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.

Clinical Interviewing: An Essential but Neglected Method of Medicine.

Clinical interviewing is the basic method to understand how a person feels and what are the presenting complaints, obtain medical history, evaluate personal attitudes and behavior related to health and disease, give the patient information about diagnosis, prognosis, and treatment, and establish a bond between patient and physician that is crucial for shared decision making and self-management. However, the value of this basic skill is threatened by time pressures and emphasis on technology. Current health care trends privilege expensive tests and procedures and tag the time devoted to interaction with the patient as lacking cost-effectiveness. Instead, the time spent to inquire about problems and life setting may actually help to avoid further testing, procedures, and referrals. Moreover, the dialogue between patient and physician is an essential instrument to increase patient's motivation to engage in healthy behavior. The aim of this paper was to provide an overview of clinical interviewing and its optimal use in relation to style, flow and hypothesis testing, clinical domains, modifications according to settings and goals, and teaching. This review points to the primacy of interviewing in the clinical process. The quality of interviewing determines the quality of data that are collected and, eventually, of assessment and treatment. Thus, interviewing deserves more attention in educational training and more space in clinical encounters than it is currently receiving.

Genotyping Array Design and Data Quality Control in the Million Veteran Program.

The Million Veteran Program (MVP), initiated by the Department of Veterans Affairs (VA), aims to collect biosamples with consent from at least one million veterans. Presently, blood samples have been collected from over 800,000 enrolled participants. The size and diversity of the MVP cohort, as well as the availability of extensive VA electronic health records, make it a promising resource for precision medicine. MVP is conducting array-based genotyping to provide a genome-wide scan of the entire cohort, in parallel with whole-genome sequencing, methylation, and other 'omics assays. Here, we present the design and performance of the MVP 1.0 custom Axiom array, which was designed and developed as a single assay to be used across the multi-ethnic MVP cohort. A unified genetic quality-control analysis was developed and conducted on an initial tranche of 485,856 individuals, leading to a high-quality dataset of 459,777 unique individuals. 668,418 genetic markers passed quality control and showed high-quality genotypes not only on common variants but also on rare variants. We confirmed that, with non-European individuals making up nearly 30%, MVP's substantial ancestral diversity surpasses that of other large biobanks. We also demonstrated the quality of the MVP dataset by replicating established genetic associations with height in European Americans and African Americans ancestries. This current dataset has been made available to approved MVP researchers for genome-wide association studies and other downstream analyses. Further data releases will be available for analysis as recruitment at the VA continues and the cohort expands both in size and diversity.

Evaluating and improving real-world evidence with Targeted Learning.

BACKGROUND: The Targeted Learning roadmap provides a systematic guide for generating and evaluating real-world evidence (RWE). From a regulatory perspective, RWE arises from diverse sources such as randomized controlled trials that make use of real-world data, observational studies, and other study designs. This paper illustrates a principled approach to assessing the validity and interpretability of RWE. METHODS: We applied the roadmap to a published observational study of the dose-response association between ritodrine hydrochloride and pulmonary edema among women pregnant with twins in Japan. The goal was to identify barriers to causal effect estimation beyond unmeasured confounding reported by the study's authors, and to explore potential options for overcoming the barriers that robustify results. RESULTS: Following the roadmap raised issues that led us to formulate alternative causal questions that produced more reliable, interpretable RWE. The process revealed a lack of information in the available data to identify a causal dose-response curve. However, under explicit assumptions the effect of treatment with any amount of ritodrine versus none, albeit a less ambitious parameter, can be estimated from data. CONCLUSIONS: Before RWE can be used in support of clinical and regulatory decision-making, its quality and reliability must be systematically evaluated. The TL roadmap prescribes how to carry out a thorough, transparent, and realistic assessment of RWE. We recommend this approach be a routine part of any decision-making process.

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.

Emulation of Randomized Clinical Trials With Nonrandomized Database Analyses: Results of 32 Clinical Trials.

Importance: Nonrandomized studies using insurance claims databases can be analyzed to produce real-world evidence on the effectiveness of medical products. Given the lack of baseline randomization and measurement issues, concerns exist about whether such studies produce unbiased treatment effect estimates. Objective: To emulate the design of 30 completed and 2 ongoing randomized clinical trials (RCTs) of medications with database studies using observational analogues of the RCT design parameters (population, intervention, comparator, outcome, time [PICOT]) and to quantify agreement in RCT-database study pairs. Design, Setting, and Participants: New-user cohort studies with propensity score matching using 3 US claims databases (Optum Clinformatics, MarketScan, and Medicare). Inclusion-exclusion criteria for each database study were prespecified to emulate the corresponding RCT. RCTs were explicitly selected based on feasibility, including power, key confounders, and end points more likely to be emulated with real-world data. All 32 protocols were registered on ClinicalTrials.gov before conducting analyses. Emulations were conducted from 2017 through 2022. Exposures: Therapies for multiple clinical conditions were included. Main Outcomes and Measures: Database study emulations focused on the primary outcome of the corresponding RCT. Findings of database studies were compared with RCTs using predefined metrics, including Pearson correlation coefficients and binary metrics based on statistical significance agreement, estimate agreement, and standardized difference. Results: In these highly selected RCTs, the overall observed agreement between the RCT and the database emulation results was a Pearson correlation of 0.82 (95% CI, 0.64-0.91), with 75% meeting statistical significance, 66% estimate agreement, and 75% standardized difference agreement. In a post hoc analysis limited to 16 RCTs with closer emulation of trial design and measurements, concordance was higher (Pearson r, 0.93; 95% CI, 0.79-0.97; 94% meeting statistical significance, 88% estimate agreement, 88% standardized difference agreement). Weaker concordance occurred among 16 RCTs for which close emulation of certain design elements that define the research question (PICOT) with data from insurance claims was not possible (Pearson r, 0.53; 95% CI, 0.00-0.83; 56% meeting statistical significance, 50% estimate agreement, 69% standardized difference agreement). Conclusions and Relevance: Real-world evidence studies can reach similar conclusions as RCTs when design and measurements can be closely emulated, but this may be difficult to achieve. Concordance in results varied depending on the agreement metric. Emulation differences, chance, and residual confounding can contribute to divergence in results and are difficult to disentangle.

Deintensification or No Statin Treatment Is Associated With Higher Mortality in Patients With Ischemic Stroke or Transient Ischemic Attack.

Background and Purpose: Practice guidelines recommend that most patients receive moderate- or high-potency statins after ischemic stroke or transient ischemic attack (TIA) of atherosclerotic origin. We tested the association of different patterns of potency for prescribed statin therapy­assessed before admission and at hospital discharge for ischemic stroke or TIA­on mortality in a large, nationwide sample of US Veterans. Methods: The study population included patients with an ischemic stroke or TIA occurring during 2011 at any of the 134 Veterans Health Administration facilities. We used electronic outpatient pharmacy files to identify statin dose at hospital admission and within 7 days after hospital discharge. We categorized statin dosing as low, moderate, or high potency; moderate or high potency was considered at goal. We created 6 mutually exclusive groups to reflect patterns of statin potency from hospital admission to discharge: goal to goal, low to goal, goal to low or goal to none (deintensification), none to none, none to low, and low to low. We used logistic regression to compare 30-day and 1-year mortality across statin potency groups. Results: The population included 9380 predominately White (71.1%) men (96.3%) who were hospitalized for stroke or TIA. In this sample, 34.1% of patients (n=3194) were discharged off a statin medication. Deintensification occurred in 14.0% of patients (n=1312) and none to none in 20.5% (n=1924). Deintensification and none to none were associated with a higher odds of mortality as compared with goal to goal (adjusted odds ratio 1-year mortality: deintensification versus goal to goal, 1.26 [95% CI, 1.02­1.57]; none to none versus goal to goal, 1.59 [95% CI, 1.30­1.93]). Adjustments for differences in baseline characteristics using propensity weighted scores demonstrated similar results. Conclusions: Underutilization of statins, including no treatment or underdosing after stroke (deintensification), was observed in approximately one-third of veterans with ischemic stroke or TIA and was associated with higher mortality when compared with patients who were at goal for statin prescription dosing.

Clinimetric Criteria for Patient-Reported Outcome Measures.

Patient-reported outcome measures (PROMs) are self-rated scales and indices developed to improve the detection of the patients' subjective experience. Given that a considerable number of PROMs are available, it is important to evaluate their validity and usefulness in a specific research or clinical setting. Published guidelines, based on psychometric criteria, do not fit in with the complexity of clinical challenges, because of their quest for homogeneity of components and inadequate attention to sensitivity. Psychometric theory has stifled the field and led to the routine use of scales widely accepted yet with a history of poor performance. Clinimetrics, the science of clinical measurements, may provide a more suitable conceptual and methodological framework. The aims of this paper are to outline the major limitations of the psychometric model and to provide criteria for clinimetric patient-reported outcome measures (CLIPROMs). The characteristics related to reliability, sensitivity, validity, and clinical utility of instruments are critically reviewed, with particular reference to the differences between clinimetric and psychometric approaches. Of note is the fact that PROMs, rating scales, and indices developed according to psychometric criteria may display relevant clinimetric properties. The present paper underpins the importance of the clini-metric methodology in choosing the appropriate PROMs. CLIPROM criteria may also guide the development of new indices and the validation of existing PROMs to be employed in clinical settings.

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.

Association of APOL1 Risk Alleles With Cardiovascular Disease in Blacks in the Million Veteran Program.

BACKGROUND: Approximately 13% of black individuals carry 2 copies of the apolipoprotein L1 (APOL1) risk alleles G1 or G2, which are associated with 1.5- to 2.5-fold increased risk of chronic kidney disease. There have been conflicting reports as to whether an association exists between APOL1 risk alleles and cardiovascular disease (CVD) that is independent of the effects of APOL1 on kidney disease. We sought to test the association of APOL1 G1/G2 alleles with coronary artery disease, peripheral artery disease, and stroke among black individuals in the Million Veteran Program. METHODS: We performed a time-to-event analysis of retrospective electronic health record data using Cox proportional hazard and competing-risks Fine and Gray subdistribution hazard models. The primary exposure was APOL1 risk allele status. The primary outcome was incident coronary artery disease among individuals without chronic kidney disease during the 12.5-year follow-up period. We separately analyzed the cross-sectional association of APOL1 risk allele status with lipid traits and 115 cardiovascular diseases using phenome-wide association. RESULTS: Among 30 903 black Million Veteran Program participants, 3941 (13%) carried the 2 APOL1 risk allele high-risk genotype. Individuals with normal kidney function at baseline with 2 risk alleles had slightly higher risk of developing coronary artery disease compared with those with no risk alleles (hazard ratio, 1.11 [95% CI, 1.01-1.21]; P=0.039). Similarly, modest associations were identified with incident stroke (hazard ratio, 1.20 [95% CI, 1.05-1.36; P=0.007) and peripheral artery disease (hazard ratio, 1.15 [95% CI, 1.01-1.29l; P=0.031). When both cardiovascular and renal outcomes were modeled, APOL1 was strongly associated with incident renal disease, whereas no significant association with the CVD end points could be detected. Cardiovascular phenome-wide association analyses did not identify additional significant associations with CVD subsets. CONCLUSIONS: APOL1 risk variants display a modest association with CVD, and this association is likely mediated by the known APOL1 association with chronic kidney disease.

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.

The Biological and Biographical Basis of Precision Medicine.

Construction of a patient narrative (case history) is a core strategy in the care of patients. Recent advances in biomarker identification and digital sensors to monitor physiological and behavioral features have made constructing a case history more complex. Notably, however, although a biological profile is increasingly a part of the patient's profile, an analogous patient-based biographical (life experience) profile is typically overlooked. Evolving concepts such as allostasis and allostatic load refer to processes promoting stability of physiological systems in the presence of diverse life experiences. Integrating details of both biology and biography is a goal of "precision medicine." In this review, we describe how complex interactions between biology and biography affect disease risk and treatment response and highlight a strategy to develop narratives that establish the integration of biology and biography as the scientific basis for precision medicine.

Using DNA methylation to validate an electronic medical record phenotype for smoking.

A validated, scalable approach to characterizing (phenotyping) smoking status is needed to facilitate genetic discovery. Using established DNA methylation sites from blood samples as a criterion standard for smoking behavior, we compare three candidate electronic medical record (EMR) smoking metrics based on longitudinal EMR text notes. With data from the Veterans Aging Cohort Study (VACS), we employed a validated algorithm to translate each smoking-related text note into current, past or never categories. We compared three alternative summary characterizations of smoking: most recent, modal and trajectories using descriptive statistics and Spearman's correlation coefficients. Logistic regression and area under the curve analyses were used to compare the associations of these phenotypes with the DNA methylation sites, cg05575921 and cg03636183, which are known to have strong associations with current smoking. DNA methylation data were available from the VACS Biomarker Cohort (VACS-BC), a sub-study of VACS. We also considered whether the associations differed by the certainty of trajectory group assignment (<0.80/≥0.80). Among 140 152 VACS participants, EMR summary smoking phenotypes varied in frequency by the metric chosen: current from 33 to 53 percent; past from 16 to 24 percent and never from 24 to 33 percent. The association between the EMR smoking pairs was highest for modal and trajectories (rho = 0.89). Among 728 individuals in the VACS-BC, both DNA methylation sites were associated with all three EMR summary metrics (p < 0.001), but the strongest association with both methylation sites was observed for trajectories (p < 0.001). Longitudinal EMR smoking data support using a summary phenotype, the validity of which is enhanced when data are integrated into statistical trajectories.

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/).

Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea.

BACKGROUND: Obstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes. METHODS: Cross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death. RESULTS: Seven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk. CONCLUSIONS: Among patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.

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).