Pharmacy Interventions in Transitions of Care from Hospital Discharge (PITCH) Pilot Program for Medicare Part D Patients at High-Risk for Readmission

Background: Pharmacist implemented transitions-of-care (TOC) programs focus on identifying adherence issues, developing care plans, investigating medication-related insurance problems, and instilling the value of medication treatment in patients frequently admitted to hospitals for manageable, chronic disease-states. Study objective: The primary objective of this study is to determine the impact of pharmacist interventions during a TOC pilot on hospital readmission rates for patients with acute myocardial infarction (AMI), heart failure (HF), chronic obstructive pulmonary disease (COPD), or pneumonia. Methods: This study is a retrospective review of patients receiving high intensity care to prevent readmission, including pharmacy intervention during a three-month pilot period versus patients with the same disease states not receiving high intensity care. The patient population includes patients greater than 18 years of age who were admitted for AMI, HF, COPD, or pneumonia. Patients were excluded if less than 18 years of age and prisoners. Results: Hospital records identified 513 patients eligible for study inclusion following removal of exclusion patients. The study showed no statistical evidence to conclude that pharmacy intervention has effect on readmission rate when pharmacy intervention in the Medicare population was compared to the non-pharmacy intervention Medicare population (Fisher’s Exact P = 0.123). Conclusion: The study failed to show a significant difference in readmissions for patients receiving additional pharmacy care. Other factors play a role in readmission risk. Additional studies including more patients and comparing risk factors for readmission are needed to determine best practices to reduce risk while promoting patient health

The role of genetic and environmental oxidative stress factors in prostate cancer.

Prostate cancer (PCA) development may be influenced by genetic variations within oxidative stress response (OSR) related mechanisms, such as antioxidation (e.g., carcinogen metabolism/detoxification), DNA repair, and apoptotic regulation. Excessive oxidative stress can produce DNA base changes, damage tumor suppressors, enhance proto-oncogene expression, and induce malignant transformation of cells. Persistent oxidative stress may even trigger apoptosis. Environmental reactive oxygen species (ROS) exposure attributable to lifestyle factors may exacerbate this situation by increasing oxidative stress. Therefore, it is likely that genetic variation resulting in compromised ROS capacity combined with increased environmental ROS exposure may increase PCA risk and disease aggressiveness. Consequently, this research evaluated the individual and joint modifying effects of OSR 242 genetic and 27 environmental factors in relation to PCA development among men of European and African descents. This analysis utilized a combination of traditional and innovative advanced mathematical methodologies that provided an opportunity to visualize, verify, and evaluate the predictive accuracy of higher-order interactions as indicators of disease risk and aggressiveness. Our analysis identified several OSR sequence variants to individually associated PCA risk among MED. In addition, antioxidative- and apoptotic-related SNPs were linked to increased disease risk in MAD. Higher order interaction analyses for across both populations detected gene-gene combinations among antioxidative- and apoptoticrelated sequence targets associated with increased risk. The potential functional consequences of these polymorph isms suggest that compromised detoxification and apoptotic induction may cause increased risk for PCA and more aggressive disease. Our results also indicate that environmental factors related to meat consumption and cooking methods may contribute to PCA mechanisms. Unfortunately, we were not able to characterize environmental factors alone or combined with gene variants that are involved in PCA. This may be attributed to MDR data filtering, small MAD sample size, or limitations in some study variables (e.g., meat-derived carcinogen exposure). However, future analysis within larger study populations, more accurate exposure variables, and improved computational power may allow us to identify and validate environmental factors relevant to PCA development

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In this paper, we introduce the concept of tree-based pipeline optimization for automating one of the most tedious parts of machine learning---pipeline design. We implement a Tree-based Pipeline Optimization Tool (TPOT) and demonstrate its effectiveness on a series of simulated and real-world genetic data sets. In particular, we show that TPOT can build machine learning pipelines that achieve competitive classification accuracy and discover novel pipeline operators---such as synthetic feature constructors---that significantly improve classification accuracy on these data sets. We also highlight the current challenges to pipeline optimization, such as the tendency to produce pipelines that overfit the data, and suggest future research paths to overcome these challenges. As such, this work represents an early step toward fully automating machine learning pipeline design.Comment: 16 pages, 5 figures, to appear in EvoBIO 2016 proceeding

Interaction among apoptosis-associated sequence variants and joint effects on aggressive prostate cancer

<p>Abstract</p> <p>Background</p> <p>Molecular and epidemiological evidence demonstrate that altered gene expression and single nucleotide polymorphisms in the apoptotic pathway are linked to many cancers. Yet, few studies emphasize the interaction of variant apoptotic genes and their joint modifying effects on prostate cancer (PCA) outcomes. An exhaustive assessment of all the possible two-, three- and four-way gene-gene interactions is computationally burdensome. This statistical conundrum stems from the prohibitive amount of data needed to account for multiple hypothesis testing.</p> <p>Methods</p> <p>To address this issue, we systematically prioritized and evaluated individual effects and complex interactions among 172 apoptotic SNPs in relation to PCA risk and aggressive disease (i.e., Gleason score ≥ 7 and tumor stages III/IV). Single and joint modifying effects on PCA outcomes among European-American men were analyzed using statistical epistasis networks coupled with multi-factor dimensionality reduction (SEN-guided MDR). The case-control study design included 1,175 incident PCA cases and 1,111 controls from the prostate, lung, colo-rectal, and ovarian (PLCO) cancer screening trial. Moreover, a subset analysis of PCA cases consisted of 688 aggressive and 488 non-aggressive PCA cases. SNP profiles were obtained using the NCI Cancer Genetic Markers of Susceptibility (CGEMS) data portal. Main effects were assessed using logistic regression (LR) models. Prior to modeling interactions, SEN was used to pre-process our genetic data. SEN used network science to reduce our analysis from > 36 million to < 13,000 SNP interactions. Interactions were visualized, evaluated, and validated using entropy-based MDR. All parametric and non-parametric models were adjusted for age, family history of PCA, and multiple hypothesis testing.</p> <p>Results</p> <p>Following LR modeling, eleven and thirteen sequence variants were associated with PCA risk and aggressive disease, respectively. However, none of these markers remained significant after we adjusted for multiple comparisons. Nevertheless, we detected a modest synergistic interaction between <it>AKT3 rs2125230-PRKCQ rs571715 </it>and disease aggressiveness using SEN-guided MDR (p = 0.011).</p> <p>Conclusions</p> <p>In summary, entropy-based SEN-guided MDR facilitated the logical prioritization and evaluation of apoptotic SNPs in relation to aggressive PCA. The suggestive interaction between <it>AKT3-PRKCQ </it>and aggressive PCA requires further validation using independent observational studies.</p

Examination of polymorphic glutathione S-transferase (GST) genes, tobacco smoking and prostate cancer risk among Men of African Descent: A case-control study

<p>Abstract</p> <p>Background</p> <p>Polymorphisms in <it>glutathione S-transferase </it>(GST) genes may influence response to oxidative stress and modify prostate cancer (PCA) susceptibility. These enzymes generally detoxify endogenous and exogenous agents, but also participate in the activation and inactivation of oxidative metabolites that may contribute to PCA development. Genetic variations within selected <it>GST </it>genes may influence PCA risk following exposure to carcinogen compounds found in cigarette smoke and decreased the ability to detoxify them. Thus, we evaluated the effects of polymorphic <it>GSTs </it>(<it>M1</it>, <it>T1</it>, and <it>P1</it>) alone and combined with cigarette smoking on PCA susceptibility.</p> <p>Methods</p> <p>In order to evaluate the effects of <it>GST </it>polymorphisms in relation to PCA risk, we used TaqMan allelic discrimination assays along with a multi-faceted statistical strategy involving conventional and advanced statistical methodologies (e.g., Multifactor Dimensionality Reduction and Interaction Graphs). Genetic profiles collected from 873 men of African-descent (208 cases and 665 controls) were utilized to systematically evaluate the single and joint modifying effects of <it>GSTM1 </it>and <it>GSTT1 </it>gene deletions, <it>GSTP1 </it>105 Val and cigarette smoking on PCA risk.</p> <p>Results</p> <p>We observed a moderately significant association between risk among men possessing at least one variant <it>GSTP1 </it>105 Val allele (OR = 1.56; 95%CI = 0.95-2.58; p = 0.049), which was confirmed by MDR permutation testing (p = 0.001). We did not observe any significant single gene effects among <it>GSTM1 </it>(OR = 1.08; 95%CI = 0.65-1.82; p = 0.718) and <it>GSTT1 </it>(OR = 1.15; 95%CI = 0.66-2.02; p = 0.622) on PCA risk among all subjects. Although the <it>GSTM1</it>-<it>GSTP1 </it>pairwise combination was selected as the best two factor LR and MDR models (p = 0.01), assessment of the hierarchical entropy graph suggested that the observed synergistic effect was primarily driven by the <it>GSTP1 </it>Val marker. Notably, the <it>GSTM1</it>-<it>GSTP1 </it>axis did not provide additional information gain when compared to either loci alone based on a hierarchical entropy algorithm and graph. Smoking status did not significantly modify the relationship between the <it>GST </it>SNPs and PCA.</p> <p>Conclusion</p> <p>A moderately significant association was observed between PCA risk and men possessing at least one variant <it>GSTP1 </it>105 Val allele (p = 0.049) among men of African descent. We also observed a 2.1-fold increase in PCA risk associated with men possessing the <it>GSTP1 </it>(Val/Val) and <it>GSTM1 </it>(*1/*1 + *1/*0) alleles. MDR analysis validated these findings; detecting <it>GSTP1 </it>105 Val (p = 0.001) as the best single factor for predicting PCA risk. Our findings emphasize the importance of utilizing a combination of traditional and advanced statistical tools to identify and validate single gene and multi-locus interactions in relation to cancer susceptibility.</p

Level of agreement between frequently used cardiovascular risk calculators in people living with HIV

Objectives The aim of the study was to describe agreement between the QRISK2, Framingham and Data Collection on Adverse Events of Anti‐HIV Drugs (D:A:D) cardiovascular disease (CVD) risk calculators in a large UK study of people living with HIV (PLWH). Methods PLWH enrolled in the Pharmacokinetic and Clinical Observations in People over Fifty (POPPY) study without a prior CVD event were included in this study. QRISK2, Framingham CVD and the full and reduced D:A:D CVD scores were calculated; participants were stratified into ‘low’ ( 20%) categories for each. Agreement between scores was assessed using weighted kappas and Bland–Altman plots. Results The 730 included participants were predominantly male (636; 87.1%) and of white ethnicity (645; 88.5%), with a median age of 53 [interquartile range (IQR) 49–59] years. The median calculated 10‐year CVD risk was 11.9% (IQR 6.8–18.4%), 8.9% (IQR 4.6–15.0%), 8.5% (IQR 4.8–14.6%) and 6.9% (IQR 4.1–11.1%) when using the Framingham, QRISK2, and full and reduced D:A:D scores, respectively. Agreement between the different scores was generally moderate, with the highest level of agreement being between the Framingham and QRISK2 scores (weighted kappa = 0.65) but with most other kappa coefficients in the 0.50–0.60 range. Conclusions Estimates of predicted 10‐year CVD risk obtained with commonly used CVD risk prediction tools demonstrate, in general, only moderate agreement among PLWH in the UK. While further validation with clinical endpoints is required, our findings suggest that care should be taken when interpreting any score alone

The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar and APOGEE-2 Data

This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library (MaStar) accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2) survey which publicly releases infra-red spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the sub-survey Time Domain Spectroscopic Survey (TDSS) data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey (SPIDERS) sub-survey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated Value Added Catalogs (VACs). This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper (MWM), Local Volume Mapper (LVM) and Black Hole Mapper (BHM) surveys

Toward the integrated marine debris observing system

Plastics and other artificial materials pose new risks to the health of the ocean. Anthropogenic debris travels across large distances and is ubiquitous in the water and on shorelines, yet, observations of its sources, composition, pathways, and distributions in the ocean are very sparse and inaccurate. Total amounts of plastics and other man-made debris in the ocean and on the shore, temporal trends in these amounts under exponentially increasing production, as well as degradation processes, vertical fluxes, and time scales are largely unknown. Present ocean circulation models are not able to accurately simulate drift of debris because of its complex hydrodynamics. In this paper we discuss the structure of the future integrated marine debris observing system (IMDOS) that is required to provide long-term monitoring of the state of this anthropogenic pollution and support operational activities to mitigate impacts on the ecosystem and on the safety of maritime activity. The proposed observing system integrates remote sensing and in situ observations. Also, models are used to optimize the design of the system and, in turn, they will be gradually improved using the products of the system. Remote sensing technologies will provide spatially coherent coverage and consistent surveying time series at local to global scale. Optical sensors, including high-resolution imaging, multi- and hyperspectral, fluorescence, and Raman technologies, as well as SAR will be used to measure different types of debris. They will be implemented in a variety of platforms, from hand-held tools to ship-, buoy-, aircraft-, and satellite-based sensors. A network of in situ observations, including reports from volunteers, citizen scientists and ships of opportunity, will be developed to provide data for calibration/validation of remote sensors and to monitor the spread of plastic pollution and other marine debris. IMDOS will interact with other observing systems monitoring physical, chemical, and biological processes in the ocean and on shorelines as well as the state of the ecosystem, maritime activities and safety, drift of sea ice, etc. The synthesized data will support innovative multi-disciplinary research and serve a diverse community of users

Depression, lifestyle factors and cognitive function in people living with HIV and comparable HIV-negative controls

We investigated whether differences in cognitive performance between people living with HIV (PLWH) and comparable HIV-negative people were mediated or moderated by depressive symptoms and lifestyle factors. METHODS: A cross-sectional study of 637 'older' PLWH aged ≥ 50 years, 340 'younger' PLWH aged < 50 years and 276 demographically matched HIV-negative controls aged ≥ 50 years enrolled in the Pharmacokinetic and Clinical Observations in People over Fifty (POPPY) study was performed. Cognitive function was assessed using a computerized battery (CogState). Scores were standardized into Z-scores [mean = 0; standard deviation (SD) = 1] and averaged to obtain a global Z-score. Depressive symptoms were evaluated via the Patient Health Questionnaire (PHQ-9). Differences between the three groups and the effects of depression, sociodemographic factors and lifestyle factors on cognitive performance were evaluated using median regression. All analyses accounted for age, gender, ethnicity and level of education. RESULTS: After adjustment for sociodemographic factors, older and younger PLWH had poorer overall cognitive scores than older HIV-negative controls (P < 0.001 and P = 0.006, respectively). Moderate or severe depressive symptoms were more prevalent in both older (27%; P < 0.001) and younger (21%; P < 0.001) PLWH compared with controls (8%). Depressive symptoms (P < 0.001) and use of hashish (P = 0.01) were associated with lower cognitive function; alcohol consumption (P = 0.02) was associated with better cognitive scores. After further adjustment for these factors, the difference between older PLWH and HIV-negative controls was no longer significant (P = 0.08), while that between younger PLWH and older HIV-negative controls remained significant (P = 0.01). CONCLUSIONS: Poorer cognitive performances in PLWH compared with HIV-negative individuals were, in part, mediated by the greater prevalence of depressive symptoms and recreational drug use reported by PLWH