Neurological diagnoses in hospitalized COVID-19 patients associated with adverse outcomes: A multinational cohort study.

Few studies examining the patient outcomes of concurrent neurological manifestations during acute COVID-19 leveraged multinational cohorts of adults and children or distinguished between central and peripheral nervous system (CNS vs. PNS) involvement. Using a federated multinational network in which local clinicians and informatics experts curated the electronic health records data, we evaluated the risk of prolonged hospitalization and mortality in hospitalized COVID-19 patients from 21 healthcare systems across 7 countries. For adults, we used a federated learning approach whereby we ran Cox proportional hazard models locally at each healthcare system and performed a meta-analysis on the aggregated results to estimate the overall risk of adverse outcomes across our geographically diverse populations. For children, we reported descriptive statistics separately due to their low frequency of neurological involvement and poor outcomes. Among the 106,229 hospitalized COVID-19 patients (104,031 patients ≥18 years; 2,198 patients <18 years, January 2020-October 2021), 15,101 (14%) had at least one CNS diagnosis, while 2,788 (3%) had at least one PNS diagnosis. After controlling for demographics and pre-existing conditions, adults with CNS involvement had longer hospital stay (11 versus 6 days) and greater risk of (Hazard Ratio = 1.78) and faster time to death (12 versus 24 days) than patients with no neurological condition (NNC) during acute COVID-19 hospitalization. Adults with PNS involvement also had longer hospital stay but lower risk of mortality than the NNC group. Although children had a low frequency of neurological involvement during COVID-19 hospitalization, a substantially higher proportion of children with CNS involvement died compared to those with NNC (6% vs 1%). Overall, patients with concurrent CNS manifestation during acute COVID-19 hospitalization faced greater risks for adverse clinical outcomes than patients without any neurological diagnosis. Our global informatics framework using a federated approach (versus a centralized data collection approach) has utility for clinical discovery beyond COVID-19.

Is the Structure of "Regular Working Days" about to Dissolve? Changes in Employees' Time-Use Patterns Working in Enterprise Collaboration Systems During the COVID-19 Pandemic.

The measures taken during the COVID-19 pandemic have strengthened Work from Home and the use of digital communication technologies and digital collaboration. The resulting flexibility in terms of control over time and place of work can support workers in improving their work-life balance. In this paper, we investigate whether workers using digital communication technologies made use of this flexibility and changed the distribution of their work hours across the week and day. To answer this question, we investigate the use of an Enterprise Collaboration System in 2019-2022. Using real-world data, a MS Power BI dashboard was created following the steps suggested by the Social Collaboration Analytics Framework (SCAF). The dashboard shows that the system use increased during the COVID-19 pandemic. Furthermore, an expansion of work on weekends and outside of regular working hours can be observed. Although, timely adjustments of use-patterns were highest in the beginning of the pandemic, long-term trends could be observed as well. The results indicate that knowledge workers used digital collaboration technologies to restructure working days, according to their specific personal and business needs while working from home. The paper is a valuable starting point in the context of a long-term interdisciplinary research project on the digitalisation of the workplace.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

PURPOSE: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population. METHODS: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE). To identify the risk factors associated with ARDS, we compared young patients with and without ARDS through a federated analysis. We further compared the outcomes between young and old patients with ARDS. RESULTS: Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%). CONCLUSION: Trough an innovative method, a large international cohort study of young adults developing ARDS after SARS-CoV-2 infection has been gather. It demonstrated the poor outcomes of this population and associated risk factor.

Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses.

BACKGROUND: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million person-years of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25 917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eGFR values <60 or >105 mL·min-1·1.73 m-2, compared with those with eGFR between 60 and 105 mL·min-1·1.73 m-2. Mendelian randomization analyses for CHD showed an association among participants with eGFR <60 mL·min-1·1.73 m-2, with a 14% (95% CI, 3%-27%) higher CHD risk per 5 mL·min-1·1.73 m-2 lower genetically predicted eGFR, but not for those with eGFR >105 mL·min-1·1.73 m-2. Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin A1c, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function.

SurvMaximin: Robust federated approach to transporting survival risk prediction models.

OBJECTIVE: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

International electronic health record-derived post-acute sequelae profiles of COVID-19 patients.

The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09-1.55), heart failure (RR 1.22, 95% CI 1.10-1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07-1.31), and fatigue (RR 1.18, 95% CI 1.07-1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58-2.76), venous embolism (RR 1.34, 95% CI 1.17-1.54), atrial fibrillation (RR 1.30, 95% CI 1.13-1.50), type 2 diabetes (RR 1.26, 95% CI 1.16-1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09-1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90-3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21-2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04-1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.

Transmission from vaccinated individuals in a large SARS-CoV-2 Delta variant outbreak.

An outbreak of over 1,000 COVID-19 cases in Provincetown, Massachusetts (MA), in July 2021-the first large outbreak mostly in vaccinated individuals in the US-prompted a comprehensive public health response, motivating changes to national masking recommendations and raising questions about infection and transmission among vaccinated individuals. To address these questions, we combined viral genomic and epidemiological data from 467 individuals, including 40% of outbreak-associated cases. The Delta variant accounted for 99% of cases in this dataset; it was introduced from at least 40 sources, but 83% of cases derived from a single source, likely through transmission across multiple settings over a short time rather than a single event. Genomic and epidemiological data supported multiple transmissions of Delta from and between fully vaccinated individuals. However, despite its magnitude, the outbreak had limited onward impact in MA and the US overall, likely due to high vaccination rates and a robust public health response.

A Workspace Typology for Enterprise Collaboration Systems.

The global COVID-19 pandemic and the need for organisations to provide digital support for work-from-anywhere has put collaboration software into the centre of attention for IT managers. In this paper we examine (self-managed) workspaces in (integrated) Enterprise Collaboration Systems (ECS) that provide the environment for asynchronous communication and exchange of information. Our aim is to better understand how employees use the ECS to support their work. Based on a structured literature review and an in-depth case study of an ECS user company we developed a generic typology of workspaces containing three main categories (community, team and non-work-related) and 5 different types of workspaces. The types are characterised by their purpose, characteristics and possible metrics for their identification. The findings contribute to our understanding of collaborative user activity in enterprise collaboration environments and provide the basis for Social Collaboration Analytics.

Multistate Outbreak of SARS-CoV-2 Infections, Including Vaccine Breakthrough Infections, Associated with Large Public Gatherings, United States.

During July 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.617.2 variant infections, including vaccine breakthrough infections, occurred after large public gatherings in Provincetown, Massachusetts, USA, prompting a multistate investigation. Public health departments identified primary and secondary cases by using coronavirus disease surveillance data, case investigations, and contact tracing. A primary case was defined as SARS-CoV-2 detected <14 days after travel to or residence in Provincetown during July 3-17. A secondary case was defined as SARS-CoV-2 detected <14 days after close contact with a person who had a primary case but without travel to or residence in Provincetown during July 3-August 10. We identified 1,098 primary cases and 30 secondary cases associated with 26 primary cases among fully and non-fully vaccinated persons. Large gatherings can have widespread effects on SARS-CoV-2 transmission, and fully vaccinated persons should take precautions, such as masking, to prevent SARS-CoV-2 transmission, particularly during substantial or high transmission.

Evidence of transmission from fully vaccinated individuals in a large outbreak of the SARS-CoV-2 Delta variant in Provincetown, Massachusetts.

Multiple summer events, including large indoor gatherings, in Provincetown, Massachusetts (MA), in July 2021 contributed to an outbreak of over one thousand COVID-19 cases among residents and visitors. Most cases were fully vaccinated, many of whom were also symptomatic, prompting a comprehensive public health response, motivating changes to national masking recommendations, and raising questions about infection and transmission among vaccinated individuals. To characterize the outbreak and the viral population underlying it, we combined genomic and epidemiological data from 467 individuals, including 40% of known outbreak-associated cases. The Delta variant accounted for 99% of sequenced outbreak-associated cases. Phylogenetic analysis suggests over 40 sources of Delta in the dataset, with one responsible for a single cluster containing 83% of outbreak-associated genomes. This cluster was likely not the result of extensive spread at a single site, but rather transmission from a common source across multiple settings over a short time. Genomic and epidemiological data combined provide strong support for 25 transmission events from, including many between, fully vaccinated individuals; genomic data alone provides evidence for an additional 64. Together, genomic epidemiology provides a high-resolution picture of the Provincetown outbreak, revealing multiple cases of transmission of Delta from fully vaccinated individuals. However, despite its magnitude, the outbreak was restricted in its onward impact in MA and the US, likely due to high vaccination rates and a robust public health response.

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data.

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

Risk factors and prediction models for incident heart failure with reduced and preserved ejection fraction.

AIMS: This study aims to develop the first race-specific and sex-specific risk prediction models for heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). METHODS AND RESULTS: We created a cohort of 1.8 million individuals who had an outpatient clinic visit between 2002 and 2007 within the Veterans Affairs (VA) Healthcare System and obtained information on HFpEF, HFrEF, and several risk factors from electronic health records (EHR). Variables were selected for the risk prediction models in a 'derivation cohort' that consisted of individuals with baseline date in 2002, 2003, or 2004 using a forward stepwise selection based on a change in C-index threshold. Discrimination and calibration were assessed in the remaining participants (internal 'validation cohort'). A total of 66 831 individuals developed HFpEF, and 92 233 developed HFrEF (52 679 and 71 463 in the derivation cohort) over a median of 11.1 years of follow-up. The HFpEF risk prediction model included age, diabetes, BMI, COPD, previous MI, antihypertensive treatment, SBP, smoking status, atrial fibrillation, and estimated glomerular filtration rate (eGFR), while the HFrEF model additionally included previous CAD. For the HFpEF model, C-indices were 0.74 (SE = 0.002) for white men, 0.76 (0.005) for black men, 0.79 (0.015) for white women, and 0.77 (0.026) for black women, compared with 0.72 (0.002), 0.72 (0.004), 0.77 (0.017), and 0.75 (0.028), respectively, for the HFrEF model. These risk prediction models were generally well calibrated in each race-specific and sex-specific stratum of the validation cohort. CONCLUSIONS: Our race-specific and sex-specific risk prediction models, which used easily obtainable clinical variables, can be a useful tool to implement preventive strategies or subtype-specific prevention trials in the nine million users of the VA healthcare system and the general population after external validation.

Outbreak of SARS-CoV-2 Infections, Including COVID-19 Vaccine Breakthrough Infections, Associated with Large Public Gatherings - Barnstable County, Massachusetts, July 2021.

During July 2021, 469 cases of COVID-19 associated with multiple summer events and large public gatherings in a town in Barnstable County, Massachusetts, were identified among Massachusetts residents; vaccination coverage among eligible Massachusetts residents was 69%. Approximately three quarters (346; 74%) of cases occurred in fully vaccinated persons (those who had completed a 2-dose course of mRNA vaccine [Pfizer-BioNTech or Moderna] or had received a single dose of Janssen [Johnson & Johnson] vaccine ≥14 days before exposure). Genomic sequencing of specimens from 133 patients identified the B.1.617.2 (Delta) variant of SARS-CoV-2, the virus that causes COVID-19, in 119 (89%) and the Delta AY.3 sublineage in one (1%). Overall, 274 (79%) vaccinated patients with breakthrough infection were symptomatic. Among five COVID-19 patients who were hospitalized, four were fully vaccinated; no deaths were reported. Real-time reverse transcription-polymerase chain reaction (RT-PCR) cycle threshold (Ct) values in specimens from 127 vaccinated persons with breakthrough cases were similar to those from 84 persons who were unvaccinated, not fully vaccinated, or whose vaccination status was unknown (median = 22.77 and 21.54, respectively). The Delta variant of SARS-CoV-2 is highly transmissible (1); vaccination is the most important strategy to prevent severe illness and death. On July 27, CDC recommended that all persons, including those who are fully vaccinated, should wear masks in indoor public settings in areas where COVID-19 transmission is high or substantial.* Findings from this investigation suggest that even jurisdictions without substantial or high COVID-19 transmission might consider expanding prevention strategies, including masking in indoor public settings regardless of vaccination status, given the potential risk of infection during attendance at large public gatherings that include travelers from many areas with differing levels of transmission.

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.

Leisure time physical activity, sedentary behavior, and risk of cardiovascular disease and mortality among US Veterans.

BACKGROUND: Cardiovascular disease (CVD) remains the leading cause of death in the US. Although physical activity (PA) has been inversely associated with the risk of CVD, few studies have examined whether sedentary behaviors modify such association. Our goal was to examine associations of leisure time PA with risk of CVD and mortality and the role of sedentary behavior as potential effect modifier among US veterans. METHODS: We analyzed self-reported data on leisure time PA, television watching, and time spent on the computer among 438,364 participants of the Veterans Affairs Million Veteran Program from 2011 to 2018. We calculated metabolic equivalent of task-hours per week (MET-h/week) for each person and used electronic health record data to ascertain CVD. RESULTS: Mean age was 64.6 ± 12.6 years and 92% were men. During a mean follow up of 3.3 years, we observed 22,942 new cases of CVD and 48,325 deaths. There was an inverse relation of leisure time PA with CVD and total mortality [HR: 0.96 (0.95-0.97) and 0.91 (0.90-0.92) per 2 MET-h/week increment for CVD and total mortality, respectively]. The associations of PA with both incident CVD and mortality were stronger in participants who spent more time watching television or on computer (all p values for interaction < 0.01). No interaction of PA with time spent on video game was observed (p>0.05). CONCLUSIONS: Leisure time PA is inversely associated with risk of CVD and mortality among US veterans and such relations were stronger in participants who spent more time watching television or on computer.

Association of pulse rate with outcomes in heart failure with reduced ejection fraction: a retrospective cohort study.

BACKGROUND: In a real-world setting, the effect of pulse rate measured at the time of diagnosis and serially during follow-up and management, on outcomes in heart failure with reduced ejection fraction (HFrEF), has not been well-studied. Furthermore, how beta-blockade use in a real-world situation modifies this relation between pulse rate and outcomes in HFrEF is not well-known. Hence, we identified a large, national, real-world cohort of HFrEF to examine the association of pulse rate and outcomes. METHODS: Using Veterans Affairs (VA) national electronic health records we identified incident HFrEF cases between 2006 and 2012. We examined the associations of both baseline and serially measured pulse rates, with mortality and days hospitalized per year for heart failure and for any cause, using crude and multivariable Cox proportional hazards and Poisson or negative binomial models, respectively. The exposure was examined as continuous, dichotomous, and categorical. Post-hoc analyses addressed the interaction of pulse rate and beta-blocker target dose. RESULTS: We identified 51,194 incident HFrEF cases (67 ± 12 years, 98% male, 77% white. A significant positive, near linear relationship was observed for both baseline and serially measured pulse rates with all-cause mortality, all-cause hospitalization and heart failure hospitalization after adjusting for covariates including beta-blocker use. Patients who had a pulse rate ≥ 70 bpm in the past 6 months had 36% (95% CI: 31-42%), 25% (95% CI: 19-32%), and 51% (95% CI: 33-72%) increased rates of mortality, all-cause hospitalization, and heart failure hospitalization, respectively, compared to patients with pulse rates < 70 bpm. A minority of subjects (15%) were treated with guideline directed beta blockade ≥50% of recommended target dose, among whom better outcomes were seen compared to those who did not achieve target dose in patients with pulse rates both above and below 70 beats per minute. CONCLUSIONS: High pulse rate, both at the time of diagnosis and during follow-up, is strongly associated with increased risk of adverse outcomes in HFrEF patients, independent of the use of beta-blockers. In a real-world setting, the majority of HFrEF patients do not achieve target dose of beta-blockade; greater use of strategies to reduce heart rate may improve outcomes in HFrEF.

Association of Interleukin 6 Receptor Variant With Cardiovascular Disease Effects of Interleukin 6 Receptor Blocking Therapy: A Phenome-Wide Association Study.

Importance: Electronic health record (EHR) biobanks containing clinical and genomic data on large numbers of individuals have great potential to inform drug discovery. Individuals with interleukin 6 receptor (IL6R) single-nucleotide polymorphisms (SNPs) who are not receiving IL6R blocking therapy have biomarker profiles similar to those treated with IL6R blockers. This gene-drug pair provides an example to test whether associations of IL6R SNPs with a broad range of phenotypes can inform which diseases may benefit from treatment with IL6R blockade. Objective: To determine whether screening for clinical associations with the IL6R SNP in a phenome-wide association study (PheWAS) using EHR biobank data can identify drug effects from IL6R clinical trials. Design, Setting, and Participants: Diagnosis codes and routine laboratory measurements were extracted from the VA Million Veteran Program (MVP); diagnosis codes were mapped to phenotype groups using published PheWAS methods. A PheWAS was performed by fitting logistic regression models for testing associations of the IL6R SNPs with 1342 phenotype groups and by fitting linear regression models for testing associations of the IL6R SNP with 26 routine laboratory measurements. Significance was reported using a false discovery rate of 0.05 or less. Findings were replicated in 2 independent cohorts using UK Biobank and Vanderbilt University Biobank data. The Million Veteran Program included 332 799 US veterans; the UK Biobank, 408 455 individuals from the general population of the United Kingdom; and the Vanderbilt University Biobank, 13 835 patients from a tertiary care center. Exposures: IL6R SNPs (rs2228145; rs4129267). Main Outcomes and Measures: Phenotypes defined by International Classification of Diseases, Ninth Revision codes. Results: Of the 332 799 veterans included in the main cohort, 305 228 (91.7%) were men, and the mean (SD) age was 66.1 (13.6) years. The IL6R SNP was most strongly associated with a reduced risk of aortic aneurysm phenotypes (odds ratio, 0.87-0.90; 95% CI, 0.84-0.93) in the MVP. We observed known off-target effects of IL6R blockade from clinical trials (eg, higher hemoglobin level). The reduced risk for aortic aneurysms among those with the IL6R SNP in the MVP was replicated in the Vanderbilt University Biobank, and the reduced risk for coronary heart disease was replicated in the UK Biobank. Conclusions and Relevance: In this proof-of-concept study, we demonstrated application of the PheWAS using large EHR biobanks to inform drug effects. The findings of an association of the IL6R SNP with reduced risk for aortic aneurysms correspond with the newest indication for IL6R blockade, giant cell arteritis, of which a major complication is aortic aneurysm.