High Hospitalization Rates and Risk Factors Among Frail Patients With Cirrhosis: A 10-year Population-based Cohort Study.

Background & Aims: Cirrhosis-related inpatient hospitalizations have increased dramatically over the past decade. We used a longitudinal dataset capturing a large metropolitan area in the United States from 2011 to 2021 to evaluate contemporary hospitalization rates and risk factors among frail patients with cirrhosis.

Methods: We conducted a retrospective, longitudinal cohort study using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) database, an electronic health record repository that aggregates de-duplicated data across 7 health care systems in the Chicago metropolitan area, from 2011 to 2021.

Hospital variation in adoption of balanced transfusion practices among injured patients requiring blood transfusions.

Background: This study sought to measure hospital variability in adoption of balanced transfusion following the Pragmatic, Randomized Optimal Platelet and Plasma Ratios (PROPPR) guidelines. We hypothesized hospital adoption rates of balanced transfusion would be low, and vary significantly among hospitals after controlling for patient, injury and hospital characteristics.

Study Design And Methods: This was an observational cohort study of injured adult patients (≥16 years) in Trauma Quality Improvement Program hospitals 2016-2021.

PREDICTION OF TIME TO HEMODYNAMIC STABILIZATION OF UNSTABLE INJURED PATIENT ENCOUNTERS USING ELECTRONIC MEDICAL RECORD DATA.

Background : This study sought to predict time to patient hemodynamic stabilization during trauma resuscitations of hypotensive patient encounters using electronic medical record (EMR) data. Methods: This observational cohort study leveraged EMR data from a nine-hospital academic system composed of Level I, Level II, and nontrauma centers. Injured, hemodynamically unstable (initial systolic blood pressure, <90 mm Hg) emergency encounters from 2015 to 2020 were identified.

Prediction of intra-abdominal injury using natural language processing of electronic medical record data.

Background: This study aimed to use natural language processing to predict the presence of intra-abdominal injury using unstructured data from electronic medical records.

Methods: This was a random-sample retrospective observational cohort study leveraging unstructured data from injured patients taken to one of 9 acute care hospitals in an integrated health system between 2015 and 2021. Patients with International Classification of Diseases External Cause of Morbidity codes were identified.

Machine learning identification of patient clusters for cutaneous melanoma.

Background: Cutaneous melanoma is a cancer arising in melanocyte skin cells and is the deadliest form of skin cancer worldwide. Although some risk factors are known, accurate prediction of disease progression and probability for metastasis are difficult to ascertain, given the complexity of the disease and the absence of reliable predictive markers. Since early detection and treatment are essential to enhance survival, this study utilizing machine learning (ML) aims to further delineate additional risk factors associated with cutaneous melanoma.

Re-triage moderates association between state trauma funding and lower mortality of trauma patients.

Background: Severely injured patients who are re-triaged (emergently transferred from an emergency department to a high-level trauma center) experience lower in-hospital mortality. Patients in states with trauma funding also experience lower in-hospital mortality. This study examines the interaction of re-triage, state trauma funding, and in-hospital mortality.

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use.

Divergent Trends in Emergency Department Presentations Amid the Novel Coronavirus Disease 2019 Pandemic in Chicago, Illinois.

Excess deaths during the coronavirus disease 2019 (COVID-19) pandemic have been largely attributed to cardiovascular disease (CVD); however, patterns in CVD hospitalizations after the first surge of the pandemic have not well-documented. Our brief report, examining trends in health care avoidance documents that CVD hospitalizations decreased in Chicago before significant burden of COVID-19 cases or deaths and normalized during the first COVID-19 surge. These data may help to inform health care systems responses in the coming months while mobilizing vaccinations to the population at large.

Extracting social determinants of health from electronic health records using natural language processing: a systematic review.

Objective: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes.

The effect of number of healthcare visits on study sample selection in electronic health record data.

Introduction: Few studies have addressed how to select a study sample when using electronic health record (EHR) data.

Objective: To examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence.

Methods: Year 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence.

A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.

Background: Testing for COVID-19 remains limited in the United States and across the world. Poor allocation of limited testing resources leads to misutilization of health system resources, which complementary rapid testing tools could ameliorate.

Objective: To predict SARS-CoV-2 PCR positivity based on complete blood count components and patient sex.

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees.

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations.

Prediction of seebeck coefficient for compounds without restriction to fixed stoichiometry: A machine learning approach.

The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in the temperature range from 300 K to 1000 K. The tool accounts for the single crystal versus polycrystalline nature of the compound, the production method, and properties of the constituent elements in the chemical formula. We introduce new descriptive features of crystalline materials relevant for the prediction the Seebeck coefficient.

Molecularly Tunable Fluorescent Quantum Defects.

We describe the chemical creation of molecularly tunable fluorescent quantum defects in semiconducting carbon nanotubes through covalently bonded surface functional groups that are themselves nonemitting. By variation of the surface functional groups, the same carbon nanotube crystal is chemically converted to create more than 30 distinct fluorescent nanostructures with unique near-infrared photoluminescence that is molecularly specific, systematically tunable, and significantly brighter than that of the parent semiconductor. This novel exciton-tailoring chemistry readily occurs in aqueous solution and creates functional defects on the sp(2) carbon lattice with highly predictable C-C bonding from virtually any iodine-containing hydrocarbon precursor.

Molecular-Level Engineering of Adhesion in Carbon Nanomaterial Interfaces.

Weak interfilament van der Waals interactions are potentially a significant roadblock in the development of carbon nanotube- (CNT-) and graphene-based nanocomposites. Chemical functionalization is envisioned as a means of introducing stronger intermolecular interactions at nanoscale interfaces, which in turn could enhance composite strength. This paper reports measurements of the adhesive energy of CNT-graphite interfaces functionalized with various coverages of arylpropionic acid.

Shear and friction between carbon nanotubes in bundles and yarns.

We perform a detailed density functional theory assessment of the factors that determine shear interactions between carbon nanotubes (CNTs) within bundles and in related CNT and graphene structures including yarns, providing an explanation for the shear force measured in recent experiments (Filleter, T. etal. Nano Lett.

Electron induced single strand break and cyclization: a DFT study on the radiosensitization mechanism of the nucleotide of 8-bromoguanine.

Cleavage of the O-P bond in 8-bromo-2'-deoxyguanosine-3',5'-diphosphate (BrdGDP), considered as a model of single strand break (SSB) in labelled double-stranded DNA (ds DNA), is investigated at the B3LYP/6-31++G(d,p) level. The thermodynamic and kinetic characteristics of the formation of SSB are compared to those related to the 5',8-cycloguanosine lesion. The first reaction step, common to both damage types, which is the formation of the reactive guanyl radical, proceeds with a barrier-free or low-barrier release of the bromide anion.

In situ scanning electron microscope peeling to quantify surface energy between multiwalled carbon nanotubes and graphene.

Understanding atomic interactions between constituents is critical to the design of high-performance nanocomposites. Here, we report an experimental-computational approach to investigate the adhesion energy between as-produced arc discharge multiwalled carbon nanotubes (MWCNTs) and graphene. An in situ scanning electron microscope (SEM) experiment is used to peel MWCNTs from graphene grown on copper foils.

Novel view on the mechanism of water-assisted proton transfer in the DNA bases: bulk water hydration.

In the present work, the conventional static ab initio picture of a water-assisted mechanism of the tautomerization of Nucleic Acid Bases (NABs) in an aqueous environment is enhanced by the classical and Car-Parrinello molecular dynamics simulations. The inclusion of the dynamical contribution is vital because the formation and longevity of the NAB-water bridge complexes represent decisive factors for further tautomerization. The results of both molecular dynamic techniques indicate that the longest time when such complexes exist is significantly shorter than the time required for proton transfer suggested by the static ab initio level of theory.