Association Between Gabapentin Receipt for Any Indication and Alcohol Use Disorders Identification Test-Consumption Scores Among Clinical Subpopulations With and Without Alcohol Use Disorder.

BACKGROUND: Current medications for alcohol use disorder (AUD) have limited efficacy and utilization. Some clinical trials have shown efficacy for gabapentin among treatment-seeking individuals. The impact of gabapentin on alcohol consumption in a more general sample remains unknown. METHODS: We identified patients prescribed gabapentin for ≥180 consecutive days for any clinical indication other than substance use treatment between 2009 and 2015 in the Veterans Aging Cohort Study. We propensity-score matched each gabapentin-exposed patient with up to 5 unexposed patients. Multivariable difference-in-difference (DiD) linear regression models estimated the differential change in Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores during follow-up between exposed and unexposed patients, by baseline level of alcohol consumption and daily gabapentin dose. Analyses were stratified by AUD history. Clinically meaningful changes were a priori considered a DiD ≥1 point. RESULTS: Among patients with AUD, AUDIT-C scores decreased 0.39 points (95% confidence interval [CI] 0.05, 0.73) more among exposed than unexposed patients (p < 0.03). Potentially clinically meaningful differences were observed among those with AUD and exposed to ≥1,500 mg/d (DiD 0.77, 95% CI 0.15, 1.38, p < 0.02). No statistically significant effects were found among patients with AUD at doses lower than 1,500 mg/d or baseline AUDIT-C ≥4. Among patients without AUD, we found no overall difference in changes in AUDIT-C scores, nor in analyses stratified by baseline level of alcohol consumption. CONCLUSIONS: Patients exposed to doses of gabapentin consistent with those used in clinical trials, particularly those with AUD, experienced a greater decrease in AUDIT-C scores than matched unexposed patients

High electron mobility W-doped In₂O₃ thin films by pulsed laser deposition

High electron mobility thin films of In₂₋ₓWₓO₃₊y(0≤x≤0.075) were prepared on amorphous SiO₂ and single-crystal yttria-stablized zirconia (001) substrates by pulsed laser deposition. Mobilities ranged between 66 and 112 cm² /Vs depending on the substrate type and deposition conditions, and the highest mobility was observed at a W-dopant concentration of x~0.03. A small band gap shift was detected from films with increasing electron carrier density; the electron effective mass calculated from Burstein-Moss theory was 0.3mₑ. In₂₋ₓWₓO₃₊y films have high visible transmittance of ~80%

Safety of Gabapentin Prescribed for Any Indication in a Large Clinical Cohort of 571,718 US Veterans with and without Alcohol Use Disorder.

BACKGROUND: Gabapentin is prescribed for seizures and pain and has efficacy for treating alcohol use disorder (AUD) starting at doses of 900 milligrams per day (mg/d). Recent evidence suggests safety concerns associated with gabapentin including adverse neurologic effects. Individuals with hepatitis C (HCV), HIV, or AUD may be at increased risk due to comorbidities and potential medication interactions. METHODS: We identified patients prescribed gabapentin for ? 60 days for any indication between 2002 and 2015. We propensity-score matched each gabapentin-exposed patient with up to 5 gabapentin-unexposed patients. We followed patients for 2 years or until diagnosed with (i) falls or fractures, or (ii) altered mental status using validated ICD-9 diagnostic codes. We used Poisson regression to estimate incidence rates and relative risk (RR) of these adverse events in association with gabapentin exposure overall and stratified by age, race/ethnicity, sex, HCV, HIV, AUD, and dose. RESULTS: Incidence of falls or fractures was 1.81 per 100 person-years (PY) among 140,310 gabapentin-exposed and 1.34/100 PY among 431,408 gabapentin-unexposed patients (RR 1.35, 95% confidence interval [CI] 1.28 to 1.44). Incidence of altered mental status was 1.08/100 PY among exposed and 0.97/100 PY among unexposed patients, RR of 1.12 (95% CI 1.04 to 1.20). Excess risk of falls or fractures associated with gabapentin exposure was observed in all subgroups except patients with HCV, HIV, or AUD; however, these groups had elevated incidence regardless of exposure. There was a clear dose-response relationship for falls or fractures with highest risk observed among those prescribed ? 2,400 mg/d (RR 1.90, 95% CI 1.50 to 2.40). Patients were at increased risk for altered mental status at doses 600 to 2,399 mg/d; however, low number of events in the highest dose category limited power to detect a statistically significant association ? 2,400 mg/d. CONCLUSIONS: Gabapentin is associated with falls or fractures and altered mental status. Clinicians should be monitoring gabapentin safety, especially at doses ? 600 mg/d, in patients with and without AUD

Origin of p-type conduction in single-crystal CuAlO₂

We report measurements of the structural, optical, transport, and magnetic properties of single crystals of the anisotropic p-type transparent semiconductor CuAlO₂. The indirect and direct band gaps are 2.97 and 3.47 eV, respectively. Temperature-dependent Hall measurements yield a positive Hall coefficient in the measured range and an activated carrier temperature dependence. The resistivity is anisotropic, with the ab-plane resistivity about 25 times smaller than the c-axis resistivity at room temperature. Both are activated with similar activation energies. The room-temperature ab-plane mobility is relatively large at 3 cm² V⁻¹ s⁻¹, and we infer a c-axis mobility of 0.12 cm² V⁻¹ s⁻¹. The Seebeck coefficient is positive at all measured temperatures, and has a T⁻¹ dependence over most of the measured range. The low-temperature paramagnetic moment is consistent with a spin-1/2 defect with a density of 3.4 X 10²⁰ cm⁻³. These results suggest that the conduction mechanism for p-type carriers in CuAlO₂ is charge transport in the valence band and that the holes are thermally activated from copper-vacancy acceptor states located about 700 meV above the valence-band maximum

Survival analysis of localized prostate cancer with deep learning.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system

Development and validation of a 30-day mortality index based on pre-existing medical administrative data from 13,323 COVID-19 patients: The Veterans Health Administration COVID-19 (VACO) Index.

BACKGROUND: Available COVID-19 mortality indices are limited to acute inpatient data. Using nationwide medical administrative data available prior to SARS-CoV-2 infection from the US Veterans Health Administration (VA), we developed the VA COVID-19 (VACO) 30-day mortality index and validated the index in two independent, prospective samples. METHODS AND FINDINGS: We reviewed SARS-CoV-2 testing results within the VA between February 8 and August 18, 2020. The sample was split into a development cohort (test positive between March 2 and April 15, 2020), an early validation cohort (test positive between April 16 and May 18, 2020), and a late validation cohort (test positive between May 19 and July 19, 2020). Our logistic regression model in the development cohort considered demographics (age, sex, race/ethnicity), and pre-existing medical conditions and the Charlson Comorbidity Index (CCI) derived from ICD-10 diagnosis codes. Weights were fixed to create the VACO Index that was then validated by comparing area under receiver operating characteristic curves (AUC) in the early and late validation cohorts and among important validation cohort subgroups defined by sex, race/ethnicity, and geographic region. We also evaluated calibration curves and the range of predictions generated within age categories. 13,323 individuals tested positive for SARS-CoV-2 (median age: 63 years; 91% male; 42% non-Hispanic Black). We observed 480/3,681 (13%) deaths in development, 253/2,151 (12%) deaths in the early validation cohort, and 403/7,491 (5%) deaths in the late validation cohort. Age, multimorbidity described with CCI, and a history of myocardial infarction or peripheral vascular disease were independently associated with mortality-no other individual comorbid diagnosis provided additional information. The VACO Index discriminated mortality in development (AUC = 0.79, 95% CI: 0.77-0.81), and in early (AUC = 0.81 95% CI: 0.78-0.83) and late (AUC = 0.84, 95% CI: 0.78-0.86) validation. The VACO Index allows personalized estimates of 30-day mortality after COVID-19 infection. For example, among those aged 60-64 years, overall mortality was estimated at 9% (95% CI: 6-11%). The Index further discriminated risk in this age stratum from 4% (95% CI: 3-7%) to 21% (95% CI: 12-31%), depending on sex and comorbid disease. CONCLUSION: Prior to infection, demographics and comorbid conditions can discriminate COVID-19 mortality risk overall and within age strata. The VACO Index reproducibly identified individuals at substantial risk of COVID-19 mortality who might consider continuing social distancing, despite relaxed state and local guidelines

Covid-19 Testing, Hospital Admission, and Intensive Care Among 2,026,227 United States Veterans Aged 54-75 Years.

IMPORTANCE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes coronavirus disease 2019 (Covid-19), an evolving pandemic. Limited data are available characterizing SARS-Cov-2 infection in the United States. OBJECTIVE: To determine associations between demographic and clinical factors and testing positive for coronavirus 2019 (Covid-19+), and among Covid-19+ subsequent hospitalization and intensive care. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study including all patients tested for Covid-19 between February 8 and March 30, 2020, inclusive. We extracted electronic health record data from the national Veterans Affairs Healthcare System, the largest integrated healthcare system in the United States, on 2,026,227 patients born between 1945 and 1965 and active in care. Exposures: Demographic data, comorbidities, medication history, substance use, vital signs, and laboratory measures. Laboratory tests were analyzed first individually and then grouped into a validated summary measure of physiologic injury (VACS Index). Main Outcomes and Measures: We evaluated which factors were associated with Covid-19+ among all who tested. Among Covid-19+ we identified factors associated with hospitalization or intensive care. We identified independent associations using multivariable and conditional multivariable logistic regression with multiple imputation of missing values. RESULTS: Among Veterans aged 54-75 years, 585/3,789 (15.4%) tested Covid-19+. In adjusted analysis (C-statistic=0.806) black race was associated with Covid-19+ (OR 4.68, 95% CI 3.79-5.78) and the association remained in analyses conditional on site (OR 2.56, 95% CI 1.89-3.46). In adjusted models, laboratory abnormalities (especially fibrosis-4 score [FIB-4] >3.25 OR 8.73, 95% CI 4.11-18.56), and VACS Index (per 5-point increase OR 1.62, 95% CI 1.43-1.84) were strongly associated with hospitalization. Associations were similar for intensive care. Although significant in unadjusted analyses, associations with comorbid conditions and medications were substantially reduced and, in most cases, no longer significant after adjustment. CONCLUSIONS AND RELEVANCE: Black race was strongly associated with Covid-19+, but not with hospitalization or intensive care. Among Covid-19+, risk of hospitalization and intensive care may be better characterized by laboratory measures and vital signs than by comorbid conditions or prior medication exposure