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

Polypharmacy in HIV: recent insights and future directions.

PURPOSE OF REVIEW: Update findings regarding polypharmacy among people with HIV (PWH) and consider what research is most needed. RECENT FINDINGS: Among PWH, polypharmacy is common, occurs in middle age, and is predominantly driven by nonantiretroviral (ARV) medications. Many studies have demonstrated strong associations between polypharmacy and receipt of potentially inappropriate medications (PIMS), but few have considered actual adverse events. Falls, delirium, pneumonia, hospitalization, and mortality are associated with polypharmacy among PWH and risks remain after adjustment for severity of illness. SUMMARY: Polypharmacy is a growing problem and mechanisms of injury likely include potentially inappropriate medications, total drug burden, known pairwise drug interactions, higher level drug interactions, drug--gene interactions, and drug--substance use interactions (alcohol, extra-medical prescription medication, and drug use). Before we can effectively design interventions, we need to use observational data to gain a better understanding of the modifiable mechanisms of injury. As sicker individuals take more medications, analyses must account for severity of illness. As self-report of substance use may be inaccurate, direct biomarkers, such as phosphatidylethanol (PEth) for alcohol are needed. Large samples including electronic health records, genetics, accurate measures of substance use, and state of the art statistical and artificial intelligence techniques are needed to advance our understanding and inform clinical management of polypharmacy in PWH

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

Pharmacogenomics driven decision support prototype with machine learning: A framework for improving patient care

Introduction: A growing number of healthcare providers make complex treatment decisions guided by electronic health record (EHR) software interfaces. Many interfaces integrate multiple sources of data (e.g., labs, pharmacy, diagnoses) successfully, though relatively few have incorporated genetic data. Method: This study utilizes informatics methods with predictive modeling to create and validate algorithms to enable informed pharmacogenomic decision-making at the point of care in near real-time. The proposed framework integrates EHR and genetic data relevant to the patient's current medications including decision support mechanisms based on predictive modeling. We created a prototype with EHR and linked genetic data from the Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. The EHR data included diagnoses, medication fills, and outpatient clinic visits for 2,600 people with HIV and matched uninfected controls linked to prototypic genetic data (variations in single or multiple positions in the DNA sequence). We then mapped the medications that patients were prescribed to medications defined in the drug-gene interaction mapping of the Clinical Pharmacogenomics Implementation Consortium's (CPIC) level A (i.e., sufficient evidence for at least one prescribing action) guidelines that predict adverse events. CPIC is a National Institute of Health funded group of experts who develop evidence based pharmacogenomic guidelines. Preventable adverse events (PAE) can be defined as a harmful outcome from an intervention that could have been prevented. For this study, we focused on potential PAEs resulting from a medication-gene interaction. Results: The final model showed AUC scores of 0.972 with an F1 score of 0.97 with genetic data as compared to 0.766 and 0.73 respectively, without genetic data integration. Discussion: Over 98% of people in the cohort were on at least one medication with CPIC level a guideline in their lifetime. We compared predictive power of machine learning models to detect a PAE between five modeling methods: Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), K Nearest neighbors (KNN), and Decision Tree. We found that XGBoost performed best for the prototype when genetic data was added to the framework and improved prediction of PAE. We compared area under the curve (AUC) between the models in the testing dataset

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

Genome-Wide Association Study of Smoking Trajectory and Meta-Analysis of Smoking Status in 842,000 Individuals

Here we report a large genome-wide association study (GWAS) for longitudinal smoking phenotypes in 286,118 individuals from the Million Veteran Program (MVP) where we identified 18 loci for smoking trajectory of current versus never in European Americans, one locus in African Americans, and one in Hispanic Americans. Functional annotations prioritized several dozen genes where significant loci co-localized with either expression quantitative trait loci or chromatin interactions. The smoking trajectories were genetically correlated with 209 complex traits, for 33 of which smoking was either a causal or a consequential factor. We also performed European-ancestry meta-analyses for smoking status in the MVP and GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN) (Ntotal = 842,717) and identified 99 loci for smoking initiation and 13 loci for smoking cessation. Overall, this large GWAS of longitudinal smoking phenotype in multiple populations, combined with a meta-GWAS for smoking status, adds new insights into the genetic vulnerability for smoking behavior

Geographic and temporal variation in racial and ethnic disparities in SARS-CoV-2 positivity between February 2020 and August 2021 in the United States.

The coronavirus pandemic has disproportionally impacted racial and ethnic minority communities in the United States. Patterns of these disparities may be changing over time as outbreaks occur in different communities. Utilizing electronic health record data from the US Department of Veterans Affairs (VA), we estimated odds ratios, stratified by time period and region, for testing positive among 1,313,402 individuals tested for SARS-CoV-2 between February 12, 2020 and August 16, 2021 at VA medical facilities. We adjusted for personal characteristics (sex, age, rural/urban residence, VA facility) and a wide range of clinical characteristics that have been evaluated in prior SARS-CoV-2 reports and could potentially explain racial/ethnic disparities in SARS-CoV-2. Our study found racial and ethnic disparities for testing positive were most pronounced at the beginning of the pandemic and decreased over time. A key finding was that the disparity among Hispanic individuals attenuated but remained elevated, while disparities among Asian individuals reversed by March 1, 2021. The variation in racial and ethnic disparities in SARS-CoV-2 positivity by time and region, independent of underlying health status and other demographic characteristics in a nationwide cohort, provides important insight for strategies to prevent further outbreaks

Life-Expectancy Disparities Among Adults With HIV in the United States and Canada: The Impact of a Reduction in Drug- and Alcohol-Related Deaths Using the Lives Saved Simulation Model.

Improvements in life expectancy among people living with human immunodeficiency virus (PLWH) receiving antiretroviral treatment in the United States and Canada might differ among key populations. Given the difference in substance use among key populations and the current opioid epidemic, drug- and alcohol-related deaths might be contributing to the disparities in life expectancy. We sought to estimate life expectancy at age 20 years in key populations (and their comparison groups) in 3 time periods (2004-2007, 2008-2011, and 2012-2015) and the potential increase in expected life expectancy with a simulated 20% reduction in drug- and alcohol-related deaths using the novel Lives Saved Simulation model. Among 92,289 PLWH, life expectancy increased in all key populations and comparison groups from 2004-2007 to 2012-2015. Disparities in survival of approximately a decade persisted among black versus white men who have sex with men and people with (vs. without) a history of injection drug use. A 20% reduction in drug- and alcohol-related mortality would have the greatest life-expectancy benefit for black men who have sex with men, white women, and people with a history of injection drug use. Our findings suggest that preventing drug- and alcohol-related deaths among PLWH could narrow disparities in life expectancy among some key populations, but other causes of death must be addressed to further narrow the disparities

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