Coping Resources, Perceived Stress, and Life Experiences in Individuals with Aphasia

Individuals with certain forms of aphasia may be under considerable stress related to their linguistic skills. The current study explored coping resources, perceived stress, and life experiences in individuals with aphasia. The relation of these factors to salivary cortisol, a physiologic index of stress, was additionally investigated. Results indicate individuals with aphasia have fewer coping resources and greater perceived stress than healthy controls. Salivary cortisol was not related to perceived stress or coping resources, but was related to life experiences during the previous six months. Clinical and theoretical implications are discussed

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BackgroundPatient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.ObjectivesWe sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites.MethodsWe compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites.ResultsSeveral attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001).ConclusionsOverall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes.

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19

The handbook for standardized field and laboratory measurements in terrestrial climate change experiments and observational studies (ClimEx)

1. Climate change is a world‐wide threat to biodiversity and ecosystem structure, functioning and services. To understand the underlying drivers and mechanisms, and to predict the consequences for nature and people, we urgently need better understanding of the direction and magnitude of climate change impacts across the soil–plant–atmosphere continuum. An increasing number of climate change studies are creating new opportunities for meaningful and high‐quality generalizations and improved process understanding. However, significant challenges exist related to data availability and/or compatibility across studies, compromising opportunities for data re‐use, synthesis and upscaling. Many of these challenges relate to a lack of an established ‘best practice’ for measuring key impacts and responses. This restrains our current understanding of complex processes and mechanisms in terrestrial ecosystems related to climate change. 2. To overcome these challenges, we collected best‐practice methods emerging from major ecological research networks and experiments, as synthesized by 115 experts from across a wide range of scientific disciplines. Our handbook contains guidance on the selection of response variables for different purposes, protocols for standardized measurements of 66 such response variables and advice on data management. Specifically, we recommend a minimum subset of variables that should be collected in all climate change studies to allow data re‐use and synthesis, and give guidance on additional variables critical for different types of synthesis and upscaling. The goal of this community effort is to facilitate awareness of the importance and broader application of standardized methods to promote data re‐use, availability, compatibility and transparency. We envision improved research practices that will increase returns on investments in individual research projects, facilitate second‐order research outputs and create opportunities for collaboration across scientific communities. Ultimately, this should significantly improve the quality and impact of the science, which is required to fulfil society's needs in a changing world

Development of a national Department of Veterans Affairs mortality risk prediction model among patients with cirrhosis.

OBJECTIVE: Cirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models. DESIGN: We analysed a retrospective cohort of 73 976 patients comprising 247 650 hospitalisations between 2006 and 2013 at any of 123 Department of Veterans Affairs hospitals. Using 45 predictor variables, we built a time-dependent Cox proportional hazards model with all-cause mortality as the outcome. We compared performance to the three extant models and reported discrimination and calibration using bootstrapping. Furthermore, we analysed differential utility using the net reclassification index (NRI). RESULTS: The C-statistic for the final model was 0.863, representing a significant improvement over the MELD, MELD-Na, and the CLIF-C AD, which had C-statistics of 0.655, 0.675, and 0.679, respectively. Multiple risk factors were significant in our model, including variables reflecting disease severity and haemodynamic compromise. The NRI showed a 24% improvement in predicting survival of low-risk patients and a 30% improvement in predicting death of high-risk patients. CONCLUSION: We developed a more accurate mortality risk prediction score using variables automatically extractable from an EHR that may be used to risk stratify patients with cirrhosis for targeted postdischarge management

Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis

BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination

Compromised nonsense-mediated RNA decay results in truncated RNA-binding protein production upon DUX4 expression

Nonsense-mediated RNA decay (NMD) degrades transcripts carrying premature termination codons. NMD is thought to prevent the synthesis of toxic truncated proteins. However, whether loss of NMD results in widespread production of truncated proteins is unclear. A human genetic disease, facioscapulohumeral muscular dystrophy (FSHD), features acute inhibition of NMD upon expression of the disease-causing transcription factor, DUX4. Using a cell-based model of FSHD, we show production of truncated proteins from physiological NMD targets and find that RNA-binding proteins are enriched for aberrant truncations. The NMD isoform of one RNA-binding protein, SRSF3, is translated to produce a stable truncated protein, which is detected in FSHD patient-derived myotubes. Ectopic expression of truncated SRSF3 confers toxicity, and its downregulation is cytoprotective. Our results delineate the genome-scale impact of NMD loss. This widespread production of potentially deleterious truncated proteins has implications for FSHD biology as well as other genetic diseases where NMD is therapeutically modulated

Piperidinyl Ureas Chemically Control Defective in Cullin Neddylation 1 (DCN1)-Mediated Cullin Neddylation

We previously discovered and validated a class of piperidinyl ureas that regulate defective in cullin neddylation 1 (DCN1)-dependent neddylation of cullins. Here, we report preliminary structure–activity relationship studies aimed at advancing our high-throughput screen hit into a tractable tool compound for dissecting the effects of acute DCN1–UBE2M inhibition on the NEDD8/cullin pathway. Structure-enabled optimization led to a 100-fold increase in biochemical potency and modestly increased solubility and permeability as compared to our initial hit. The optimized compounds inhibit the DCN1–UBE2M protein–protein interaction in our TR-FRET binding assay and inhibit cullin neddylation in our pulse-chase NEDD8 transfer assay. The optimized compounds bind to DCN1 and selectively reduce steady-state levels of neddylated CUL1 and CUL3 in a squamous cell carcinoma cell line. Ultimately, we anticipate that these studies will identify early lead compounds for clinical development for the treatment of lung squamous cell carcinomas and other cancers

Piperidinyl Ureas Chemically Control Defective in Cullin Neddylation 1 (DCN1)-Mediated Cullin Neddylation

We previously discovered and validated a class of piperidinyl ureas that regulate defective in cullin neddylation 1 (DCN1)-dependent neddylation of cullins. Here, we report preliminary structure–activity relationship studies aimed at advancing our high-throughput screen hit into a tractable tool compound for dissecting the effects of acute DCN1–UBE2M inhibition on the NEDD8/cullin pathway. Structure-enabled optimization led to a 100-fold increase in biochemical potency and modestly increased solubility and permeability as compared to our initial hit. The optimized compounds inhibit the DCN1–UBE2M protein–protein interaction in our TR-FRET binding assay and inhibit cullin neddylation in our pulse-chase NEDD8 transfer assay. The optimized compounds bind to DCN1 and selectively reduce steady-state levels of neddylated CUL1 and CUL3 in a squamous cell carcinoma cell line. Ultimately, we anticipate that these studies will identify early lead compounds for clinical development for the treatment of lung squamous cell carcinomas and other cancers