Does smoking cessation reduce other substance use, psychiatric symptoms, and pain symptoms? Results from an emulated hypothetical randomized trial of US veterans.

BACKGROUND: Quitting smoking may lead to improvement in substance use, psychiatric symptoms, and pain, especially among high-risk populations who are more likely to experience comorbid conditions. However, causal inferences regarding smoking cessation and its subsequent benefits have been limited. METHODS: We emulated a hypothetical open-label randomized control trial of smoking cessation using longitudinal observational data of HIV-positive and HIV-negative US veterans from 2003-2015 in the Veterans Aging Cohort Study. We followed individuals from the first time they self-reported current cigarette smoking (baseline). We categorized participants as quitters or non-quitters at the first follow-up visit (approximately 1 year after baseline). Using inverse probability weighting to adjust for confounding and selection bias, we estimated odds ratios for improvement of co-occurring conditions (unhealthy alcohol use, cannabis use, illicit opioid use, cocaine use, depressive symptoms, anxiety symptoms, and pain symptoms) at second follow-up (approximately 2 years after baseline) for those who quit smoking compared to those who did not, among individuals who had the condition at baseline. RESULTS: Of 4,165 eligible individuals (i.e., current smokers at baseline), 419 reported no current smoking and 2,330 reported current smoking at the first follow-up. Adjusted odds ratios (95% confidence intervals) for associations between quitting smoking and improvement of each condition at second follow-up were: 2.10 (1.01, 4.35) for unhealthy alcohol use, 1.75 (1.00, 3.06) for cannabis use, 1.10 (0.58, 2.08) for illicit opioid use, and 2.25 (1.20, 4.24) for cocaine use, 0.78 (0.44, 1.38) for depressive symptoms, 0.93 (0.58, 1.49) for anxiety symptoms, and 1.31 (0.84, 2.06) for pain symptoms. CONCLUSIONS: While a causal interpretation of our findings may not be warranted, we found evidence for decreased substance use among veterans who quit cigarette smoking but none for the resolution of psychiatric conditions or pain symptoms. Findings suggest the need for additional resources combined with smoking cessation to reduce psychiatric and pain symptoms for high-risk populations.

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer.

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Severe Acute Liver Injury After Hepatotoxic Medication Initiation in Real-World Data.

Importance: Current approaches to classify the hepatotoxic potential of medications are based on cumulative case reports of acute liver injury (ALI), which do not consider the size of the exposed population. There is little evidence from real-world data (data relating to patient health status and/or the delivery of health care routinely collected from sources outside of a research setting) on incidence rates of severe ALI after initiation of medications, accounting for duration of exposure. Objective: To identify the most potentially hepatotoxic medications based on real-world incidence rates of severe ALI and to examine how these rates compare with categorization based on case reports. Design, Setting, and Participants: This series of cohort studies obtained data from the US Department of Veterans Affairs on persons without preexisting liver or biliary disease who initiated a suspected hepatotoxic medication in the outpatient setting between October 1, 2000, and September 30, 2021. Data were analyzed from June 2020 to November 2023. Exposures: Outpatient initiation of any one of 194 medications with 4 or more published reports of hepatotoxicity. Main Outcomes and Measures: Hospitalization for severe ALI, defined by either inpatient: (1) alanine aminotransferase level greater than 120 U/L plus total bilirubin level greater than 2.0 mg/dL or (2) international normalized ratio of 1.5 or higher plus total bilirubin level greater than 2.0 mg/dL recorded within the first 2 days of admission. Acute or chronic liver or biliary disease diagnosis recorded during follow-up or as a discharge diagnosis of a hospitalization for severe ALI resulted in censoring. This study calculated age- and sex-adjusted incidence rates of severe ALI and compared observed rates with hepatotoxicity categories based on cumulative published case reports. Results: The study included 7 899 888 patients across 194 medication cohorts (mean [SD] age, 64.4 [16.4] years, 7 305 558 males [92.5%], 4 354 136 individuals [55.1%] had polypharmacy). Incidence rates of severe ALI ranged from 0 events per 10 000 person-years (candesartan, minocycline) to 86.4 events per 10 000 person-years (stavudine). Seven medications (stavudine, erlotinib, lenalidomide or thalidomide, chlorpromazine, metronidazole, prochlorperazine, and isoniazid) exhibited rates of 10.0 or more events per 10 000 person-years, and 10 (moxifloxacin, azathioprine, levofloxacin, clarithromycin, ketoconazole, fluconazole, captopril, amoxicillin-clavulanate, sulfamethoxazole-trimethoprim, and ciprofloxacin) had rates between 5.0 and 9.9 events per 10 000 person-years. Of these 17 medications with the highest observed rates of severe ALI, 11 (64%) were not included in the highest hepatotoxicity category when based on case reports. Conclusions and Relevance: In this study, incidence rates of severe ALI using real-world data identified the most potentially hepatotoxic medications and can serve as a tool to investigate hepatotoxicity safety signals obtained from case reports. Case report counts did not accurately reflect the observed rates of severe ALI after medication initiation.

A multi-trait epigenome-wide association study identified DNA methylation signature of inflammation among people with HIV.

Inflammation underlies many conditions causing excess morbidity and mortality among people with HIV (PWH). A handful of single-trait epigenome-wide association studies (EWAS) have suggested that inflammation is associated with DNA methylation (DNAm) among PWH. Multi-trait EWAS may further improve statistical power and reveal pathways in common between different inflammatory markers. We conducted single-trait EWAS of three inflammatory markers (soluble CD14, D-dimers, and interleukin 6) in the Veteran Aging Cohort Study (n = 920). The study population was all male PWH with an average age of 51 years, and 82.3% self-reported as Black. We then applied two multi-trait EWAS methods-CPASSOC and OmniTest-to combine single-trait EWAS results. CPASSOC and OmniTest identified 189 and 157 inflammation-associated DNAm sites respectively, of which 112 overlapped. Among the identified sites, 56% were not significant in any single-trait EWAS. Top sites were mapped to inflammation-related genes including IFITM1, PARP9 and STAT1. These genes were significantly enriched in pathways such as "type I interferon signaling" and "immune response to virus". We demonstrate that multi-trait EWAS can improve the discovery of inflammation-associated DNAm sites, genes, and pathways. These DNAm sites suggest molecular mechanisms in response to inflammation associated with HIV and might hold the key to addressing persistent inflammation in PWH.

Contribution of Potentially Inappropriate Medications to Polypharmacy-Associated Risk of Mortality in Middle-Aged Patients: A National Cohort Study.

BACKGROUND: The role of potentially inappropriate medications (PIMs) in mortality has been studied among those 65 years or older. While middle-aged individuals are believed to be less susceptible to the harms of polypharmacy, PIMs have not been as carefully studied in this group. OBJECTIVE: To estimate PIM-associated risk of mortality and evaluate the extent PIMs explain associations between polypharmacy and mortality in middle-aged patients, overall and by sex and race/ethnicity. DESIGN: Observational cohort study. SETTING: Department of Veterans Affairs (VA), the largest integrated healthcare system in the US. PARTICIPANTS: Patients aged 41 to 64 who received a chronic medication (continuous use of ≥ 90 days) between October 1, 2008, and September 30, 2017. MEASUREMENT: Patients were followed for 5 years until death or end of study period (September 30, 2019). Time-updated polypharmacy and hyperpolypharmacy were defined as 5-9 and ≥ 10 chronic medications, respectively. PIMs were identified using the Beers criteria (2015) and were time-updated. Cox models were adjusted for demographic, behavioral, and clinical characteristics. RESULTS: Of 733,728 patients, 676,935 (92.3%) were men, 479,377 (65.3%) were White, and 156,092 (21.3%) were Black. By the end of follow-up, 104,361 (14.2%) patients had polypharmacy, 15,485 (2.1%) had hyperpolypharmacy, and 129,992 (17.7%) were dispensed ≥ 1 PIM. PIMs were independently associated with mortality (HR 1.11, 95% CI 1.04-1.18). PIMs also modestly attenuated risk of mortality associated with polypharmacy (HR 1.07, 95% CI 1.03-1.11 before versus HR 1.05, 95% CI 1.01-1.09 after) and hyperpolypharmacy (HR 1.18, 95% CI 1.09-1.28 before versus HR 1.12, 95% CI 1.03-1.22 after). Patterns varied when stratified by sex and race/ethnicity. LIMITATIONS: The predominantly male VA patient population may not represent the general population. CONCLUSION: PIMs were independently associated with increased mortality, and partially explained polypharmacy-associated mortality in middle-aged people. Other mechanisms of injury from polypharmacy should also be studied.

Identification of hepatic steatosis among persons with and without HIV using natural language processing.

BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis systematically within large clinical repositories of imaging reports. We validated the performance of an NLP algorithm for the identification of SLD in clinical imaging reports and applied this tool to a large population of people with and without HIV. METHODS: Patients were included in the analysis if they enrolled in the Veterans Aging Cohort Study between 2001 and 2017, had an imaging report inclusive of the liver, and had ≥2 years of observation before the imaging study. SLD was considered present when reports contained the terms "fatty," "steatosis," "steatotic," or "steatohepatitis." The performance of the SLD NLP algorithm was compared to a clinical review of 800 reports. We then applied the NLP algorithm to the first eligible imaging study and compared patient characteristics by SLD and HIV status. RESULTS: NLP achieved 100% sensitivity and 88.5% positive predictive value for the identification of SLD. When applied to 26,706 eligible Veterans Aging Cohort Study patient imaging reports, SLD was identified in 72.2% and did not significantly differ by HIV status. SLD was associated with a higher prevalence of metabolic comorbidities, alcohol use disorder, and hepatitis B and C, but not HIV infection. CONCLUSIONS: While limited to those undergoing radiologic study, the NLP algorithm accurately identified SLD in people with and without HIV and offers a valuable tool to evaluate the determinants and consequences of hepatic steatosis.

Self-injurious unnatural death among Veterans with HIV: a nested case-control study.

OBJECTIVE: People living with HIV (PWH) are at increased risk of suicide and death from unintentional causes compared with people living without HIV. Broadening the categorization of death from suicide to self-injurious unnatural death (SIUD) may better identify a more complete set of modifiable risk factors that could be targeted for prevention efforts among PWH. DESIGN: We conducted a nested case-control study using data from the Veterans Aging Cohort Study (VACS), a longitudinal, observational cohort of Veterans from 2006-2015. A total of 5,036 Veterans with HIV, of whom 461 died by SIUD, were included in the sample. METHODS: SIUD was defined using the International Classification of Disease 10th revision cause of death codes. Cases (n = 461) included individuals who died by SIUD (intentional, unintentional, and undetermined causes of death). Controls (n = 4,575) were selected using incidence density sampling, matching on date of birth ± one year, race, sex, and HIV status. SIUD and suicide was estimated using conditional logistic regression. RESULTS: A previous suicide attempt, a diagnosis of an affective disorder, recent use of benzodiazepines, psychiatric hospitalization, and living in the western US significantly increased the risk of suicide and SIUD. Risk factors that appear more important for SIUD than for suicide included a drug use disorder, alcohol use disorder, Hepatitis C, VACS Index 2.0, current smoking, and high pain levels (7-10). CONCLUSION: Limiting studies to known suicides obscures the larger public health burden of excess deaths from self-injurious behavior. Our findings demonstrate the benefit of expanding the focus to SIUD for the identification of modifiable risk factors that could be targeted for treatment.

Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans.

Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main Outcome and Measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and Relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.

Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations.

Background: The prevalence of co-occurring heavy alcohol consumption and obesity is increasing in the United States. Despite neurobiological overlap in the regulation of alcohol consumption and eating behavior, alcohol- and body mass index (BMI)-related phenotypes show no or minimal genetic correlation. We hypothesized that the lack of genetic correlation is due to mixed effect directions of variants shared by AUD and BMI. Methods: We applied MiXeR, to investigate shared genetic architecture between AUD and BMI in individuals of European ancestry. We used conjunctional false discovery rate (conjFDR) analysis to detect loci associated with both phenotypes and their directional effect, Functional Mapping and Annotation (FUMA) to identify lead single nucleotide polymorphisms (SNPs), Genotype-Tissue Expression (GTEx) samples to examine gene expression enrichment across tissue types, and BrainXcan to evaluate the shared associations of AUD and BMI with brain image-derived phenotypes. Results: MiXeR analysis indicated polygenic overlap of 80.9% between AUD and BMI, despite a genetic correlation (r g ) of -.03. ConjFDR analysis yielded 56 lead SNPs with the same effect direction and 76 with the opposite direction. Of the 132 shared lead SNPs, 53 were novel for both AUD and BMI. GTEx analyses identified significant overexpression in the frontal cortex (BA9), hypothalamus, cortex, anterior cingulate cortex (BA24), hippocampus, and amygdala. Amygdala and caudate nucleus gray matter volumes were significantly associated with both AUD and BMI in BrainXcan analyses. Conclusions: More than half of variants significantly associated with AUD and BMI had opposite directions of effect for the traits, supporting our hypothesis that this is the basis for their lack of genetic correlation. Follow-up analyses identified brain regions implicated in executive functioning, reward, homeostasis, and food intake regulation. Together, these findings clarify the extensive polygenic overlap between AUD and BMI and elucidate several overlapping neurobiological mechanisms.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (ncombined = 898,680). We identified 88 independent risk loci; integration with functional genomic tools uncovered 461 potential risk genes, primarily expressed in the brain. TUD was genetically correlated with smoking and psychiatric traits from traditionally ascertained cohorts, externalizing behaviours in children and hundreds of medical outcomes, including HIV infection, heart disease and pain. This work furthers our biological understanding of TUD and establishes electronic health records as a source of phenotypic information for studying the genetics of TUD.

DNA methylation-based telomere length is associated with HIV infection, physical frailty, cancer, and all-cause mortality.

Telomere length (TL) is an important indicator of cellular aging. Shorter TL is associated with several age-related diseases including coronary heart disease, heart failure, diabetes, osteoporosis, and cancer. Recently, a DNA methylation-based TL (DNAmTL) estimator has been developed as an alternative method for directly measuring TL. In this study, we examined the association of DNAmTL with cancer prevalence and mortality risk among people with and without HIV in the Veterans Aging Cohort Study Biomarker Cohort (VACS, N = 1917) and Women's Interagency HIV Study Cohort (WIHS, N = 481). We profiled DNAm in whole blood (VACS) or in peripheral blood mononuclear cells (WIHS) using an array-based method. Cancer prevalence was estimated from electronic medical records and cancer registry data. The VACS Index was used as a measure of physiologic frailty. Models were adjusted for self-reported race and ethnicity, batch, smoking status, alcohol consumption, and five cell types (CD4, CD8, NK, B cell, and monocyte). We found that people with HIV had shorter average DNAmTL than those without HIV infection [beta = -0.25, 95% confidence interval (-0.32, -0.18), p = 1.48E-12]. Greater value of VACS Index [beta = -0.002 (-0.003, -0.001), p = 2.82E-05] and higher cancer prevalence [beta = -0.07 (-0.10, -0.03), p = 1.37E-04 without adjusting age] were associated with shortened DNAmTL. In addition, one kilobase decrease in DNAmTL was associated with a 40% increase in mortality risk [hazard ratio: 0.60 (0.44, 0.82), p = 1.42E-03]. In summary, HIV infection, physiologic frailty, and cancer are associated with shortening DNAmTL, contributing to an increased risk of all-cause mortality.

A multi-ancestry genetic study of pain intensity in 598,339 veterans.

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, ß-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

Human Immunodeficiency Virus in Older Adults.

As people with HIV live longer, they can experience increased incidence and earlier onset of chronic conditions and geriatric syndromes. Older people are also at substantially increased risk of delayed diagnosis and treatment for HIV. Increasing provider awareness of this is pivotal in ensuring adequate consideration of HIV testing and earlier screening for chronic conditions. In addition, evaluating patients for common geriatric syndromes such as polypharmacy, frailty, falls, and cognitive impairment should be contextualized based on how they present.

Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration.

BACKGROUND: Survival in pancreatic ductal adenocarcinoma (PDAC) remains poor due to late diagnosis. Electronic Health Records (EHRs) can be used to study this rare disease, but validated algorithms to identify PDAC in the United States EHRs do not currently exist. AIMS: To develop and validate an algorithm using Veterans Health Administration (VHA) EHR data for the identification of patients with PDAC. METHODS: We developed two algorithms to identify patients with PDAC in the VHA from 2002 to 2023. The algorithms required diagnosis of exocrine pancreatic cancer in either ≥ 1 or ≥ 2 of the following domains: (i) the VA national cancer registry, (ii) an inpatient encounter, or (iii) an outpatient encounter in an oncology setting. Among individuals identified with ≥ 1 of the above criteria, a random sample of 100 were reviewed by three gastroenterologists to adjudicate PDAC status. We also adjudicated fifty patients not qualifying for either algorithm. These patients died as inpatients and had alkaline phosphatase values within the interquartile range of patients who met ≥ 2 of the above criteria for PDAC. These expert adjudications allowed us to calculate the positive and negative predictive value of the algorithms. RESULTS: Of 10.8 million individuals, 25,533 met ≥ 1 criteria (PPV 83.0%, kappa statistic 0.93) and 13,693 individuals met ≥ 2 criteria (PPV 95.2%, kappa statistic 1.00). The NPV for PDAC was 100%. CONCLUSIONS: An algorithm incorporating readily available EHR data elements to identify patients with PDAC achieved excellent PPV and NPV. This algorithm is likely to enable future epidemiologic studies of PDAC.

Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts.

BACKGROUND: Epigenome-wide association studies (EWAS) have identified CpG sites associated with HIV infection in blood cells in bulk, which offer limited knowledge of cell-type specific methylation patterns associated with HIV infection. In this study, we aim to identify differentially methylated CpG sites for HIV infection in immune cell types: CD4+ T-cells, CD8+ T-cells, B cells, Natural Killer (NK) cells, and monocytes. METHODS: Applying a computational deconvolution method, we performed a cell-type based EWAS for HIV infection in three independent cohorts (Ntotal = 1,382). DNA methylation in blood or in peripheral blood mononuclear cells (PBMCs) was profiled by an array-based method and then deconvoluted by Tensor Composition Analysis (TCA). The TCA-computed CpG methylation in each cell type was first benchmarked by bisulfite DNA methylation capture sequencing in a subset of the samples. Cell-type EWAS of HIV infection was performed in each cohort separately and a meta-EWAS was conducted followed by gene set enrichment analysis. RESULTS: The meta-analysis unveiled a total of 2,021 cell-type unique significant CpG sites for five inferred cell types. Among these inferred cell-type unique CpG sites, the concordance rate in the three cohorts ranged from 96% to 100% in each cell type. Cell-type level meta-EWAS unveiled distinct patterns of HIV-associated differential CpG methylation, where 74% of CpG sites were unique to individual cell types (false discovery rate, FDR <0.05). CD4+ T-cells had the largest number of unique HIV-associated CpG sites (N = 1,624) compared to any other cell type. Genes harboring significant CpG sites are involved in immunity and HIV pathogenesis (e.g. CD4+ T-cells: NLRC5, CX3CR1, B cells: IFI44L, NK cells: IL12R, monocytes: IRF7), and in oncogenesis (e.g. CD4+ T-cells: BCL family, PRDM16, monocytes: PRDM16, PDCD1LG2). HIV-associated CpG sites were enriched among genes involved in HIV pathogenesis and oncogenesis that were enriched among interferon-α and -γ, TNF-α, inflammatory response, and apoptotic pathways. CONCLUSION: Our findings uncovered computationally inferred cell-type specific modifications in the host epigenome for people with HIV that contribute to the growing body of evidence regarding HIV pathogenesis.

Postoperative Outcomes Associated with the Timing of Surgery After SARS-CoV-2 Infection.

OBJECTIVE: Examine the association between prior SARS-CoV-2 infection, interval from infection to surgery, and adverse surgical outcomes. SUMMARY BACKGROUND DATA: Earlier series have reported worse outcomes for surgery after COVID-19 illness, and these findings have led to routinely deferring surgery seven weeks after infection. METHODS: We created a retrospective cohort of patients from US Veterans Health Administration facilities nationwide, April 2020-September 2022, undergoing surgical procedures. Primary outcomes were 90-day all-cause mortality and 30-day complications. Within surgical procedure groupings, SARS-CoV-2 infected and uninfected patients were matched in a 1:4 ratio. We categorized patients by two-week intervals from SARS-CoV-2 positive test to surgery. Hierarchical multilevel multivariable logistic regression models were used to estimate the association between infection to surgery interval versus no infection and primary endpoints. RESULTS: We identified 82,815 veterans undergoing eligible operations (33% general, 27% orthopedic, 13% urologic, 9% vascular), of whom 16,563 (20%) had laboratory confirmed SARS-CoV-2 infection prior to surgery. The multivariable models demonstrated an association between prior SARS-CoV-2 infection and increased 90-day mortality (odds ratio (OR) 1.42, 95% CI 1.08, 1.86) and complications (OR 1.32, 95% CI 1.11, 1.57) only for patients having surgery within 14 days of infection. ASA-stratified multivariable models showed that the associations between increased 90-day mortality (OR 1.40, 95% CI 1.12, 1.75) and complications (OR 1.73, 95% CI 1.34, 2.24) for patients having surgery within 14 days of infection were confined to those with ASA 4-5. CONCLUSIONS: In a contemporary surgical cohort, patients with prior SARS-CoV-2 infection only had increased post-operative mortality or complications when they had surgery within 14 days after positive test. These findings support revising timing recommendations between surgery and prior SARS-CoV-2 infection.

"Call 911 - That's my [Advance Care] Plan": Factors that Inform Advance Care Planning Conversation Readiness Among Aging Persons Living With HIV.

Antiretroviral therapy has dramatically increased the lifespan of people living with HIV (PLWH), but advance care planning (ACP) and hospice services are underutilized in this population. The purpose of this study was to understand barriers and facilitators to ACP among this group. PLWH (n = 25) were recruited from an HIV Clinic at a Veterans Affairs (VA) Medical Center in Atlanta, GA to represent a range of sociodemographic characteristics and experiences. Semi-structured interviews were analyzed using thematic analysis. More than half of participants (64%) indicated not engaging in ACP. We identified four key barriers to ACP: (1) a self-image among PLWH as "survivors" (and a reluctance to think about ACP); (2) a history of mistrust and mistreatment; (3) weak social ties and a desire to avoid disclosure of HIV status; and (4) a value for self-reliance. Findings have important implications for interventions to overcome these barriers.

The relationship of age and comorbid conditions to hospital and nursing home days in Medicaid recipients with HIV.

OBJECTIVE: To determine how aging impacts healthcare utilization in persons with HIV (PWH) compared with persons without HIV (PWoH). DESIGN: Matched case-control study. METHODS: We studied Medicaid recipients in the United States, aged 18-64 years, from 2001 to 2012. We matched each of 270 074 PWH to three PWoH by baseline year, age, gender, and zip code. Outcomes were hospital and nursing home days per month (DPM). Comorbid condition groups were cardiovascular disease, diabetes, liver disease, mental health conditions, pulmonary disease, and renal disease. We used linear regression to examine the joint relationships of age and comorbid conditions on the two outcomes, stratified by sex at birth. RESULTS: We found small excesses in hospital DPM for PWH compared with PWoH. There were 0.03 and 0.07 extra hospital DPM for female and male individuals, respectively, and no increases with age. In contrast, excess nursing home DPM for PWH compared with PWoH rose linearly with age, peaking at 0.35 extra days for female individuals and 0.4 extra days for male individuals. HIV-associated excess nursing home DPM were greatest for persons with cardiovascular disease, diabetes, mental health conditions, and renal disease. For PWH at age 55 years, this represents an 81% increase in the nursing home DPM for male individuals, and a 110% increase for female individuals, compared PWoH. CONCLUSION: Efforts to understand and interrupt this pronounced excess pattern of nursing home DPM among PWH compared with PWoH are needed and may new insights into how HIV and comorbid conditions jointly impact aging with HIV.

Creation and Validation of an Automated Registry for Outpatient Parenteral Antibiotics.

Existing outpatient parenteral antibiotic therapy (OPAT) registries are resource intensive, and OPAT programs struggle to produce objective data to show the value of their work. We describe the building and validation of an automated OPAT registry within our electronic medical record and provide objective data on the value of the program. Variables and outcomes include age, sex, race, ethnicity, primary insurance payor, antibiotic names, infection syndromes treated, discharge disposition, 30-day all-cause readmission and death rates, complications, and an estimate of the hospital days saved. Records for 146 OPAT episodes were reviewed manually to validate the registry. Data were displayed in a dashboard within the electronic medical record. Over the 4-year time frame, our registry collected 3956 unique patients who completed 4710 episodes (approximately 1200 episodes per year). A total of 400 complications during OPAT were identified. All variables had an accuracy of >90% on validation. The OPAT program resulted in a reduction in hospital length of stay by 88 820 days, or roughly 22 000 days per year. We intend our registry to serve as a blueprint for similar OPAT programs with limited administrative resources. Wider application of our system would allow for easier aggregation and comparisons of OPAT practice and address the lack interinstitutional standardization of OPAT data and outcomes.