Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder.

Persons diagnosed with schizophrenia (SCZ) or bipolar I disorder (BPI) are at high risk for self-injurious behavior, suicidal ideation, and suicidal behaviors (SB). Characterizing associations between diagnosed health problems, prior pharmacological treatments, and polygenic scores (PGS) has potential to inform risk stratification. We examined self-reported SB and ideation using the Columbia Suicide Severity Rating Scale (C-SSRS) among 3,942 SCZ and 5,414 BPI patients receiving care within the Veterans Health Administration (VHA). These cross-sectional data were integrated with electronic health records (EHRs), and compared across lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. PGS were constructed using available genomic data for related traits. Genome-wide association studies were performed to identify and prioritize specific loci. Only 20% of the veterans who reported SB had a corroborating ICD-9/10 EHR code. Among those without prior SB, more than 20% reported new-onset SB at follow-up. SB were associated with a range of additional clinical diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking initiation, suicide attempt, and major depressive disorder were associated with SB. The GWAS for SB yielded no significant loci. Among individuals with a diagnosed mental illness, self-reported SB were strongly associated with clinical variables across several EHR domains. Analyses point to sequelae of substance-related and psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in health records, underscoring the value of regular screening with direct, in-person assessments, especially among high-risk individuals.

Identifying Veterans Who Benefit From Nirmatrelvir-Ritonavir: A Target Trial Emulation.

BACKGROUND: Nirmatrelvir-ritonavir is recommended for persons at risk for severe coronavirus disease 2019 (COVID-19) but remains underutilized. Information on which eligible groups are likely to benefit from treatment is needed. METHODS: We conducted a target trial emulation study in the Veterans Health Administration comparing nirmatrelvir-ritonavir treated versus matched untreated veterans at risk for severe COVID-19 who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from April 2022 through March 2023. We measured incidence of any hospitalization or all-cause mortality at 30 days. Outcomes were measured for the entire cohort, as well as among subgroups defined by 30-day risk of death or hospitalization, estimated using an ensemble risk prediction model. RESULTS: Participants were 87% male with median age 66 years and 16% unvaccinated. Compared with matched untreated participants, those treated with nirmatrelvir-ritonavir (n = 24 205) had a lower 30-day risk for hospitalization (1.80% vs 2.30%; risk difference [RD], -0.50% points [95% confidence interval {CI}: -.69 to -.35]) and death (0.11% vs 0.30%; RD, -0.20 [95% CI: -.24 to -.13]). The greatest reductions in combined hospitalization or death were observed in the highest risk quartile (RD -2.85 [95% CI: -3.94 to -1.76]), immunocompromised persons (RD -1.91 [95% CI: -3.09 to -.74]), and persons aged ≥75 years (RD -1.16 [95% CI: -1.73 to -.59]). No reductions were observed in the 2 lowest risk quartiles or persons younger than 65 years. CONCLUSIONS: Nirmatrelvir-ritonavir was effective in reducing 30-day hospitalization and death in older veterans, those at highest predicted risk for severe outcomes, and immunocompromised groups. Benefit was not observed in younger veterans or groups at lower predicted risk for hospitalization and death.

Comparison of the Test-negative Design and Cohort Design With Explicit Target Trial Emulation for Evaluating COVID-19 Vaccine Effectiveness.

BACKGROUND: Observational studies are used for estimating vaccine effectiveness under real-world conditions. The practical performance of two common approaches-cohort and test-negative designs-need to be compared for COVID-19 vaccines. METHODS: We compared the cohort and test-negative designs to estimate the effectiveness of the BNT162b2 vaccine against COVID-19 outcomes using nationwide data from the United States Department of Veterans Affairs. Specifically, we (1) explicitly emulated a target trial using follow-up data and evaluated the potential for confounding using negative controls and benchmarking to a randomized trial, (2) performed case-control sampling of the cohort to confirm empirically that the same estimate is obtained, (3) further restricted the sampling to person-days with a test, and (4) implemented additional features of a test-negative design. We also compared their performance in limited datasets. RESULTS: Estimated BNT162b2 vaccine effectiveness was similar under all four designs. Empirical results suggested limited residual confounding by healthcare-seeking behavior. Analyses in limited datasets showed evidence of residual confounding, with estimates biased downward in the cohort design and upward in the test-negative design. CONCLUSION: Vaccine effectiveness estimates under a cohort design with explicit target trial emulation and a test-negative design were similar when using rich information from the VA healthcare system, but diverged in opposite directions when using a limited dataset. In settings like ours with sufficient information on confounders and other key variables, the cohort design with explicit target trial emulation may be preferable as a principled approach that allows estimation of absolute risks and facilitates interpretation of effect estimates.

Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program.

BACKGROUND: The Million Veteran Program (MVP) participants represent 100 years of US history, including significant social and demographic changes over time. Our study assessed two aspects of the MVP: (i) longitudinal changes in population diversity and (ii) how these changes can be accounted for in genome-wide association studies (GWAS). To investigate these aspects, we divided MVP participants into five birth cohorts (N-range = 123,888 [born from 1943 to 1947] to 136,699 [born from 1948 to 1953]). RESULTS: Ancestry groups were defined by (i) HARE (harmonized ancestry and race/ethnicity) and (ii) a random-forest clustering approach using the 1000 Genomes Project and the Human Genome Diversity Project (1kGP + HGDP) reference panels (77 world populations representing six continental groups). In these groups, we performed GWASs of height, a trait potentially affected by population stratification. Birth cohorts demonstrate important trends in ancestry diversity over time. More recent HARE-assigned Europeans, Africans, and Hispanics had lower European ancestry proportions than older birth cohorts (0.010 < Cohen's d < 0.259, p < 7.80 × 10-4). Conversely, HARE-assigned East Asians showed an increase in European ancestry proportion over time. In GWAS of height using HARE assignments, genomic inflation due to population stratification was prevalent across all birth cohorts (linkage disequilibrium score regression intercept = 1.08 ± 0.042). The 1kGP + HGDP-based ancestry assignment significantly reduced the population stratification (mean intercept reduction = 0.045 ± 0.007, p < 0.05) confounding in the GWAS statistics. CONCLUSIONS: This study provides a characterization of ancestry diversity of the MVP cohort over time and compares two strategies to infer genetically defined ancestry groups by assessing differences in controlling population stratification in genome-wide association studies.

Association of deployment characteristics and exposures with persistent ill health among 1990-1991 Gulf War veterans in the VA Million Veteran Program.

BACKGROUND: Veterans of the 1990-1991 Gulf War have experienced excess health problems, most prominently the multisymptom condition Gulf War illness (GWI). The Department of Veterans Affairs (VA) Cooperative Studies Program #2006 "Genomics of Gulf War Illness in Veterans" project was established to address important questions concerning pathobiological and genetic aspects of GWI. The current study evaluated patterns of chronic ill health/GWI in the VA Million Veteran Program (MVP) Gulf War veteran cohort in relation to wartime exposures and key features of deployment, 27-30 years after Gulf War service. METHODS: MVP participants who served in the 1990-1991 Gulf War completed the MVP Gulf War Era Survey in 2018-2020. Survey responses provided detailed information on veterans' health, Gulf War exposures, and deployment time periods and locations. Analyses determined associations of three defined GWI/ill health outcomes with Gulf War deployment characteristics and exposures. RESULTS: The final cohort included 14,103 veterans; demographic and military characteristics of the sample were similar to the full population of U.S. 1990-1991 Gulf War veterans. Overall, a substantial number of veterans experienced chronic ill health, as indicated by three defined outcomes: 49% reported their health as fair or poor, 31% met Centers for Disease Control and Prevention criteria for severe GWI, and 20% had been diagnosed with GWI by a healthcare provider. Health outcomes varied consistently with veterans' demographic and military characteristics, and with exposures during deployment. All outcomes were most prevalent among youngest veterans (< 50 years), Army and Marine Corps veterans, enlisted personnel (vs. officers), veterans located in Iraq and/or Kuwait for at least 7 days, and veterans who remained in theater from January/February 1991 through the summer of 1991. In multivariable models, GWI/ill health was most strongly associated with three exposures: chemical/biological warfare agents, taking pyridostigmine bromide pills, and use of skin pesticides. CONCLUSIONS: Results from this large cohort indicate that GWI/chronic ill health continues to affect a large proportion of Gulf War veterans in patterns associated with 1990-1991 Gulf War deployment and exposures. Findings establish a foundation for comprehensive evaluation of genetic factors and deployment exposures in relation to GWI risk and pathobiology.

Effectiveness of Nirmatrelvir-Ritonavir Against the Development of Post-COVID-19 Conditions Among U.S. Veterans : A Target Trial Emulation.

BACKGROUND: COVID-19 has been linked to the development of many post-COVID-19 conditions (PCCs) after acute infection. Limited information is available on the effectiveness of oral antivirals used to treat acute COVID-19 in preventing the development of PCCs. OBJECTIVE: To measure the effectiveness of outpatient treatment of COVID-19 with nirmatrelvir-ritonavir in preventing PCCs. DESIGN: Retrospective target trial emulation study comparing matched cohorts receiving nirmatrelvir-ritonavir versus no treatment. SETTING: Veterans Health Administration (VHA). PARTICIPANTS: Nonhospitalized veterans in VHA care who were at risk for severe COVID-19 and tested positive for SARS-CoV-2 during January through July 2022. INTERVENTION: Nirmatrelvir-ritonavir treatment for acute COVID-19. MEASUREMENTS: Cumulative incidence of 31 potential PCCs at 31 to 180 days after treatment or a matched index date, including cardiac, pulmonary, renal, thromboembolic, gastrointestinal, neurologic, mental health, musculoskeletal, endocrine, and general conditions and symptoms. RESULTS: Eighty-six percent of the participants were male, with a median age of 66 years, and 17.5% were unvaccinated. Baseline characteristics were well balanced between participants treated with nirmatrelvir-ritonavir and matched untreated comparators. No differences were observed between participants treated with nirmatrelvir-ritonavir (n = 9593) and their matched untreated comparators in the incidence of most PCCs examined individually or grouped by organ system, except for lower combined risk for venous thromboembolism and pulmonary embolism (subhazard ratio, 0.65 [95% CI, 0.44 to 0.97]; cumulative incidence difference, -0.29 percentage points [CI, -0.52 to -0.05 percentage points]). LIMITATIONS: Ascertainment of PCCs using International Classification of Diseases, 10th Revision, codes may be inaccurate. Evaluation of many outcomes could have resulted in spurious associations with combined thromboembolic events by chance. CONCLUSION: Out of 31 potential PCCs, only combined thromboembolic events seemed to be reduced by nirmatrelvir-ritonavir. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.

Effectiveness of COVID-19 Treatment With Nirmatrelvir-Ritonavir or Molnupiravir Among U.S. Veterans: Target Trial Emulation Studies With One-Month and Six-Month Outcomes.

BACKGROUND: Information about the effectiveness of oral antivirals in preventing short- and long-term COVID-19-related outcomes in the setting of Omicron variant transmission and COVID-19 vaccination is limited. OBJECTIVE: To measure the effectiveness of nirmatrelvir-ritonavir and molnupiravir for outpatient treatment of COVID-19. DESIGN: Three retrospective target trial emulation studies comparing matched cohorts of nirmatrelvir-ritonavir versus no treatment, molnupiravir versus no treatment, and nirmatrelvir-ritonavir versus molnupiravir. SETTING: Veterans Health Administration (VHA). PARTICIPANTS: Nonhospitalized veterans in VHA care who were at risk for severe COVID-19 and tested positive for SARS-CoV-2 during January through July 2022. INTERVENTION: Nirmatrelvir-ritonavir or molnupiravir pharmacotherapy. MEASUREMENTS: Incidence of any hospitalization or all-cause mortality at 30 days and from 31 to 180 days. RESULTS: Eighty-seven percent of participants were male; the median age was 66 years, and 18% were unvaccinated. Compared with matched untreated control participants, those treated with nirmatrelvir-ritonavir (n = 9607) had lower 30-day risk for hospitalization (22.07 vs. 30.32 per 1000 participants; risk difference [RD], -8.25 [95% CI, -12.27 to -4.23] per 1000 participants) and death (1.25 vs. 5.47 per 1000 participants; RD, -4.22 [CI, -5.45 to -3.00] per 1000 participants). Among persons alive at day 31, reductions were seen in 31- to 180-day incidence of death (hazard ratio, 0.66 [CI, 0.49 to 0.89]) but not hospitalization (subhazard ratio, 0.90 [CI, 0.79 to 1.02]). Molnupiravir-treated participants (n = 3504) had lower 30-day and 31- to 180-day risks for death (3.14 vs. 13.56 per 1000 participants at 30 days; RD, -10.42 [CI, -13.49 to -7.35] per 1000 participants; hazard ratio at 31 to 180 days, 0.67 [CI, 0.48 to 0.95]) but not hospitalization. A difference in 30-day or 31- to 180-day risk for hospitalization or death was not observed between matched nirmatrelvir- or molnupiravir-treated participants. LIMITATION: The date of COVID-19 symptom onset for most veterans was unknown. CONCLUSION: Nirmatrelvir-ritonavir was effective in reducing 30-day hospitalization and death. Molnupiravir was associated with a benefit for 30-day mortality but not hospitalization. Further reductions in mortality from 31 to 180 days were observed with both antivirals. PRIMARY FUNDING SOURCE: U.S. Department of Veterans Affairs.

A Network Analysis of Cigarette Craving.

INTRODUCTION: Craving is considered a central process to addictive behavior including cigarette smoking, although the clinical utility of craving relies on how it is defined and measured. Network analysis enables examining the network structure of craving symptoms, identifying the most central symptoms of cigarette craving, and improving our understanding of craving and its measurement. AIMS AND METHODS: This study used network analysis to identify the central symptoms of self-reported cigarette craving as measured by the Craving Experience Questionnaire, which assesses both craving strength and craving frequency. Data were obtained from baseline of a randomized controlled trial of mindfulness training for smoking cessation. RESULTS: The most central symptoms in an overall cigarette craving network were the frequency of imagining its smell, imagining its taste, and intrusive thoughts. The most central symptoms of both craving frequency and craving strength sub-networks were imagining its taste, the urge to have it, and intrusive thoughts. CONCLUSIONS: The most central craving symptoms reported by individuals in treatment for cigarette smoking were from the frequency domain, demonstrating the value of assessing craving frequency along with craving strength. Central craving symptoms included multisensory imagery (taste, smell), intrusive thoughts, and urge, providing additional evidence that these symptoms may be important to consider in craving measurement and intervention. Findings provide insight into the symptoms that are central to craving, contributing to a better understanding of cigarette cravings, and suggesting potential targets for clinical interventions. IMPLICATIONS: This study used network analysis to identify central symptoms of cigarette craving. Both craving frequency and strength were assessed. The most central symptoms of cigarette craving were related to craving frequency. Central symptoms included multisensory imagery, intrusive thoughts, and urge. Central symptoms might be targeted by smoking cessation treatment.

Early predictors of chronic posttraumatic stress disorder symptom trajectories in U.S. Army soldiers deployed to the Iraq war zone.

The course of posttraumatic stress disorder (PTSD) symptoms varies among veterans of war zones, but sources of variation in long-term symptom course remain poorly understood. Modeling of symptom growth trajectories facilitates the understanding of predictors of individual outcomes over time. Although growth mixture modeling (GMM) has been applied to military populations, few studies have incorporated both predeployment and follow-up measurements over an extended time. In this prospective study, 1,087 U.S. Army soldiers with varying military occupational specialties and geographic locations were assessed before and after deployment to the Iraq war zone, with long-term follow-up assessment occurring at least 5 years after return from deployment. The primary outcome variable was the PTSD Checklist-Civilian Version summary score. GMM yielded four latent profiles, characterized as primarily asymptomatic (n = 194, 17.8%); postdeployment worsening symptoms (n = 84, 7.7%); mild symptoms (n = 320, 29.4%); and preexisting, with a chronic postdeployment elevation of symptoms (n = 489, 45.0%). Regression models comparing the primarily asymptomatic class to the symptomatic classes revealed that chronic symptom classes were associated with higher degrees of stress exposure, less predeployment social support, military reservist or veteran status at the most recent assessment, and poorer predeployment visual memory, ORs = 0.98-2.90. PTSD symptom course varies considerably over time after military deployment and is associated with potentially modifiable biopsychosocial factors that occur early in its course in addition to exposures and military status.

Associations Among Increases in Posttraumatic Stress Symptoms, Neurocognitive Performance, and Long-Term Functional Outcomes in U.S. Iraq War Veterans.

Cross-sectional research suggests that posttraumatic stress symptoms (PTSS) among war zone veterans are associated with functional impairment and poor quality of life. Less is known about the long-term functional repercussions of PTSS. This study of Iraq War veterans examined the associations between increases in PTSS and long-term functional outcomes, including the potential contributions of neurocognitive decrements. Service members and veterans (N = 594) completed self-report measures of functioning and PTSS severity before Iraq War deployment and again after their return (M = 9.3 years postdeployment). Some participants (n = 278) also completed neurocognitive testing at both times. Multiple regression analyses with the full sample-adjusted for TBI, demographic characteristics, military variables, and predeployment PTSS and functioning-revealed that increased PTSS severity over time was significantly associated with unemployment, aOR = 1.04, 95% CI [1.03, 1.06]; poorer work performance; and poorer physical, emotional, and cognitive health-related functioning at long-term follow-up, f2 s = 0.37-1.79. Among participants who completed neurocognitive testing, a decline in select neurocognitive measures was associated with poorer functioning; however, neurocognitive decrements did not account for associations between increased PTSS and unemployment, aOR = 1.04, 95% CI [1.02, 1.07], with the size and direction upheld after adding neurocognitive variables, or poorer functional outcomes, with small increases after adding neurocognitive measures to the models, f2 s = 0.03-0.10. War zone veterans experiencing long-term increased PTSS and/or neurocognitive decrements may be at elevated risk for higher-level functional impairment over time, suggesting that early PTSS management may enhance long-term functioning.

Genome-wide association study of cognitive performance in U.S. veterans with schizophrenia or bipolar disorder.

Cognitive impairment is a frequent and serious problem in patients with various forms of severe mental illnesses (SMI), including schizophrenia (SZ) and bipolar disorder (BP). Recent research suggests genetic links to several cognitive phenotypes in both SMI and in the general population. Our goal in this study was to identify potential genomic signatures of cognitive functioning in veterans with severe mental illness and compare them to previous findings for cognition across different populations. Veterans Affairs (VA) Cooperative Studies Program (CSP) Study #572 evaluated cognitive and functional capacity measures among SZ and BP patients. In conjunction with the VA Million Veteran Program, 3,959 European American (1,095 SZ, 2,864 BP) and 2,601 African American (1,095 SZ, 2,864 BP) patients were genotyped using a custom Affymetrix Axiom Biobank array. We performed a genome-wide association study of global cognitive functioning, constructed polygenic scores for SZ and cognition in the general population, and examined genetic correlations with 2,626 UK Biobank traits. Although no single locus attained genome-wide significance, observed allelic effects were strongly consistent with previous studies. We observed robust associations between global cognitive functioning and polygenic scores for cognitive performance, intelligence, and SZ risk. We also identified significant genetic correlations with several cognition-related traits in UK Biobank. In a diverse cohort of U.S. veterans with SZ or BP, we demonstrate broad overlap of common genetic effects on cognition in the general population, and find that greater polygenic loading for SZ risk is associated with poorer cognitive performance.

Validation of an Electronic Medical Record-Based Algorithm for Identifying Posttraumatic Stress Disorder in U.S. Veterans.

We developed an algorithm for identifying U.S. veterans with a history of posttraumatic stress disorder (PTSD), using the Department of Veterans Affairs (VA) electronic medical record (EMR) system. This work was motivated by the need to create a valid EMR-based phenotype to identify thousands of cases and controls for a genome-wide association study of PTSD in veterans. We used manual chart review (n = 500) as the gold standard. For both the algorithm and chart review, three classifications were possible: likely PTSD, possible PTSD, and likely not PTSD. We used Lasso regression with cross-validation to select statistically significant predictors of PTSD from the EMR and then generate a predicted probability score of being a PTSD case for every participant in the study population (range: 0-1.00). Comparing the performance of our probabilistic approach (Lasso algorithm) to a rule-based approach (International Classification of Diseases [ICD] algorithm), the Lasso algorithm showed modestly higher overall percent agreement with chart review than the ICD algorithm (80% vs. 75%), higher sensitivity (0.95 vs. 0.84), and higher accuracy (AUC = 0.95 vs. 0.90). We applied a 0.7 probability cut-point to the Lasso results to determine final PTSD case-control status for the VA population. The final algorithm had a 0.99 sensitivity, 0.99 specificity, 0.95 positive predictive value, and 1.00 negative predictive value for PTSD classification (grouping possible PTSD and likely not PTSD) as determined by chart review. This algorithm may be useful for other research and quality improvement endeavors within the VA.

Spanish Abstracts by Asociación Chilena de Estrés Traumático (ACET) Validación de un algoritmo basado en registros médicos electrónicos para identificar el trastorno por estrés postraumático en veteranos de los EE. UU. VALIDACIÓN DE ALGORITOMO DE TEPT Desarrollamos un algoritmo para identificar a los veteranos de EE. UU. con historial de trastorno de estrés postraumático (TEPT), utilizando el sistema de registro médico electrónico (RME) del Departamento de Asuntos de Veteranos (AS). Este trabajo fue motivado por la necesidad de crear un fenotipo válido, basado en RME para identificar miles de casos y controles para un estudio de asociación del genoma del TEPT en los veteranos. Utilizamos la revisión manual de tablas (n = 500) como gold estándar. Tanto para el algoritmo como para la revisión de la tabla, fueron posibles tres clasificaciones: PTSD probable, PTSD posible y probablemente no PTSD. Usamos la regresión Lasso con validación cruzada para seleccionar los factores de pronóstico estadísticamente significativos del TEPT a partir de la RME y luego generar una puntuación de probabilidad pronosticada de ser un caso de TEPT para cada participante en la población del estudio (rango: 0-1.00). Comparando el rendimiento de nuestro enfoque probabilístico (algoritmo Lasso) con un enfoque basado en reglas (algoritmo de Clasificación Internacional de Enfermedades [CIE]), el algoritmo Lasso mostró un porcentaje de acuerdo global modestamente más alto con la revisión de tablas que el algoritmo CIE (80% vs. 75). %), mayor sensibilidad (0.95 frente a 0.84) y mayor precisión (AUC = 0.95 frente a 0.90). Aplicamos un punto de corte de probabilidad de 0.7 a los resultados de Lasso para determinar el estado final de control de caso de TEPT para la población de AV. El algoritmo final tuvo una sensibilidad de 0.99, una especificidad de 0.99, un valor predictivo positivo de 0.95 y un valor predictivo negativo de 1.00 para la clasificación de TEPT (agrupación de TEPT posible y probablemente no TEPT) según lo determinado por la revisión de la tabla. Este algoritmo puede ser útil para otros esfuerzos de investigación y mejora de la calidad dentro del AV.

Impact of genotyping errors on statistical power of association tests in genomic analyses: A case study.

A key step in genomic studies is to assess high throughput measurements across millions of markers for each participant's DNA, either using microarrays or sequencing techniques. Accurate genotype calling is essential for downstream statistical analysis of genotype-phenotype associations, and next generation sequencing (NGS) has recently become a more common approach in genomic studies. How the accuracy of variant calling in NGS-based studies affects downstream association analysis has not, however, been studied using empirical data in which both microarrays and NGS were available. In this article, we investigate the impact of variant calling errors on the statistical power to identify associations between single nucleotides and disease, and on associations between multiple rare variants and disease. Both differential and nondifferential genotyping errors are considered. Our results show that the power of burden tests for rare variants is strongly influenced by the specificity in variant calling, but is rather robust with regard to sensitivity. By using the variant calling accuracies estimated from a substudy of a Cooperative Studies Program project conducted by the Department of Veterans Affairs, we show that the power of association tests is mostly retained with commonly adopted variant calling pipelines. An R package, GWAS.PC, is provided to accommodate power analysis that takes account of genotyping errors (http://zhaocenter.org/software/).

Longitudinal Associations among Posttraumatic Stress Disorder Symptoms, Traumatic Brain Injury, and Neurocognitive Functioning in Army Soldiers Deployed to the Iraq War.

OBJECTIVES: Military deployment is associated with increased risk of adverse emotional and cognitive outcomes. Longitudinal associations involving posttraumatic stress disorder (PTSD), relatively mild traumatic brain injury (TBI), and neurocognitive compromise are poorly understood, especially with regard to long-term outcomes, and rigorous research is necessary to better understand the corresponding relationships. The objective of this study was to examine short-term and long-term (>5 years) longitudinal associations among PTSD, neurocognitive performance, and TBI following military deployment. METHODS: In this prospective study, N=315 U.S. Army soldiers were assessed at military installations before (2003-2005) and after (2004-2006) an index deployment to the Iraq War, and again an average of 7.6 years later (2010-2014) as a nationally dispersed cohort of active duty soldiers, reservists, and veterans. Thus, the study design allowed for two measurement intervals over which to examine changes. All assessments included the PTSD Checklist, civilian version, and individually-administered performance-based neurocognitive tests. TBI history was derived from clinical interview. RESULTS: Autoregressive analyses indicated that visual reproduction scores were inversely related to subsequent PTSD symptom severity at subsequent assessments. Conversely, increases in PTSD symptom severity over each measurement interval were associated with poorer verbal and/or visual recall at the end of each interval, and less efficient reaction time at post-deployment. TBI, primarily mild in this sample, was associated with adverse PTSD symptom outcomes at both post-deployment and long-term follow-up. CONCLUSIONS: These results suggest longitudinal relationships among PTSD symptoms, TBI, and neurocognitive decrements may contribute to sustained emotional and neurocognitive symptoms over time. (JINS, 2018, 24, 311-323).

Longitudinal Examination of Posttraumatic Stress Disorder as a Long-Term Outcome of Iraq War Deployment.

The mental health toll of the Iraq and Afghanistan Wars on military veterans has been considerable, yet little is known about the persistence of these adverse outcomes, especially relative to predeployment status. We prospectively examined posttraumatic stress disorder (PTSD) as a long-term consequence of warzone deployment, integrating data collected from 2003-2014. In the Neurocognition Deployment Health Study, we measured PTSD symptoms in US Army soldiers before and shortly after Iraq War deployment. We used the PTSD Checklist-Civilian Version and a structured clinical interview (i.e., Clinician-Administered PTSD Scale) to reassess PTSD in 598 service members and military veterans a median of 7.9 years (interquartile range, 7.2-8.5 years) after an index Iraq deployment. At long-term follow-up, 24.7% (95% confidence interval (CI): 21.5, 28.4) of participants met the case definition for PTSD, which was an absolute increase of 14.2% from the percentage assessed postdeployment (10.5%; 95% CI: 7.8, 13.7) and of 17.3% from the percentage assessed predeployment (7.4%; 95% CI: 5.5, 9.8). These findings highlight that PTSD is an enduring consequence of warzone participation among contemporary military personnel and veterans. The largest increase in PTSD cases occurred between the postdeployment and long-term follow-up assessments, which suggests that adverse stress reactions cannot necessarily be expected to dissipate over time and actually may increase.

Consistency of Recall for Deployment-Related Traumatic Brain Injury.

OBJECTIVE: To examine the temporal consistency of self-reported deployment-related traumatic brain injury (TBI) and its association with posttraumatic stress disorder (PTSD) symptom severity. SETTING: In-person interviews at US Army installations (postdeployment); phone interviews (long-term follow-up). PARTICIPANTS: A total of 378 US Army soldiers and veterans deployed to Iraq; 14.3% (n = 54) reported TBI with loss of consciousness during an index deployment. DESIGN: Participants were evaluated after returning from deployment and again 5 to 9 years later. MAIN MEASURES: Temporal consistency of TBI endorsement based on TBI screening interviews; PTSD Checklist, Civilian Version. RESULTS: The concordance of deployment-related TBI endorsement from the postdeployment to long-term follow-up assessment was moderate (κ = 0.53). Of the 54 participants reporting (predominantly mild) TBI occurring during an index deployment, 32 endorsed TBI inconsistently over time. More severe PTSD symptoms at postdeployment assessment were independently associated with discordant reporting (P = .0004); each 10-point increase in PCL scores increasing odds of discordance by 69% (odds ratio = 1.69; 95% confidence interval, 1.26-2.26). CONCLUSIONS: Deployment-related TBI may not be reported reliably over time, particularly among war-zone veterans with greater PTSD symptoms. Results of screening evaluations for TBI history should be viewed with caution in the context of PTSD symptom history.

The genetics of functional disability in schizophrenia and bipolar illness: Methods and initial results for VA cooperative study #572.

Given the prominence of cognitive impairments and disability associated with schizophrenia and bipolar disorder, substantial interest has arisen in identifying determinants of the diseases and their features. Genetic variation has been linked to skills that underlie disability ("functional capacity" or FC), highlighting need for understanding of these relationships. We describe the design and methods of a large, multisite, observational study focusing on the genetics of functional disability in schizophrenia and bipolar disorder, presenting initial data on recruitment, and characterization of the sample. Known as Veterans Affairs (VA) Cooperative Studies Program (CSP)#572, this study is recruiting, diagnosing, and assessing U.S. Veterans with either schizophrenia or bipolar I disorder. Assessments include neuropsychological (NP) testing, FC, suicidality, and co-morbid conditions such as posttraumatic stress disorder (PTSD). A sample of "psychiatrically healthy" Veterans from another project serves as a comparison group. An interim total of 8,140 participants (42.1% schizophrenia) have been recruited and assessed as of September 30, 2013, with 9 months of enrollment remaining and with a target sample size of 9,500. Veterans with schizophrenia were more likely to never have married, whereas lifetime PTSD and suicidality were more common in the bipolar veterans. Performance on the FC measures and NP tests was consistent with previous results, with mean t-scores of 35 (-1.5 SD) for schizophrenia and 41 (-0.9 SD) for the bipolar Veterans. This large population is representative of previous studies in terms of patient performance and co-morbidities. Subsequent genomic analyses will examine the genomic correlates of performance-based measures. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.

Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning.

The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 172,814 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, and vaccination status, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.903, 95% Confidence Interval (CI): 0.895, 0.911) followed by hospitalization (AUC = 0.822, CI: 0.818, 0.826), then escalation of care (AUC = 0.793, CI: 0.784, 0.805). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization and death in more than 2 of 5 unvaccinated patients.

Development of a prediction model for 30-day COVID-19 hospitalization and death in a national cohort of Veterans Health Administration patients-March 2022-April 2023.

OBJECTIVE: The epidemiology of COVID-19 has substantially changed since its emergence given the availability of effective vaccines, circulation of different viral variants, and re-infections. We aimed to develop models to predict 30-day COVID-19 hospitalization and death in the Omicron era for contemporary clinical and research applications. METHODS: We used comprehensive electronic health records from a national cohort of patients in the Veterans Health Administration (VHA) who tested positive for SARS-CoV-2 between March 1, 2022, and March 31, 2023. Full models incorporated 84 predictors, including demographics, comorbidities, and receipt of COVID-19 vaccinations and anti-SARS-CoV-2 treatments. Parsimonious models included 19 predictors. We created models for 30-day hospitalization or death, 30-day hospitalization, and 30-day all-cause mortality. We used the Super Learner ensemble machine learning algorithm to fit prediction models. Model performance was assessed with the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration intercepts and slopes in a 20% holdout dataset. RESULTS: Models were trained and tested on 198,174 patients, of whom 8% were hospitalized or died within 30 days of testing positive. AUCs for the full models ranged from 0.80 (hospitalization) to 0.91 (death). Brier scores were close to 0, with the lowest error in the mortality model (Brier score: 0.01). All three models were well calibrated with calibration intercepts <0.23 and slopes <1.05. Parsimonious models performed comparably to full models. CONCLUSIONS: We developed prediction models that accurately estimate COVID-19 hospitalization and mortality risk following emergence of the Omicron variant and in the setting of COVID-19 vaccinations and antiviral treatments. These models may be used for risk stratification to inform COVID-19 treatment and to identify high-risk patients for inclusion in clinical trials.

Metformin prescription for U.S. veterans with prediabetes, 2010-2019.

Affecting an estimated 88 million Americans, prediabetes increases the risk for developing type 2 diabetes mellitus (T2DM), and independently, cardiovascular disease, retinopathy, nephropathy, and neuropathy. Nevertheless, little is known about the use of metformin for diabetes prevention among patients in the Veterans Health Administration, the largest integrated healthcare system in the U.S. This is a retrospective observational cohort study of the proportion of Veterans with incident prediabetes who were prescribed metformin at the Veterans Health Administration from October 2010 to September 2019. Among 1,059,605 Veterans with incident prediabetes, 12,009 (1.1%) were prescribed metformin during an average 3.4 years of observation after diagnosis. Metformin prescribing was marginally higher (1.6%) among those with body mass index (BMI) ≥35 kg/m2, age <60 years, HbA1c≥6.0%, or those with a history of gestational diabetes, all subgroups at a higher risk for progression to T2DM. In a multivariable model, metformin was more likely to be prescribed for those with BMI ≥35 kg/m2 incidence rate ratio [IRR] 2.6 [95% confidence intervals (CI): 2.1-3.3], female sex IRR, 2.4 [95% CI: 1.8-3.3], HbA1c≥6% IRR, 1.93 [95% CI: 1.5-2.4], age <60 years IRR, 1.7 [95% CI: 1.3-2.3], hypertriglyceridemia IRR, 1.5 [95% CI: 1.2-1.9], hypertension IRR, 1.5 [95% CI: 1.1-2.1], Major Depressive Disorder IRR, 1.5 [95% CI: 1.1-2.0], or schizophrenia IRR, 2.1 [95% CI: 1.2-3.8]. Over 20% of Veterans with prediabetes attended a comprehensive structured lifestyle modification clinic or program. Among Veterans with prediabetes, metformin was prescribed to 1.1% overall, a proportion that marginally increased to 1.6% in the subset of individuals at highest risk for progression to T2DM.