Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

BACKGROUND: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.9 for "Post COVID-19 condition, unspecified" to identify patients with long COVID-19 has provided a method of evaluating this condition in EHRs; however, the accuracy of this code is unclear. OBJECTIVE: This study aimed to characterize the utility and accuracy of the U09.9 code across 3 health care systems-the Veterans Health Administration, the Beth Israel Deaconess Medical Center, and the University of Pittsburgh Medical Center-against patients identified with long COVID-19 via a chart review by operationalizing the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) definitions. METHODS: Patients who were COVID-19 positive with either a U07.1 ICD-10 code or positive polymerase chain reaction test within these health care systems were identified for chart review. Among this cohort, we sampled patients based on two approaches: (1) with a U09.9 code and (2) without a U09.9 code but with a new onset long COVID-19-related ICD-10 code, which allows us to assess the sensitivity of the U09.9 code. To operationalize the long COVID-19 definition based on health agency guidelines, symptoms were grouped into a "core" cluster of 11 commonly reported symptoms among patients with long COVID-19 and an extended cluster that captured all other symptoms by disease domain. Patients having ≥2 symptoms persisting for ≥60 days that were new onset after their COVID-19 infection, with ≥1 symptom in the core cluster, were labeled as having long COVID-19 per chart review. The code's performance was compared across 3 health care systems and across different time periods of the pandemic. RESULTS: Overall, 900 patient charts were reviewed across 3 health care systems. The prevalence of long COVID-19 among the cohort with the U09.9 ICD-10 code based on the operationalized WHO definition was between 23.2% and 62.4% across these health care systems. We also evaluated a less stringent version of the WHO definition and the CDC definition and observed an increase in the prevalence of long COVID-19 at all 3 health care systems. CONCLUSIONS: This is one of the first studies to evaluate the U09.9 code against a clinical case definition for long COVID-19, as well as the first to apply this definition to EHR data using a chart review approach on a nationwide cohort across multiple health care systems. This chart review approach can be implemented at other EHR systems to further evaluate the utility and performance of the U09.9 code.

Centralized Interactive Phenomics Resource: an integrated online phenomics knowledgebase for health data users.

OBJECTIVE: Development of clinical phenotypes from electronic health records (EHRs) can be resource intensive. Several phenotype libraries have been created to facilitate reuse of definitions. However, these platforms vary in target audience and utility. We describe the development of the Centralized Interactive Phenomics Resource (CIPHER) knowledgebase, a comprehensive public-facing phenotype library, which aims to facilitate clinical and health services research. MATERIALS AND METHODS: The platform was designed to collect and catalog EHR-based computable phenotype algorithms from any healthcare system, scale metadata management, facilitate phenotype discovery, and allow for integration of tools and user workflows. Phenomics experts were engaged in the development and testing of the site. RESULTS: The knowledgebase stores phenotype metadata using the CIPHER standard, and definitions are accessible through complex searching. Phenotypes are contributed to the knowledgebase via webform, allowing metadata validation. Data visualization tools linking to the knowledgebase enhance user interaction with content and accelerate phenotype development. DISCUSSION: The CIPHER knowledgebase was developed in the largest healthcare system in the United States and piloted with external partners. The design of the CIPHER website supports a variety of front-end tools and features to facilitate phenotype development and reuse. Health data users are encouraged to contribute their algorithms to the knowledgebase for wider dissemination to the research community, and to use the platform as a springboard for phenotyping. CONCLUSION: CIPHER is a public resource for all health data users available at https://phenomics.va.ornl.gov/ which facilitates phenotype reuse, development, and dissemination of phenotyping knowledge.

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.

Development and utility of a clinical research informatics application for participant recruitment and workflow management for a return of results pilot trial in familial hypercholesterolemia in the Million Veteran Program.

Objective: The development of clinical research informatics tools and workflow processes associated with re-engaging biobank participants has become necessary as genomic repositories increasingly consider the return of actionable research results. Materials and Methods: Here we describe the development and utility of an informatics application for participant recruitment and enrollment management for the Veterans Affairs Million Veteran Program Return Of Actionable Results Study, a randomized controlled pilot trial returning individual genetic results associated with familial hypercholesterolemia. Results: The application is developed in Python-Flask and was placed into production in November 2021. The application includes modules for chart review, medication reconciliation, participant contact and biospecimen logging, survey recording, randomization, and documentation of genetic counseling and result disclosure. Three primary users, a genetic counselor and two research coordinators, and 326 Veteran participants have been integrated into the system as of February 23, 2023. The application has successfully handled 3367 task requests involving greater than 95 000 structured data points. Specifically, application users have recorded 326 chart reviews, 867 recruitment telephone calls, 158 telephone-based surveys, and 61 return of results genetic counseling sessions, among other available study tasks. Conclusion: The development of usable, customizable, and secure informatics tools will become increasingly important as large genomic repositories begin to return research results at scale. Our work provides a proof-of-concept for developing and using such tools to aid in managing the return of results process within a national biobank.

The Million Veteran Program 1990-1991 Gulf War Era Survey: An Evaluation of Veteran Response, Characteristics, and Representativeness of the Gulf War Era Veteran Population.

To address gaps in understanding the pathophysiology of Gulf War Illness (GWI), the VA Million Veteran Program (MVP) developed and implemented a survey to MVP enrollees who served in the U.S. military during the 1990-1991 Persian Gulf War (GW). Eligible Veterans were invited via mail to complete a survey assessing health conditions as well as GW-specific deployment characteristics and exposures. We evaluated the representativeness of this GW-era cohort relative to the broader population by comparing demographic, military, and health characteristics between respondents and non-respondents, as well as with all GW-era Veterans who have used Veterans Health Administration (VHA) services and the full population of U.S. GW-deployed Veterans. A total of 109,976 MVP GW-era Veterans were invited to participate and 45,270 (41%) returned a completed survey. Respondents were 84% male, 72% White, 8% Hispanic, with a mean age of 61.6 years (SD = 8.5). Respondents were more likely to be older, White, married, better educated, slightly healthier, and have higher socioeconomic status than non-respondents, but reported similar medical conditions and comparable health status. Although generally similar to all GW-era Veterans using VHA services and the full population of U.S. GW Veterans, respondents included higher proportions of women and military officers, and were slightly older. In conclusion, sample characteristics of the MVP GW-era cohort can be considered generally representative of the broader GW-era Veteran population. The sample represents the largest research cohort of GW-era Veterans established to date and provides a uniquely valuable resource for conducting in-depth studies to evaluate health conditions affecting 1990-1991 GW-era Veterans.

Overview of Efforts to Increase Women Enrollment in the Veterans Affairs Million Veteran Program.

Background: Ensuring enhanced delivery of care to women Veterans is a top Veterans Affairs (VA) priority; however, women are historically underrepresented in research that informs evidence-based health care. A primary barrier to women's participation is the inability to engage with research in person due to a number of documented challenges. The VA Million Veteran Program (MVP) is committed to increasing access for women Veterans to participate in research, thereby better understanding conditions specific to this population and how disease manifests differently in women compared to men. The goal of this work is to describe the results of the MVP Women's Campaign, an effort designed to increase outreach to and awareness of remote enrollment options for women Veterans. Materials and Methods: The MVP Women's Campaign launched two phases between March 2021 and April 2022: the Multimedia Phase leveraged a variety of strategic multichannel communication tactics and the Email Phase focused on direct email communication to women Veterans. The effect of the Multimedia Phase was determined using t-tests and chi-square tests, as well as logistic regression models to compare demographic subgroups. The Email Phase was evaluated using comparisons of the enrollment rate across demographic groups through a multivariate adjusted logistic regression model. Results: Overall, 4694 women Veterans enrolled during the MVP Women's Campaign (54% during the Multimedia Phase and 46% during the Email Phase). For the Multimedia Phase, the percentage of older women online enrollees increased, along with women from the southwest and western regions of the United States. Differences for women Veteran online enrollment across different ethnicity and race groups were not observed. During the Email Phase, the enrollment rate increased with age. Compared to White women Veterans, Blacks, Asians, and Native Americans were significantly less likely to enroll while Veterans with multiple races were more likely to enroll. Conclusion: The MVP Women's Campaign is the first large-scale outreach effort focusing on recruitment of women Veterans into MVP. The combination of print and digital outreach tactics and direct email recruitment resulted in over a fivefold increase in women Veteran enrollees during a 7-month period. Attention to messaging and communication channels, combined with a better understanding of effective recruitment methods for certain Veteran populations, allows MVP the opportunity to advance health and health care not only for women Veterans, but beyond. Lessons learned will be applied to increase other populations in MVP such as Blacks, Hispanics, Asians, Native Americans, younger Veterans, and Veterans with certain health conditions.

Framework of the Centralized Interactive Phenomics Resource (CIPHER) standard for electronic health data-based phenomics knowledgebase.

The development of phenotypes using electronic health records is a resource-intensive process. Therefore, the cataloging of phenotype algorithm metadata for reuse is critical to accelerate clinical research. The Department of Veterans Affairs (VA) has developed a standard for phenotype metadata collection which is currently used in the VA phenomics knowledgebase library, CIPHER (Centralized Interactive Phenomics Resource), to capture over 5000 phenotypes. The CIPHER standard improves upon existing phenotype library metadata collection by capturing the context of algorithm development, phenotyping method used, and approach to validation. While the standard was iteratively developed with VA phenomics experts, it is applicable to the capture of phenotypes across healthcare systems. We describe the framework of the CIPHER standard for phenotype metadata collection, the rationale for its development, and its current application to the largest healthcare system in the United States.

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

Objective: 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 mental and physical health problems, prior pharmacological treatments, and aggregate genetic factors has potential to inform risk stratification and mitigation strategies. Methods: In this study of 3,942 SCZ and 5,414 BPI patients receiving VA care, self-reported SB and ideation were assessed using the Columbia Suicide Severity Rating Scale (C-SSRS). These cross-sectional data were integrated with electronic health records (EHR), and compared by lifetime diagnoses, treatment histories, follow-up screenings, and mortality data. Polygenic scores (PGS) for traits related to psychiatric disorders, substance use, and cognition were constructed using available genomic data, and exploratory genome-wide association studies were performed to identify and prioritize specific loci. Results: Only 20% of veterans who self-reported SB had a corroborating ICD-9/10 code in their EHR; and among those who denied prior behaviors, more than 20% reported new-onset SB at follow-up. SB were associated with a range of psychiatric and non-psychiatric diagnoses, and with treatment with specific classes of psychotropic medications (e.g., antidepressants, antipsychotics, etc.). PGS for externalizing behaviors, smoking, suicide attempt, and major depressive disorder were also associated with attempt and ideation. Conclusions: Among individuals with a diagnosed mental illness, a GWAS for SB did not yield any significant loci. Self-reported SB were strongly associated with clinical variables across several EHR domains. Overall, clinical and polygenic analyses point to sequelae of substance-use related behaviors and other psychiatric comorbidities as strong correlates of prior and subsequent SB. Nonetheless, past SB was frequently not documented in clinical settings, underscoring the value of regular screening based on direct, in-person assessments, especially among high-risk individuals.

Pharmacogenetic allele variant frequencies: An analysis of the VA's Million Veteran Program (MVP) as a representation of the diversity in US population.

We present allele frequencies of pharmacogenomics relevant variants across multiple ancestry in a sample representative of the US population. We analyzed 658,582 individuals with genotype data and extracted pharmacogenomics relevant single nucleotide variant (SNV) alleles, human leukocyte antigens (HLA) 4-digit alleles and an important copy number variant (CNV), the full deletion/duplication of CYP2D6. We compiled distinct allele frequency tables for European, African American, Hispanic, and Asian ancestry individuals. In addition, we compiled allele frequencies based on local ancestry reconstruction in the African-American (2-way deconvolution) and Hispanic (3-way deconvolution) cohorts.

Leveraging the Million Veteran Program Infrastructure and Data for a Rapid Research Response to COVID-19.

Background: The Veterans Health Administration Office of Research and Development (ORD) played a key role in the federal government's response to the COVID-19 pandemic. The ORD effectively leveraged existing resources to answer questions related to the SARS-CoV-2 virus and COVID-19. Observations: When the COVID-19 pandemic hit in 2020, the Million Veteran Program (MVP), one of the largest genomic cohorts in the world, extended the centralized recruitment and enrollment infrastructure to develop a COVID-19 research volunteer registry to assist enrollment in the vaccine and treatment trials in which the US Department of Veterans Affairs (VA) participated. In addition, the MVP allowed for new data collection and a large genomic cohort to understand host contributions to COVID-19. This article describes ways the MVP contributed to the VA's rapid research response to COVID-19. Several host genetic factors believed to play a role in the development and severity of COVID-19 were identified. Furthermore, existing MVP partnerships with other federal agencies, particularly with the Department of Energy, were leveraged to improve understanding and management of COVID-19. Conclusions: A previously established enterprise approach and research infrastructure were essential to the VA's successful and timely COVID-19 research response. This infrastructure not only supported rapid recruitment in vaccine and treatment trials, but also leveraged the unique MVP and VA electronic health record data to drive rapid scientific discovery and inform clinical operations. Extending the models that VA research applied to the federal government at large and establishing centralized resources for shared or federated data analyses across federal agencies will better equip the nation to respond to future public health crises.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression Among Adults in the US Veterans Affairs Health Care System.

Importance: Serious mental illnesses, including schizophrenia, bipolar disorder, and depression, are heritable, highly multifactorial disorders and major causes of disability worldwide. Objective: To benchmark the penetrance of current neuropsychiatric polygenic risk scores (PRSs) in the Veterans Health Administration health care system and to explore associations between PRS and broad categories of human disease via phenome-wide association studies. Design, Setting, and Participants: Extensive Veterans Health Administration's electronic health records were assessed from October 1999 to January 2021, and an embedded cohort of 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder were found. The performance of schizophrenia, bipolar disorder, and major depression PRSs were compared in participants of African or European ancestry in the Million Veteran Program (approximately 400 000 individuals), and associations between PRSs and 1650 disease categories based on ICD-9/10 billing codes were explored. Last, genomic structural equation modeling was applied to derive novel PRSs indexing common and disorder-specific genetic factors. Analysis took place from January 2021 to January 2022. Main Outcomes and Measures: Diagnoses based on in-person structured clinical interviews were compared with ICD-9/10 billing codes. PRSs were constructed using summary statistics from genome-wide association studies of schizophrenia, bipolar disorder, and major depression. Results: Of 707 299 enrolled study participants, 459 667 were genotyped at the time of writing; 84 806 were of broadly African ancestry (mean [SD] age, 58 [12.1] years) and 314 909 were of broadly European ancestry (mean [SD] age, 66.4 [13.5] years). Among 9378 individuals with confirmed diagnoses of schizophrenia or bipolar 1 disorder, 8962 (95.6%) were correctly identified using ICD-9/10 codes (2 or more). Among those of European ancestry, PRSs were robustly associated with having received a diagnosis of schizophrenia (odds ratio [OR], 1.81 [95% CI, 1.76-1.87]; P < 10-257) or bipolar disorder (OR, 1.42 [95% CI, 1.39-1.44]; P < 10-295). Corresponding effect sizes in participants of African ancestry were considerably smaller for schizophrenia (OR, 1.35 [95% CI, 1.29-1.42]; P < 10-38) and bipolar disorder (OR, 1.16 [95% CI, 1.11-1.12]; P < 10-10). Neuropsychiatric PRSs were associated with increased risk for a range of psychiatric and physical health problems. Conclusions and Relevance: Using diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRSs, the validity of an electronic health records-based phenotyping approach in US veterans was demonstrated, highlighting the potential of PRSs for disentangling biological and mediated pleiotropy.

Effect of Pharmacogenomic Testing for Drug-Gene Interactions on Medication Selection and Remission of Symptoms in Major Depressive Disorder: The PRIME Care Randomized Clinical Trial.

Importance: Selecting effective antidepressants for the treatment of major depressive disorder (MDD) is an imprecise practice, with remission rates of about 30% at the initial treatment. Objective: To determine whether pharmacogenomic testing affects antidepressant medication selection and whether such testing leads to better clinical outcomes. Design, Setting, and Participants: A pragmatic, randomized clinical trial that compared treatment guided by pharmacogenomic testing vs usual care. Participants included 676 clinicians and 1944 patients. Participants were enrolled from 22 Department of Veterans Affairs medical centers from July 2017 through February 2021, with follow-up ending November 2021. Eligible patients were those with MDD who were initiating or switching treatment with a single antidepressant. Exclusion criteria included an active substance use disorder, mania, psychosis, or concurrent treatment with a specified list of medications. Interventions: Results from a commercial pharmacogenomic test were given to clinicians in the pharmacogenomic-guided group (n = 966). The comparison group received usual care and access to pharmacogenomic results after 24 weeks (n = 978). Main Outcomes and Measures: The co-primary outcomes were the proportion of prescriptions with a predicted drug-gene interaction written in the 30 days after randomization and remission of depressive symptoms as measured by the Patient Health Questionnaire-9 (PHQ-9) (remission was defined as PHQ-9 ≤ 5). Remission was analyzed as a repeated measure across 24 weeks by blinded raters. Results: Among 1944 patients who were randomized (mean age, 48 years; 491 women [25%]), 1541 (79%) completed the 24-week assessment. The estimated risks for receiving an antidepressant with none, moderate, and substantial drug-gene interactions for the pharmacogenomic-guided group were 59.3%, 30.0%, and 10.7% compared with 25.7%, 54.6%, and 19.7% in the usual care group. The pharmacogenomic-guided group was more likely to receive a medication with a lower potential drug-gene interaction for no drug-gene vs moderate/substantial interaction (odds ratio [OR], 4.32 [95% CI, 3.47 to 5.39]; P < .001) and no/moderate vs substantial interaction (OR, 2.08 [95% CI, 1.52 to 2.84]; P = .005) (P < .001 for overall comparison). Remission rates over 24 weeks were higher among patients whose care was guided by pharmacogenomic testing than those in usual care (OR, 1.28 [95% CI, 1.05 to 1.57]; P = .02; risk difference, 2.8% [95% CI, 0.6% to 5.1%]) but were not significantly higher at week 24 when 130 patients in the pharmacogenomic-guided group and 126 patients in the usual care group were in remission (estimated risk difference, 1.5% [95% CI, -2.4% to 5.3%]; P = .45). Conclusions and Relevance: Among patients with MDD, provision of pharmacogenomic testing for drug-gene interactions reduced prescription of medications with predicted drug-gene interactions compared with usual care. Provision of test results had small nonpersistent effects on symptom remission. Trial Registration: ClinicalTrials.gov Identifier: NCT03170362.

Impact of Coronavirus Disease 2019 (COVID-19) Severity on Long-term Events in United States Veterans Using the Veterans Affairs Severity Index for COVID-19 (VASIC).

In this retrospective cohort study of 94 595 severe acute respiratory syndrome coronavirus 2-positive cases, we developed and validated an algorithm to assess the association between coronavirus disease 2019 (COVID-19) severity and long-term complications (stroke, myocardial infarction, pulmonary embolism/deep vein thrombosis, heart failure, and mortality). COVID-19 severity was associated with a greater risk of experiencing a long-term complication 31-120 days postinfection. Most incident events occurred 31-60 days postinfection and diminished after day 91, except heart failure for severe patients and death for moderate patients, which peaked on days 91-120. Understanding the differential impact of COVID-19 severity on long-term events provides insight into possible intervention modalities and critical prevention strategies.

Million Veteran Program's response to COVID-19: Survey development and preliminary findings.

BACKGROUND: In response to the novel Coronavirus Disease 2019 (COVID-19) pandemic, the Department of Veterans Affairs (VA) Million Veteran Program (MVP) organized efforts to better understand the impact of COVID-19 on Veterans by developing and deploying a self-reported survey. METHODS: The MVP COVID-19 Survey was developed to collect COVID-19 specific elements including symptoms, diagnosis, hospitalization, behavioral and psychosocial factors and to augment existing MVP data with longitudinal collection of key domains in physical and mental health. Due to the rapidly evolving nature of the pandemic, a multipronged strategy was implemented to widely disseminate the COVID-19 Survey and capture data using both the online platform and mailings. RESULTS: We limited the findings of this paper to the initial phase of survey dissemination which began in May 2020. A total of 729,625 eligible MVP Veterans were invited to complete version 1 of the COVID-19 Survey. As of October 31, 2020, 58,159 surveys have been returned. The mean and standard deviation (SD) age of responders was 71 (11) years, 8.6% were female, 8.2% were Black, 5.6% were Hispanic, and 446 (0.8%) self-reported a COVID-19 diagnosis. Over 90% of responders reported wearing masks, practicing social distancing, and frequent hand washing. CONCLUSION: The MVP COVID-19 Survey provides a systematic collection of data regarding COVID-19 behaviors among Veterans and represents one of the first large-scale, national surveillance efforts of COVID-19 in the Veteran population. Continued work will examine the overall response to the survey with comparison to available VA health record data.

Pharmacoepidemiology, Machine Learning, and COVID-19: An Intent-to-Treat Analysis of Hydroxychloroquine, With or Without Azithromycin, and COVID-19 Outcomes Among Hospitalized US Veterans.

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score-adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ.

Phenome-wide association of 1809 phenotypes and COVID-19 disease progression in the Veterans Health Administration Million Veteran Program.

BACKGROUND: The risk factors associated with the stages of Coronavirus Disease-2019 (COVID-19) disease progression are not well known. We aim to identify risk factors specific to each state of COVID-19 progression from SARS-CoV-2 infection through death. METHODS AND RESULTS: We included 648,202 participants from the Veteran Affairs Million Veteran Program (2011-). We identified characteristics and 1,809 ICD code-based phenotypes from the electronic health record. We used logistic regression to examine the association of age, sex, body mass index (BMI), race, and prevalent phenotypes to the stages of COVID-19 disease progression: infection, hospitalization, intensive care unit (ICU) admission, and 30-day mortality (separate models for each). Models were adjusted for age, sex, race, ethnicity, number of visit months and ICD codes, state infection rate and controlled for multiple testing using false discovery rate (≤0.1). As of August 10, 2020, 5,929 individuals were SARS-CoV-2 positive and among those, 1,463 (25%) were hospitalized, 579 (10%) were in ICU, and 398 (7%) died. We observed a lower risk in women vs. men for ICU and mortality (Odds Ratio (95% CI): 0.48 (0.30-0.76) and 0.59 (0.31-1.15), respectively) and a higher risk in Black vs. Other race patients for hospitalization and ICU (OR (95%CI): 1.53 (1.32-1.77) and 1.63 (1.32-2.02), respectively). We observed an increased risk of all COVID-19 disease states with older age and BMI ≥35 vs. 20-24 kg/m2. Renal failure, respiratory failure, morbid obesity, acid-base balance disorder, white blood cell diseases, hydronephrosis and bacterial infections were associated with an increased risk of ICU admissions; sepsis, chronic skin ulcers, acid-base balance disorder and acidosis were associated with mortality. CONCLUSIONS: Older age, higher BMI, males and patients with a history of respiratory, kidney, bacterial or metabolic comorbidities experienced greater COVID-19 severity. Future studies to investigate the underlying mechanisms associated with these phenotype clusters and COVID-19 are warranted.

Actionable druggable genome-wide Mendelian randomization identifies repurposing opportunities for COVID-19.

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2, P = 1.6 × 10-6; IFNAR2, P = 9.8 × 10-11 and IL-10RB, P = 2.3 × 10-14) using cis-expression quantitative trait loci genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared expression quantitative trait loci signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.