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.

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 .

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.

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.