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%

Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated HIV/Hepatitis C Virus-Coinfected Patients

Background. End-stage liver disease (ESLD) is an important cause of morbidity among HIV/hepatitis C virus (HCV)-coinfected patients. Quantifying the risk of this outcome over time could help determine which coinfected patients should be targeted for risk factor modification and HCV treatment. We evaluated demographic, clinical, and laboratory variables to predict risk of ESLD in HIV/HCV-coinfected patients receiving antiretroviral therapy (ART). Methods. We conducted a retrospective cohort study among 6,016 HIV/HCV-coinfected patients who received ART within the Veterans Health Administration between 1997 and 2010. The main outcome was incident ESLD, defined by hepatic decompensation, hepatocellular carcinoma, or liver-related death. Cox regression was used to develop prognostic models based on baseline demographic, clinical, and laboratory variables, including FIB-4 and aspartate aminotransferase-to-platelet ratio index, previously validated markers of hepatic fibrosis. Model performance was assessed by discrimination and decision curve analysis. Results. Among 6,016 HIV/HCV patients, 532 (8.8%) developed ESLD over a median of 6.6 years. A model comprising FIB-4 and race had modest discrimination for ESLD (c-statistic, 0.73) and higher net benefit than alternative strategies of treating no or all coinfected patients at relevant risk thresholds. For FIB-4 \u3e3.25, ESLD risk ranged from 7.9% at 1 year to 26.0% at 5 years among non-blacks and from 2.4% at 1 year to 14.0% at 5 years among blacks. Conclusions. Race and FIB-4 provided important predictive information on ESLD risk among HIV/HCV patients. Estimating risk of ESLD using these variables could help direct HCV treatment decisions among HIV/HCV-coinfected patients

Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios as Prognostic Inflammatory Biomarkers in Human Immunodeficiency Virus (HIV), Hepatitis C Virus (HCV), and HIV/HCV Coinfection.

Background:Inflammation in human immunodeficiency virus (HIV)-infected patients is associated with poorer health outcomes. Whether inflammation as measured by the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) adds information to existing prognostic indices is not known. Methods:We analyzed data from 2000 to 2012 in the Veterans Aging Cohort Study (VACS), overall and stratified by HIV/hepatitis C virus status (n = 89 786). We randomly selected a visit date at which all laboratory values of interest were available within 180 days; participants with HIV received at least 1 year of antiretroviral therapy. We followed patients for (1) mortality and (2) hepatic decompensation (HD) and analyzed associations using Cox regression, adjusted for a validated mortality risk index (VACS Index 2.0). In VACS Biomarker Cohort, we considered correlation with biomarkers of inflammation: interleukin-6, D-dimer, and soluble CD-14. Results:Neutrophil-to-lymphocyte ratio and PLR demonstrated strong unadjusted associations with mortality (P &lt; .0001) and HD (P &lt; .0001) and were weakly correlated with other inflammatory biomarkers. Although NLR remained statistically independent for mortality, as did PLR for HD, the addition of NLR and PLR to the VACS Index 2.0 did not result in significant improvement in discrimination compared with VACS Index 2.0 alone for mortality (C-statistic 0.767 vs 0.758) or for HD (C-statistic 0.805 vs 0.801). Conclusions:Neutrophil-to-lymphocyte ratio and PLR were strongly associated with mortality and HD and weakly correlated with inflammatory biomarkers. However, most of their association was explained by VACS Index 2.0. Addition of NLR and PLR to VACS 2.0 did not substantially improve discrimination for either outcome

Cancer incidence in HIV-infected versus uninfected veterans: Comparison of cancer registry and ICD-9 code diagnoses

Background: Given the growing interest in the cancer burden in persons living with HIV/AIDS, we examined the validity of data sources for cancer diagnoses (cancer registry versus International Classification of Diseases, Ninth Revision [ICD-9 codes]) and compared the association between HIV status and cancer risk using each data source in the Veterans Aging Cohort Study (VACS), a prospective cohort of HIV-infected and uninfected veterans from 1996 to 2008. Methods: We reviewed charts to confirm potential incident cancers at four VACS sites. In the entire cohort, we calculated cancer-type-specific age-, sex-, race/ethnicity-, and calendar-period-standardized incidence rates and incidence rate ratios (IRR) (HIV-infected versus uninfected). We calculated standardized incidence ratios (SIR) to compare VACS and Surveillance, Epidemiology, and End Results rates. Results: Compared to chart review, both Veterans Affairs Central Cancer Registry (VACCR) and ICD-9 diagnoses had approximately 90% sensitivity; however, VACCR had higher positive predictive value (96% versus 63%). There were 6,010 VACCR and 13,386 ICD-9 incident cancers among 116,072 veterans. Although ICD-9 rates tended to be double VACCR rates, most IRRs were in the same direction and of similar magnitude, regardless of data source. Using source, all cancers combined, most viral-infection-related cancers, lung cancer, melanoma, and leukemia had significantly elevated IRRs. Using ICD-9, eight additional IRRs were significantly elevated, most likely due to false positive diagnoses. Most ICD-9 SIRs were significantly elevated and all were higher than the corresponding VACCR SIR. Conclusions: ICD-9 may be used with caution for estimating IRRs, but should be avoided when estimating incidence or SIRs. Elevated cancer risk based on VACCR diagnoses among HIV-infected veterans was consistent with other studies

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