman filter approach. From this dataset, fibers were warped to Montreal Neurological Institute space, and probability maps with center-of-mass analysis were subsequently generated.

Toward Defining the Neural Substrates of ADHD: A Controlled Structural MRI Study in Medication-Naïve Adults.

Objective: We assessed the neural correlates of adult ADHD in treatment-naïve participants, an approach necessary for identifying neural substrates unconfounded by medication effects.

Method: The sample consisted of 24 medication-naïve adults with Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV) diagnosed ADHD and 24 healthy controls, comparable on age, sex, handedness, reading achievement, IQ, and psychiatric comorbidity.

Further understanding of the comorbidity between Attention-Deficit/Hyperactivity Disorder and bipolar disorder in adults: an MRI study of cortical thickness.

Although Attention-Deficit/Hyperactivity Disorder (ADHD) and Bipolar Disorder (BPD) frequently co-occur and represent a particularly morbid clinical form of both disorders, neuroimaging research addressing this comorbidity is scarce. Our aim was to evaluate cortical thickness in ADHD and BPD, testing the hypothesis that comorbid subjects (ADHD+BPD) would have neuroanatomical correlates of both disorders. Magnetic Resonance Imaging (MRI) findings were compared between 31 adults with ADHD+BPD, 18 with BPD, 26 with ADHD, and 23 healthy controls.

Covariance modeling of MRI brain volumes in memory circuitry in schizophrenia: Sex differences are critical.

Women have consistently demonstrated better verbal memory on tests that evaluate immediate and delayed free recall. In patients with schizophrenia, these verbal memory processes are relatively more preserved in women than men. However an understanding of the brain anatomy of the female advantage for verbal memory is still unclear.

Predictive learning with structured (grouped) data.

Many applications of machine learning involve sparse and heterogeneous data. For example, estimation of diagnostic models using patients' data from clinical studies requires effective integration of genetic, clinical and demographic data. Typically all heterogeneous inputs are properly encoded and mapped onto a single feature vector, used for estimating a classifier.

Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA.

In functional magnetic resonance imaging (fMRI) analysis, although the univariate general linear model (GLM) is currently the dominant approach to brain activation detection, there is growing interest in multivariate approaches such as principal component analysis, canonical variate analysis (CVA), independent component analysis and cluster analysis, which have the potential to reveal neural networks and functional connectivity in the brain. To understand the effect of processing options on performance of multivariate model-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly used fMRI preprocessing steps and optimized the associated multivariate CVA-based, single-subject processing pipelines with the NPAIRS (nonparametric prediction, activation, influence and reproducibility resampling) performance metrics [prediction accuracy and statistical parametric image (SPI) reproducibility] on the Fiswidgets platform. We also compared the single-subject SPIs of univariate GLM with multivariate CVA-based processing pipelines from SPM, FSL.

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.

Evaluation and comparison of GLM- and CVA-based fMRI processing pipelines with Java-based fMRI processing pipeline evaluation system.

Activation patterns identified by fMRI processing pipelines or fMRI software packages are usually determined by the preprocessing options, parameters, and statistical models used. Previous studies that evaluated options of GLM (general linear model)--based fMRI processing pipelines are mainly based on simulated data with receiver operating characteristics (ROC) analysis, but evaluation of such fMRI processing pipelines on real fMRI data is rare. To understand the effect of processing options on performance of GLM-based fMRI processing pipelines with real fMRI data, we investigated the impact of commonly-used fMRI preprocessing steps; optimized the associated GLM-based single-subject processing pipelines; and quantitatively compared univariate GLM (in FSL.

Automatic segmentation of left and right cerebral hemispheres from MRI brain volumes using the graph cuts algorithm.

An automated algorithm has been developed to segment stripped (non-brain tissue excluded) T1-weighted MRI brain volumes into left and right cerebral hemispheres and cerebellum+brainstem. The algorithm, which uses the Graph Cuts technique, performs a fully automated segmentation in approximately 30 s following pre-processing. It is robust and accurate and has been tested on datasets from two scanners using different field strengths and pulse sequences.