Pharmacy-led initiative for improving peri-operative medication reconciliation among bariatric surgical patients: what is the role?

BACKGROUND: Multiple medication changes are common after bariatric surgery, but pharmacist assistance in this setting is not well described. This study evaluated the feasibility and effectiveness of a pharmacy-led initiative for facilitating discharge medicine reconciliation after bariatric surgery. METHODS: A standardized post-operative pharmacy consult evaluation was conducted on bariatric surgery inpatients at a single academic center starting 1/2/2019. Retrospective chart review evaluated patient characteristics, medication changes, and 30-day outcomes pre-intervention (7/2018-12/2018) and post-intervention (1/2019-12/2019). Two-sample t tests or binomial tests were used for continuous or categorical variables, respectively; a p-value of < 0.05 was deemed statistically significant. RESULTS: A total of 353 patients were identified for study inclusion (n = 158 pre-intervention, n = 195 post-intervention) with a mean age of 45 years, 87% female, and 71% sleeve gastrectomy. Overall pharmacy consultation compliance was 94% with 77.0% of home medication recommendations followed. Non-narcotic pain medication prescription use significantly increased (39% pre- vs. 54% post-intervention; p < 0.001). At discharge, the average number of changed or new medications significantly increased (3.7 ± 1.2 pre- vs. 4.2 ± 1.8 post-intervention; p = 0.003) while the average number of stopped medications was similar (1.2 ± 1.5 pre- vs. 1.5 ± 1.9 post-intervention; p = 0.09). Anti-hypertensive medications were decreased or stopped substantially more often with pharmacist input (44.7% pre- vs. 85.4% post-intervention; p < 0.001). Three medication-related readmissions happened pre-intervention with none post-intervention. Outpatient medication-related phone calls did considerably increase (31% pre- vs. 39% post-intervention; p = 0.04), while overall 30-day readmissions significantly decreased (7.6% pre- vs. 1.5% post-intervention; p = 0.04). CONCLUSIONS: Inpatient pharmacy consultation facilitated rapid alteration to more appropriate therapy for hypertension management and significantly increased use of non-narcotic pain medications upon discharge among bariatric surgery patients. Improved protocol adherence is anticipated with program maturity and patient education interventions will be deployed to address outpatient phone calls.

Atom Probe Mass Spectrometry of Uranium Isotopic Reference Materials.

Atom probe tomography (APT)-based isotopic analyses are becoming increasingly attractive for analysis applications requiring small volumes of material and sub-micrometer length scales, such as isotope geochemistry, nuclear safety, and materials science. However, there is an open question within the atom probe community as to the reliability of atom probe isotopic and elemental analyses. Using our proposed analysis guidelines, in conjunction with an empirical calibration curve and a machine learning-based adaptive peak fitting algorithm, we demonstrate accurate and repeatable uranium isotopic analyses, via atom probe mass spectrometry, on U3O8 isotopic reference materials. By using isotopic reference materials, each measured isotopic abundance value could be directly compared to a known certified reference value to permit a quantitative statement of accuracy. The isotopic abundance measurements for 235U and 238U in each individual APT sample were consistently within ±1.5% relative to the known reference values. The accuracy and repeatability are approaching values consistent with measurements limited primarily by Poisson counting statistics, i.e., the number of uranium atoms recorded.

Exploring the accuracy of isotopic analyses in atom probe mass spectrometry.

Atom probe tomography (APT) can theoretically deliver accurate chemical and isotopic analyses at a high level of sensitivity, precision, and spatial resolution. However, empirical APT data often contain significant biases that lead to erroneous chemical concentration and isotopic abundance measurements. The present study explores the accuracy of quantitative isotopic analyses performed via atom probe mass spectrometry. A machine learning-based adaptive peak fitting algorithm was developed to provide a reproducible and mathematically defensible means to determine peak shapes and intensities in the mass spectrum for specific ion species. The isotopic abundance measurements made with the atom probe are compared directly with the known isotopic abundance values for each of the materials. Even in the presence of exceedingly high numbers of multi-hit detection events (up to 80%), and in the absence of any deadtime corrections, our approach produced isotopic abundance measurements having an accuracy consistent with values limited predominantly by counting statistics.

Dynamic Network Model for Smart City Data-Loss Resilience Case Study: City-to-City Network for Crime Analytics.

Today's cities generate tremendous amounts of data, thanks to a boom in affordable smart devices and sensors. The resulting big data creates opportunities to develop diverse sets of context-aware services and systems, ensuring smart city services are optimized to the dynamic city environment. Critical resources in these smart cities will be more rapidly deployed to regions in need, and those regions predicted to have an imminent or prospective need. For example, crime data analytics may be used to optimize the distribution of police, medical, and emergency services. However, as smart city services become dependent on data, they also become susceptible to disruptions in data streams, such as data loss due to signal quality reduction or due to power loss during data collection. This paper presents a dynamic network model for improving service resilience to data loss. The network model identifies statistically significant shared temporal trends across multivariate spatiotemporal data streams and utilizes these trends to improve data prediction performance in the case of data loss. Dynamics also allow the system to respond to changes in the data streams such as the loss or addition of new information flows. The network model is demonstrated by city-based crime rates reported in Montgomery County, MD, USA. A resilient network is developed utilizing shared temporal trends between cities to provide improved crime rate prediction and robustness to data loss, compared with the use of single city-based auto-regression. A maximum improvement in performance of 7.8% for Silver Spring is found and an average improvement of 5.6% among cities with high crime rates. The model also correctly identifies all the optimal network connections, according to prediction error minimization. City-to-city distance is designated as a predictor of shared temporal trends in crime and weather is shown to be a strong predictor of crime in Montgomery County.

Quantifying the stability of trace explosives under different environmental conditions using electrospray ionization mass spectrometry.

This work investigates the stability of trace (tens of nanograms) deposits of six explosives: erythritol tetranitrate (ETN), pentaerythritol tetranitrate (PETN), cyclotrimethylenetrinitramine (RDX), cyclotetramethylenetetranitramine (HMX), 2,4,6-trinitrotoluene (TNT), and 2,4,6-trinitrophenylmethylnitramine (tetryl) to determine environmental stabilities and lifetimes of trace level materials. Explosives were inkjet printed directly onto substrates and exposed to one of seven environmental conditions (Laboratory, -4°C, 30°C, 47°C, 90% relative humidity, UV light, and ozone) up to 42 days. Throughout the study, samples were extracted and quantified using electrospray ionization mass spectrometry (ESI-MS) to determine the stability of the explosive as a function of time and environmental exposure. Statistical models were then fit to the data and used for pairwise comparisons of the environments. Stability was found to be exposure and compound dependent with minimal sample losses observed for HMX, RDX, and PETN while substantial and rapid losses were observed in all conditions except -4°C for ETN and TNT and in all conditions for tetryl. The results of this work highlight the potential fate of explosive traces when exposed to various environments.

Using Replicates in Information Retrieval Evaluation.

This article explores a method for more accurately estimating the main effect of the system in a typical test-collection-based evaluation of information retrieval systems, thus increasing the sensitivity of system comparisons. Randomly partitioning the test document collection allows for multiple tests of a given system and topic (replicates). Bootstrap ANOVA can use these replicates to extract system-topic interactions-something not possible without replicates-yielding a more precise value for the system effect and a narrower confidence interval around that value. Experiments using multiple TREC collections demonstrate that removing the topic-system interactions substantially reduces the confidence intervals around the system effect as well as increases the number of significant pairwise differences found. Further, the method is robust against small changes in the number of partitions used, against variability in the documents that constitute the partitions, and the measure of effectiveness used to quantify system effectiveness.

PEPR: pipelines for evaluating prokaryotic references.

The rapid adoption of microbial whole genome sequencing in public health, clinical testing, and forensic laboratories requires the use of validated measurement processes. Well-characterized, homogeneous, and stable microbial genomic reference materials can be used to evaluate measurement processes, improving confidence in microbial whole genome sequencing results. We have developed a reproducible and transparent bioinformatics tool, PEPR, Pipelines for Evaluating Prokaryotic References, for characterizing the reference genome of prokaryotic genomic materials. PEPR evaluates the quality, purity, and homogeneity of the reference material genome, and purity of the genomic material. The quality of the genome is evaluated using high coverage paired-end sequence data; coverage, paired-end read size and direction, as well as soft-clipping rates, are used to identify mis-assemblies. The homogeneity and purity of the material relative to the reference genome are characterized by comparing base calls from replicate datasets generated using multiple sequencing technologies. Genomic purity of the material is assessed by checking for DNA contaminants. We demonstrate the tool and its output using sequencing data while developing a Staphylococcus aureus candidate genomic reference material. PEPR is open source and available at https://github.com/usnistgov/pepr .

Decision support tool use in colorectal surgery: what is the role?

BACKGROUND: Decision support tools prioritizing transitional care can help decrease medical readmissions but little evidence exists within surgical specialties. MATERIALS AND METHODS: This study evaluated the use of early screen for discharge planning and discharge decision support system screening tools or selective multidisciplinary clinical evaluation for targeting post-acute care interventions among higher risk colorectal surgery patients based on 30-d readmission status. Patients with positive screening tool scores underwent standard discharge planning education and evaluation during index operation hospitalization and were referred for targeted post-acute interventions; patients with negative screening tool scores were further clinically evaluated for selective referral for post-acute interventions. RESULTS: We identified 300 colorectal surgery patients; 30.3% (n = 91) of patients had a positive screening score (early screen for discharge planning and/or discharge decision support system). Positive screening scores did not correlate with hospital readmission (35% of readmitted patients versus 29% of non-readmitted had a positive screen; P = 0.424). After negative screening scores, selective referral based on clinical assessment for postdischarge interventions helped to concentrate resources in patients who were later readmitted. Index hospitalization complications were significantly associated with positive screening tool scores whereas postdischarge complications were most predictive of readmission. CONCLUSIONS: Among colorectal surgery patients, selective clinical referrals appeared to be the best method for targeting post-acute interventions in patients at higher risk for readmission. Future research should focus on improving existing processes of care to reduce postoperative complications and constructing better tools to assess individual patients' needs for targeted interventions in the post-acute setting.

Coexposure to phytoestrogens and bisphenol a mimics estrogenic effects in an additive manner.

Endocrine-disrupting chemicals (EDC) are abundant in our environment. A number of EDCs, including bisphenol A (BPA) can bind to the estrogen receptors (ER), ERα and ERß, and may contribute to estrogen-linked diseases such as breast cancer. Early exposure is of particular concern; many EDCs cross the placenta and infants have measurable levels of, eg, BPA. In addition, infants are frequently fed soy-based formula (SF) that contains phytoestrogens. Effects of combined exposure to xeno- and phytoestrogens are poorly studied. Here, we extensively compared to what extent BPA, genistein, and an extract of infant SF mimic estrogen-induced gene transcription and cell proliferation. We investigated ligand-specific effects on ER activation in HeLa-ERα and ERß reporter cells; on proliferation, genome-wide gene regulation and non-ER-mediated effects in MCF7 breast cancer cells; and how coexposure influenced these effects. The biological relevance was explored using enrichment analyses of differentially regulated genes and clustering with clinical breast cancer profiles. We demonstrate that coexposure to BPA and genistein, or SF, results in increased functional and transcriptional estrogenic effects. Using statistical modeling, we determine that BPA and phytoestrogens act in an additive manner. The proliferative and transcriptional effects of the tested compounds mimic those of 17ß-estradiol, and are abolished by cotreatment with an ER antagonist. Gene expression profiles induced by each compound clustered with poor prognosis breast cancer, indicating that exposure may adversely affect breast cancer prognosis. This study accentuates that coexposure to BPA and soy-based phytoestrogens results in additive estrogenic effects, and may contribute to estrogen-linked diseases, including breast cancer.

Hospital readmission by method of data collection.

BACKGROUND: Hospital readmissions are increasingly used to pay hospitals differently. We hypothesized that readmission rates, readmissions related to index admission, and potentially unnecessary readmissions vary by data collection method for surgical patients. STUDY DESIGN: Using 3 different data collection methods, we compared 30-day unplanned readmission rates and potentially unnecessary readmissions among colorectal surgery patients at a single institution between July 2009 and November 2011. We compared the NSQIP clinical reviewer method, the University HealthSystem Consortium (UHC) administrative billing data method, and physician medical record review. RESULTS: Seven hundred and thirty-five colorectal surgery patients were identified with readmission rates as follows: NSQIP 14.6% (107 of 735) vs UHC 17.6% (129 of 735). The NSQIP method identified 9 readmissions not found in billing records because the readmission occurred at another hospital (n = 7) or due to a discrepancy in definition (n = 2). The UHC method identified 31 readmissions not identified by NSQIP because of a broader readmission definition (n = 20) or were missed by reviewers (n = 11). The NSQIP method identified 72% of readmissions as related to index admission and physician chart review identified 83%. The UHC method identified 51% of readmissions as related to index admission and physician chart review identified 86%. Sixty-six of 129 UHC readmissions (51%) were deemed potentially preventable; based on physician chart review, 112 of 129 readmissions (87%) were deemed clinically necessary at the time of presentation. Most readmissions were due to surgical site infections (46 of 129 [36%]) and dehydration (30 of 129 [23%]). With improved patient-care efforts, 41 of 129 (31.8%) complications might not have required readmission. CONCLUSIONS: Readmission rates and unnecessary readmissions vary depending on data collection methodology. Reimbursements based on readmission should use standardized and fair methods to minimize perverse incentives that penalize hospitals for appropriate care of high-risk surgical patients.

Synthetic spike-in standards improve run-specific systematic error analysis for DNA and RNA sequencing.

While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a data set used to calculate association of SSEs with various features in the reads and sequence context. This data set is typically either from a part of the data set being "recalibrated" (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 Phred-scaled quality score units, and by as much as 13 units at CpG sites. In addition, since the spike-in data used for recalibration are independent of the genome being sequenced, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.

Designing microarray phantoms for hyperspectral imaging validation.

The design and fabrication of custom-tailored microarrays for use as phantoms in the characterization of hyperspectral imaging systems is described. Corresponding analysis methods for biologically relevant samples are also discussed. An image-based phantom design was used to program a microarrayer robot to print prescribed mixtures of dyes onto microscope slides. The resulting arrays were imaged by a hyperspectral imaging microscope. The shape of the spots results in significant scattering signals, which can be used to test image analysis algorithms. Separation of the scattering signals allowed elucidation of individual dye spectra. In addition, spectral fitting of the absorbance spectra of complex dye mixtures was performed in order to determine local dye concentrations. Such microarray phantoms provide a robust testing platform for comparisons of hyperspectral imaging acquisition and analysis methods.

Algorithm validation using multicolor phantoms.

We present a framework for hyperspectral image (HSI) analysis validation, specifically abundance fraction estimation based on HSI measurements of water soluble dye mixtures printed on microarray chips. In our work we focus on the performance of two algorithms, the Least Absolute Shrinkage and Selection Operator (LASSO) and the Spatial LASSO (SPLASSO). The LASSO is a well known statistical method for simultaneously performing model estimation and variable selection. In the context of estimating abundance fractions in a HSI scene, the "sparse" representations provided by the LASSO are appropriate as not every pixel will be expected to contain every endmember. The SPLASSO is a novel approach we introduce here for HSI analysis which takes the framework of the LASSO algorithm a step further and incorporates the rich spatial information which is available in HSI to further improve the estimates of abundance. In our work here we introduce the dye mixture platform as a new benchmark data set for hyperspectral biomedical image processing and show our algorithm's improvement over the standard LASSO.

LOCAL KERNEL CANONICAL CORRELATION ANALYSIS WITH APPLICATION TO VIRTUAL DRUG SCREENING.

Drug discovery is the process of identifying compounds which have potentially meaningful biological activity. A major challenge that arises is that the number of compounds to search over can be quite large, sometimes numbering in the millions, making experimental testing intractable. For this reason computational methods are employed to filter out those compounds which do not exhibit strong biological activity. This filtering step, also called virtual screening reduces the search space, allowing for the remaining compounds to be experimentally tested.In this paper we propose several novel approaches to the problem of virtual screening based on Canonical Correlation Analysis (CCA) and on a kernel-based extension. Spectral learning ideas motivate our proposed new method called Indefinite Kernel CCA (IKCCA). We show the strong performance of this approach both for a toy problem as well as using real world data with dramatic improvements in predictive accuracy of virtual screening over an existing methodology.

Experimental estimate of the abundance and effects of nearly neutral mutations in the RNA virus phi 6.

Although the frequency and effects of neutral and nearly neutral mutations are critical to evolutionary patterns and processes governed by genetic drift, the small effects of such mutations make them difficult to study empirically. Here we present the results of a mutation-accumulation experiment designed to assess the frequencies of deleterious mutations with undetectable effects. We promoted the accumulation of spontaneous mutations by subjecting independent lineages of the RNA virus 6 to repeated population bottlenecks of a single individual. We measured fitness following every bottleneck to obtain a complete picture of the timing and effects of the accumulated mutations with detectable effects and sequenced complete genomes to determine the number of mutations that were undetected by the fitness assays. To estimate the effects of the undetected mutations, we implemented a likelihood model developed for quantitative trait locus (QTL) data (Otto and Jones 2000) to estimate the number and effects of the undetected mutations from the measured number and effects of the detected mutations. Using this method we estimated a deleterious mutation rate of U = 0.03 and a gamma effects distribution with mean s=0.093 and coefficient of variation = 0.204. Although our estimates of U and s fall within the range of recent mutation rate and effect estimates in eukaryotes, the fraction of mutations with detectable effects on laboratory fitness (39%) appears to be far higher in 6 than in eukaryotes.