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Objective Observational studies can impact patient care but must be robust and reproducible. Nonreproducibility is primarily caused by unclear reporting of design choices and analytic procedures. This study aimed to: (1) assess how the study logic described in an observational study could be interpreted by independent researchers and (2) quantify the impact of interpretations’ variability on patient characteristics. Materials and Methods Nine teams of highly qualified researchers reproduced a cohort from a study by Albogami et al. The teams were provided the clinical codes and access to the tools to create cohort definitions such that the only variable part was their logic choices. We executed teams’ cohort definitions against the database and compared the number of subjects, patient overlap, and patient characteristics. Results On average, the teams’ interpretations fully aligned with the master implementation in 4 out of 10 inclusion criteria with at least 4 deviations per team. Cohorts’ size varied from one-third of the master cohort size to 10 times the cohort size (2159–63 619 subjects compared to 6196 subjects). Median agreement was 9.4% (interquartile range 15.3–16.2%). The teams’ cohorts significantly differed from the master implementation by at least 2 baseline characteristics, and most of the teams differed by at least 5. Conclusions Independent research teams attempting to reproduce the study based on its free-text description alone produce different implementations that vary in the population size and composition. Sharing analytical code supported by a common data model and open-source tools allows reproducing a study unambiguously thereby preserving initial design choices.

Black Veterans have higher a incidence of localized and metastatic prostate cancer compared to White Veterans yet are underrepresented in reports of frequencies of somatic and germline alterations. This retrospective analysis of somatic and putative germline alterations was conducted in a large cohort of Veterans with prostate cancer (N = 835 Black, 1613 White) who underwent next generation sequencing through the VA Precision Oncology Program, which facilitates molecular testing for Veterans with metastatic cancer. No differences were observed in gene alterations for FDA approved targetable therapies (13.5% in Black Veterans vs. 15.5% in White Veterans, P = .21), nor in any potentially actionable alterations (25.5% vs. 28.7%, P =.1). Black Veterans had higher rates of BRAF (5.5% vs. 2.6%, P < .001) alterations, White Veterans TMPRSS2 fusions (27.2% vs. 11.7%, P < .0001). Putative germline alteration rates were higher in White Veterans (12.0% vs. 6.1%, P < .0001). Racial disparities in outcome are unlikely attributable to acquired somatic alterations in actionable pathways.

Background While evidence-based psychotherapy (EBP) for posttraumatic stress disorder (PTSD) is a first-line treatment, its real-world effectiveness is unknown. We compared cognitive processing therapy (CPT) and prolonged exposure (PE) each to an individual psychotherapy comparator group, and CPT to PE in a large national healthcare system. Methods We utilized effectiveness and comparative effectiveness emulated trials using retrospective cohort data from electronic medical records. Participants were veterans with PTSD initiating mental healthcare (N = 265 566). The primary outcome was PTSD symptoms measured by the PTSD Checklist (PCL) at baseline and 24-week follow-up. Emulated trials were comprised of ‘person-trials,’ representing 112 discrete 24-week periods of care (10/07–6/17) for each patient. Treatment group comparisons were made with generalized linear models, utilizing propensity score matching and inverse probability weights to account for confounding, selection, and non-adherence bias. Results There were 636 CPT person-trials matched to 636 non-EBP person-trials. Completing ⩾8 CPT sessions was associated with a 6.4-point greater improvement on the PCL (95% CI 3.1–10.0). There were 272 PE person-trials matched to 272 non-EBP person-trials. Completing ⩾8 PE sessions was associated with a 9.7-point greater improvement on the PCL (95% CI 5.4–13.8). There were 232 PE person-trials matched to 232 CPT person-trials. Those completing ⩾8 PE sessions had slightly greater, but not statistically significant, improvement on the PCL (8.3-points; 95% CI 5.9–10.6) than those completing ⩾8 CPT sessions (7.0-points; 95% CI 5.5–8.5). Conclusions PTSD symptom improvement was similar and modest for both EBPs. Although EBPs are helpful, research to further improve PTSD care is critical.

Kristine E Lynch,1,2 Benjamin Viernes,1,2 Elise Gatsby,1 Scott L DuVall,1,2 Barbara E Jones,2,3 Tamára L Box,4 Craig Kreisler,4 Makoto Jones2,3 1VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 2Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 3Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 4Analytics and Performance Integration (API), Office of Quality and Patient Safety, Veterans Health Administration, Washington, DC, USACorrespondence: Kristine E LynchVA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, 500 Foothill Drive, Salt Lake City, UT, 84148, USATel +1 801 582 1565 ext. 1913Email Kristine.Lynch@va.govPurpose: To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision (ICD-10) code U07.1, COVID-19 virus identified, in the Department of Veterans of Affairs (VA).Patients and Methods: Records of ICD-10 code U07.1 from inpatient, outpatient, and emergency/urgent care settings were extracted from VA medical record data from 4/01/2020 to 3/31/2021. A weighted, random sample of 1500 records from each quarter of the one-year observation period was reviewed by study personnel to confirm active COVID-19 infection at the time of diagnosis and classify reasons for false positive records. PPV was estimated overall and compared across clinical setting and quarters.Results: We identified 664,406 records of U07.1. Among the 1500 reviewed, 237 were false positives (PPV: 84.2%, 95% CI: 82.4– 86.0). PPV ranged from 77.7% in outpatient settings to 93.8% in inpatient settings and was 83.3% in quarter 1, 80.5% in quarter 2, 86.1% in quarter 3, and 83.6% in quarter 4. The most common reasons for false positive records were history of COVID-19 (44.3%) and orders for laboratory tests (21.5%).Conclusion: The PPV of ICD-10 code U07.1 is low, especially in outpatient settings. Directed training may improve accuracy of coding to levels that are deemed adequate for future use in surveillance efforts.Keywords: SARS CoV-2, validation, administrative codes, electronic health records

Background: We described the demographics, cancer subtypes, comorbidities, and outcomes of patients with a history of cancer and coronavirus disease 2019 (COVID-19). Second, we compared patients hospitalized with COVID-19 to patients diagnosed with COVID-19 and patients hospitalized with influenza. Methods: We conducted a cohort study using eight routinely collected health care databases from Spain and the United States, standardized to the Observational Medical Outcome Partnership common data model. Three cohorts of patients with a history of cancer were included: (i) diagnosed with COVID-19, (ii) hospitalized with COVID-19, and (iii) hospitalized with influenza in 2017 to 2018. Patients were followed from index date to 30 days or death. We reported demographics, cancer subtypes, comorbidities, and 30-day outcomes. Results: We included 366,050 and 119,597 patients diagnosed and hospitalized with COVID-19, respectively. Prostate and breast cancers were the most frequent cancers (range: 5%–18% and 1%–14% in the diagnosed cohort, respectively). Hematologic malignancies were also frequent, with non-Hodgkin's lymphoma being among the five most common cancer subtypes in the diagnosed cohort. Overall, patients were aged above 65 years and had multiple comorbidities. Occurrence of death ranged from 2% to 14% and from 6% to 26% in the diagnosed and hospitalized COVID-19 cohorts, respectively. Patients hospitalized with influenza (n = 67,743) had a similar distribution of cancer subtypes, sex, age, and comorbidities but lower occurrence of adverse events. Conclusions: Patients with a history of cancer and COVID-19 had multiple comorbidities and a high occurrence of COVID-19-related events. Hematologic malignancies were frequent. Impact: This study provides epidemiologic characteristics that can inform clinical care and etiologic studies.

ImportanceReported risk of incident peripheral artery disease (PAD) by sex and race varies significantly and has not been reported in national cohorts among individuals free of baseline PAD.ObjectiveTo evaluate the association of sex and race, as well as prevalent cardiovascular risk factors, with limb outcomes in a national cohort of people with normal baseline ankle-brachial indices (ABIs).Design, setting, and participantsThis cohort study was conducted using data from participants in the Veterans Affairs Birth Cohort Study (born 1945-1965), with follow-up data between January 1, 2000, and December 31, 2016. Baseline demographics were collected from 77 041 participants receiving care from the Veterans Health Administration with baseline ABIs of 0.90 to 1.40 and no history of PAD. Data were analyzed from October 2019 through September 2022.ExposuresSex, race, diabetes, and smoking status.Main Outcomes and MeasuresIncident PAD, defined as subsequent ABI less than 0.90, surgical or percutaneous revascularization, or nontraumatic amputation.ResultsOf 77 041 participants with normal ABIs (73 822 [95.8%] men; mean [SD] age, 60.2 [5.9] years; 13 080 Black [18.2%] and 54 377 White [75.6%] among 71 911 participants with race and ethnicity data), there were 6692 incident PAD events over a median [IQR] of 3.9 [1.7-6.9] years. Incidence rates were lower for women than men (incidence rates [IRs] per 1000 person-years, 7.4 incidents [95% CI, 6.2-8.8 incidents] vs 19.2 incidents [95% CI, 18.7-19.6 incidents]), with a lower risk of incident PAD (adjusted hazard ratio [aHR], 0.49 [95% CI, 0.41-0.59]). IRs per 1000 person-years of incident PAD were similar for Black and White participants (18.9 incidents [95% CI, 17.9-20.1 incidents] vs 18.8 incidents [95% CI, 18.3-19.4]). Compared with White participants, Black participants had increased risk of total PAD (aHR, 1.09 [95% CI, 1.02-1.16]) and nontraumatic amputation (aHR, 1.20 [95% CI, 1.06-1.36]) but not surgical or percutaneous revascularization (aHR, 1.10 [95% CI, 0.98-1.23]) or subsequent ABI less than 0.90 (aHR, 1.04 [95% CI, 0.95-1.13]). Diabetes (aHR, 1.62 [95% CI, 1.53-1.72]) and smoking (eg, current vs never: aHR, 1.76 [95% CI, 1.64-1.89]) were associated with incident PAD. Incident PAD was rare among individuals without a history of smoking or diabetes (eg, among 632 women: IR per 1000 people-years, 2.1 incidents [95% CI, 1.0-4.5 incidents]) despite an otherwise–high-risk cardiovascular profile (eg, 527 women [83.4%] with hypertension).Conclusions and RelevanceThis study found that the risk of PAD was approximately 50% lower in women than men and less than 10% higher for Black vs White participants, while the risk of nontraumatic amputation was 20% higher among Black compared with White participants.

While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking.A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021.Advanced age (HR 2.77, 95%CI 2.53-3.04, p 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI.COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery.Authors are supported by various funders, with full details stated in the acknowledgement section.

PURPOSE: The purpose of this study was to determine variation in sexual minority (SM) sexual orientation documentation within the electronic medical records of the Veterans Health Administration (VHA). METHODS: Documentation of SM sexual orientation was retrospectively extracted from clinical notes and administrative data in the VHA from October 1, 1999 to July 1, 2019. The rate of documentation overall and by calendar year was calculated, and differences across patient, provider, and clinic characteristics were evaluated. RESULTS: Approximately 1.4% of all VHA Veterans (n = 115,911) had at least one documentation of SM sexual orientation, including 79,455 men and 36,456 women. The rate of documentation increased from 81.01/100,000 in 2000 to 568.84/100,000 in 2018. The majority of documentations (58.7%) occurred in mental health settings by non-MD mental health/social work counselors, whereas only 9.6% occurred in primary care settings. Although 99% of these Veterans had a primary care visit, only 19% had SM status recorded in that setting. CONCLUSION: Documentation patterns of SM sexual orientation varied considerably in the VHA with notable gaps in primary care. Diverse approaches to culturally competent training for primary care clinicians and patient-facing collection strategies could facilitate documentation of sexual orientation.

Background Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin receptor blockers (ARBs) have been postulated to affect susceptibility to COVID-19. Observational studies so far have lacked rigorous ascertainment adjustment and international generalisability. We aimed to determine whether use of ACEIs or ARBs is associated with an increased susceptibility to COVID-19 in patients with hypertension. Methods In this international, open science, cohort analysis, we used electronic health records from Spain (Information Systems for Research in Primary Care [SIDIAP]) and the USA (Columbia University Irving Medical Center data warehouse [CUIMC] and Department of Veterans Affairs Observational Medical Outcomes Partnership [VA-OMOP]) to identify patients aged 18 years or older with at least one prescription for ACEIs and ARBs (target cohort) or calcium channel blockers (CCBs) and thiazide or thiazide-like diuretics (THZs; comparator cohort) between Nov 1, 2019, and Jan 31, 2020. Users were defined separately as receiving either monotherapy with these four drug classes, or monotherapy or combination therapy (combination use) with other antihypertensive medications. We assessed four outcomes: COVID-19 diagnosis; hospital admission with COVID-19; hospital admission with pneumonia; and hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis. We built large-scale propensity score methods derived through a data-driven approach and negative control experiments across ten pairwise comparisons, with results meta-analysed to generate 1280 study effects. For each study effect, we did negative control outcome experiments using a possible 123 controls identified through a data-rich algorithm. This process used a set of predefined baseline patient characteristics to provide the most accurate prediction of treatment and balance among patient cohorts across characteristics. The study is registered with the EU Post-Authorisation Studies register, EUPAS35296. Findings Among 1 355 349 antihypertensive users (363 785 ACEI or ARB monotherapy users, 248 915 CCB or THZ monotherapy users, 711 799 ACEI or ARB combination users, and 473 076 CCB or THZ combination users) included in analyses, no association was observed between COVID-19 diagnosis and exposure to ACEI or ARB monotherapy versus CCB or THZ monotherapy (calibrated hazard ratio [HR] 0·98, 95% CI 0·84–1·14) or combination use exposure (1·01, 0·90–1·15). ACEIs alone similarly showed no relative risk difference when compared with CCB or THZ monotherapy (HR 0·91, 95% CI 0·68–1·21; with heterogeneity of >40%) or combination use (0·95, 0·83–1·07). Directly comparing ACEIs with ARBs demonstrated a moderately lower risk with ACEIs, which was significant with combination use (HR 0·88, 95% CI 0·79–0·99) and non-significant for monotherapy (0·85, 0·69–1·05). We observed no significant difference between drug classes for risk of hospital admission with COVID-19, hospital admission with pneumonia, or hospital admission with pneumonia, acute respiratory distress syndrome, acute kidney injury, or sepsis across all comparisons. Interpretation No clinically significant increased risk of COVID-19 diagnosis or hospital admission-related outcomes associated with ACEI or ARB use was observed, suggesting users should not discontinue or change their treatment to decrease their risk of COVID-19.

Despite impressive success of machine learning algorithms in clinical natural language processing (cNLP), rule-based approaches still have a prominent role. In this paper, we introduce medspaCy, an extensible, open-source cNLP library based on spaCy framework that allows flexible integration of rule-based and machine learning-based algorithms adapted to clinical text. MedspaCy includes a variety of components that meet common cNLP needs such as context analysis and mapping to standard terminologies. By utilizing spaCy’s clear and easy-to-use conventions, medspaCy enables development of custom pipelines that integrate easily with other spaCy-based modules. Our toolkit includes several core components and facilitates rapid development of pipelines for clinical text.

Supplemental Digital Content is available in the text., Background: Abdominal aortic aneurysm (AAA) is an important cause of cardiovascular mortality; however, its genetic determinants remain incompletely defined. In total, 10 previously identified risk loci explain a small fraction of AAA heritability. Methods: We performed a genome-wide association study in the Million Veteran Program testing ≈18 million DNA sequence variants with AAA (7642 cases and 172 172 controls) in veterans of European ancestry with independent replication in up to 4972 cases and 99 858 controls. We then used mendelian randomization to examine the causal effects of blood pressure on AAA. We examined the association of AAA risk variants with aneurysms in the lower extremity, cerebral, and iliac arterial beds, and derived a genome-wide polygenic risk score (PRS) to identify a subset of the population at greater risk for disease. Results: Through a genome-wide association study, we identified 14 novel loci, bringing the total number of known significant AAA loci to 24. In our mendelian randomization analysis, we demonstrate that a genetic increase of 10 mm Hg in diastolic blood pressure (odds ratio, 1.43 [95% CI, 1.24–1.66]; P=1.6×10−6), as opposed to systolic blood pressure (odds ratio, 1.06 [95% CI, 0.97–1.15]; P=0.2), likely has a causal relationship with AAA development. We observed that 19 of 24 AAA risk variants associate with aneurysms in at least 1 other vascular territory. A 29-variant PRS was strongly associated with AAA (odds ratioPRS, 1.26 [95% CI, 1.18–1.36]; PPRS=2.7×10−11 per SD increase in PRS), independent of family history and smoking risk factors (odds ratioPRS+family history+smoking, 1.24 [95% CI, 1.14–1.35]; PPRS=1.27×10−6). Using this PRS, we identified a subset of the population with AAA prevalence greater than that observed in screening trials informing current guidelines. Conclusions: We identify novel AAA genetic associations with therapeutic implications and identify a subset of the population at significantly increased genetic risk of AAA independent of family history. Our data suggest that extending current screening guidelines to include testing to identify those with high polygenic AAA risk, once the cost of genotyping becomes comparable with that of screening ultrasound, would significantly increase the yield of current screening at reasonable cost.

Background Despite availability of evidence-based psychotherapies (EBPs) for posttraumatic stress disorder (PTSD), not all veterans who initiate EBPs experience benefit. Better understanding factors associated with clinically significant improvement can help ameliorate care. Methods A cohort of Iraq and Afghanistan War veterans who initiated an EBP was identified (N = 32,780) with ≥1 post-deployment psychotherapy visit at the Veterans Health Administration from 10/2001-6/2017, a post-deployment PTSD diagnosis, and ≥2 PTSD symptom measures. We used random-effects logistic regression to assess whether patient-level, diagnostic, and treatment factors were associated with achieving symptom improvement. Results Increased odds of PTSD symptom improvement were seen in women (OR = 1.19; 95% CI: 1.09--1.29), those who initiated EBP within a year of engaging in mental healthcare compared with the delayed EBP group (OR = 1.20; 95% CI: 1.14--1.28), those who completed at least 8 EBP sessions in 16 weeks (OR = 1.23; 95% CI:1.11--1.36), those who received PE only (vs. CPT or both; OR = 2.23; 95% CI: 1.86--2.68) or CPT individual therapy only (vs. CPT group or both; OR = 1.34; 95% CI: 1.22--1.48), and those with a drug dependence diagnosis (OR = 1.24; 95% CI: 1.11--1.39). Decreased odds of improvement were seen in Black veterans (OR=0.75; 95% CI: 0.69--0.81) and those with service-connected disability (OR = 0.61; 95% CI: 0.52--0.71). Limitations Diagnoses were from medical charts and not confirmed with gold standard assessment tools; we only included veterans with at least two PTSD measurements, which may cause bias. Conclusion Modifiable factors associated with PTSD improvement (timing, dose, and modality) can be used to improve EBP outcomes.

Background We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient’s risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. Methods We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69–0.81, COVER-I: 0.73–0.91, and COVER-F: 0.72–0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. Conclusions This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Kristin Kostka,1,2 Talita Duarte-Salles,3 Albert Prats-Uribe,4 Anthony G Sena,5,6 Andrea Pistillo,3 Sara Khalid,4 Lana YH Lai,7 Asieh Golozar,8,9 Thamir M Alshammari,10 Dalia M Dawoud,11 Fredrik Nyberg,12 Adam B Wilcox,13,14 Alan Andryc,5 Andrew Williams,15 Anna Ostropolets,16 Carlos Areia,17 Chi Young Jung,18 Christopher A Harle,19 Christian G Reich,1,2 Clair Blacketer,5,6 Daniel R Morales,20 David A Dorr,21 Edward Burn,3,4 Elena Roel,3,22 Eng Hooi Tan,4 Evan Minty,23 Frank DeFalco,5 Gabriel de Maeztu,24 Gigi Lipori,19 Hiba Alghoul,25 Hong Zhu,26 Jason A Thomas,13 Jiang Bian,19 Jimyung Park,27 Jordi Martínez Roldán,28 Jose D Posada,29 Juan M Banda,30 Juan P Horcajada,31 Julianna Kohler,32 Karishma Shah,33 Karthik Natarajan,16,34 Kristine E Lynch,35,36 Li Liu,37 Lisa M Schilling,38 Martina Recalde,3,22 Matthew Spotnitz,14 Mengchun Gong,39 Michael E Matheny,40,41 Neus Valveny,42 Nicole G Weiskopf,21 Nigam Shah,29 Osaid Alser,43 Paula Casajust,42 Rae Woong Park,27,44 Robert Schuff,21 Sarah Seager,1 Scott L DuVall,35,36 Seng Chan You,45 Seokyoung Song,46 Sergio Fernández-Bertolín,3 Stephen Fortin,5 Tanja Magoc,19 Thomas Falconer,16 Vignesh Subbian,47 Vojtech Huser,48 Waheed-Ul-Rahman Ahmed,33,49 William Carter,38 Yin Guan,50 Yankuic Galvan,19 Xing He,19 Peter R Rijnbeek,6 George Hripcsak,16,34 Patrick B Ryan,5,16 Marc A Suchard,51 Daniel Prieto-Alhambra4 1IQVIA, Cambridge, MA, USA; 2OHDSI Center at The Roux Institute, Northeastern University, Portland, ME, USA; 3Fundació Institut Universitari per a la recerca a l’Atenció Primà ria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain; 4Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK; 5Janssen Research & Development, Titusville, NJ, USA; 6Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands; 7School of Medical Sciences, University of Manchester, Manchester, UK; 8Regeneron Pharmaceuticals, Tarrytown, NY, USA; 9Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; 10College of Pharmacy, Riyadh Elm University, Riyadh, Saudi Arabia; 11National Institute for Health and Care Excellence, London, UK; 12School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; 13Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA; 14Unviersity of Washington Medicine, Seattle, WA, USA; 15Tufts Institute for Clinical Research and Health Policy Studies, Boston, MA, USA; 16Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA; 17Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; 18Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, South Korea; 19University of Florida Health, Gainesville, FL, USA; 20Division of Population Health and Genomics, University of Dundee, Dundee, UK; 21Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; 22Universitat Autònoma de Barcelona, Barcelona, Spain; 23O’Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Calgary, Canada; 24IOMED, Barcelona, Spain; 25Faculty of Medicine, Islamic University of Gaza, Gaza, Palestine; 26Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 27Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, South Korea; 28Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain; 29Department of Medicine, School of Medicine, Stanford University, Redwood City, CA, USA; 30Georgia State University, Department of Computer Science, Atlanta, GA, USA; 31Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d’Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona, Universitat Pompeu Fabra, Barcelona, Spain; 32United States Agency for International Development, Washington, DC, USA; 33Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK; 34New York-Presbyterian Hospital, New York, NY, USA; 35VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; 36Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA; 37Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, People’s Republic of China; 38Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; 39Institute of Health Management, Southern Medical University, Guangzhou, People’s Republic of China; 40Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, TN, USA; 41Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; 42Real-World Evidence, TFS, Barcelona, Spain; 43Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; 44Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; 45Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea; 46Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, South Korea; 47College of Engineering, The University of Arizona, Tucson, AZ, USA; 48National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; 49College of Medicine and Health, University of Exeter, St Luke’s Campus, Exeter, UK; 50DHC Technologies Co. Ltd., Beijing, People’s Republic of China; 51Departments of Biostatistics, Computational Medicine, and Human Genetics, University of California, Los Angeles, CA, USACorrespondence: Daniel Prieto-Alhambra, Botnar Research Centre, Windmill Road, Oxford, OX37LD, UK, Email daniel.prietoalhambra@ndorms.ox.ac.ukPurpose: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and Methods: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.Results: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.Conclusion: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.Keywords: OHDSI, OMOP CDM, descriptive epidemiology, real world data, real world evidence, open science

BackgroundThe incidence and severity of coronavirus disease 19 (COVID-19) is substantially higher in men. Sex hormones may be a potential mechanism for differences in COVID-19 outcome in men and women. We hypothesized that men treated with androgen deprivation therapy (ADT) have lower incidence and severity of COVID-19.MethodsWe conducted an observational study of male Veterans treated in the Veterans Health Administration from February 15th to July 15th, 2020. We developed a propensity score model to predict the likelihood to undergo Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. We performed multivariable logistic regression modeling adjusted with inverse probability weighting to examine the relationship between ADT and COVID-19 incidence. We conducted logistic regression analysis among COVID-19 patients to test the association between ADT and COVID-19 severity.ResultsWe identified a large cohort of 246,087 VA male patients who had been tested for SARS-CoV-2, of whom 3,057 men were exposed to ADT, and 36,096 men with cancer without ADT. Of these, 295 ADT patients and 2,427 cancer patients not on ADT had severe COVID-19 illness. In the primary, propensity-weighted comparison of ADT patients to cancer patients not on ADT, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81–0.95]; p = 0.001). Furthermore, ADT was associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53–0.96]; p = 0.03).ConclusionADT is associated with reduced incidence and severity of COVID-19 amongst male Veterans. Testosterone and androgen receptor signaling may confer increased risk for SARS-CoV-2 infection and contribute to severe COVID-19 pathophysiology in men.

Objective: To characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients. Design and setting: This is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020. Participants: Two non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days. Outcomes: Demographics, comorbidities and 30-day outcomes (hospitalisation and death for the 'diagnosed' cohort and adverse events and death for the 'hospitalised' cohort) were reported. Results: We identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension. Conclusions: COVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension. This work was supported by several funders as follows: the European Health Data and Evidence Network received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement number 806968. The JU received support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This research received partial support from the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), US National Institutes of Health (R01 LM006910), US Department of Veterans Affairs, the Health Department from the Generalitat de Catalunya with a grant for research projects on SARS-CoV-2 and COVID-19 disease organised by the Direcció General de Recerca i Innovació en Salut, Janssen Research and Development, TFS, IOMED and IQVIA. The University of Oxford received funding related to this work from the Bill and Melinda Gates Foundation (Investment ID INV-016201 and INV-019257). TFS received funding related to this work from the University of Oxford. This work was also supported with funding (resources and facilities) of the Department of Veterans Affairs (VA) Informatics and Computing Infrastructure (VINCI) (VA HSR RES 13-457).

Patients with autoimmune diseases were advised to shield to avoid COVID-19, but information on their prognosis is lacking. We characterised 30-day outcomes and mortality after hospitalisation with COVID-19 among patients with prevalent autoimmune diseases, and compared outcomes after hospital admissions among similar patients with seasonal influenza.Multinational network cohort study.Electronic health records data from Columbia University Irving Medical Center (CUIMC) (NYC, United States [US]), Optum [US], Department of Veterans Affairs (VA) (US), Information System for Research in Primary Care-Hospitalisation Linked Data (SIDIAP-H) (Spain), and claims data from IQVIA Open Claims (US) and Health Insurance and Review Assessment (HIRA) (South Korea).All patients with prevalent autoimmune diseases, diagnosed and/or hospitalised between January and June 2020 with COVID-19, and similar patients hospitalised with influenza in 2017-2018 were included.30-day complications during hospitalisation and death.We studied 133,589 patients diagnosed and 48,418 hospitalised with COVID-19 with prevalent autoimmune diseases. The majority of participants were female (60.5% to 65.9%) and aged ≥50 years. The most prevalent autoimmune conditions were psoriasis (3.5 to 32.5%), rheumatoid arthritis (3.9 to 18.9%), and vasculitis (3.3 to 17.6%). Amongst hospitalised patients, Type 1 diabetes was the most common autoimmune condition (4.8% to 7.5%) in US databases, rheumatoid arthritis in HIRA (18.9%), and psoriasis in SIDIAP-H (26.4%).Compared to 70,660 hospitalised with influenza, those admitted with COVID-19 had more respiratory complications including pneumonia and acute respiratory distress syndrome, and higher 30-day mortality (2.2% to 4.3% versus 6.3% to 24.6%).Patients with autoimmune diseases had high rates of respiratory complications and 30-day mortality following a hospitalization with COVID-19. Compared to influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality. Future studies should investigate predictors of poor outcomes in COVID-19 patients with autoimmune diseases.Patients with autoimmune conditions may be at increased risk of COVID-19 infection andcomplications.There is a paucity of evidence characterising the outcomes of hospitalised COVID-19 patients with prevalent autoimmune conditions.Most people with autoimmune diseases who required hospitalisation for COVID-19 were women, aged 50 years or older, and had substantial previous comorbidities.Patients who were hospitalised with COVID-19 and had prevalent autoimmune diseases had higher prevalence of hypertension, chronic kidney disease, heart disease, and Type 2 diabetes as compared to those with prevalent autoimmune diseases who were diagnosed with COVID-19.A variable proportion of 6% to 25% across data sources died within one month of hospitalisation with COVID-19 and prevalent autoimmune diseases.For people with autoimmune diseases, COVID-19 hospitalisation was associated with worse outcomes and 30-day mortality compared to admission with influenza in the 2017-2018 season.

Background Pulmonary hypertension incidence based on echocardiographic estimates of pulmonary artery systolic pressure in people living with HIV remains unstudied. We aimed to determine whether people living with HIV have higher incidence and risk of pulmonary hypertension than uninfected individuals. Methods In this retrospective cohort study, we evaluated data from participants in the Veterans Aging Cohort Study (VACS) referred for echocardiography with baseline pulmonary artery systolic pressure measures of 35 mm Hg or less. Incident pulmonary hypertension was defined as pulmonary artery systolic pressure higher than 35 mm Hg on subsequent echocardiogram. We used Poisson regression to estimate incidence rates (IRs) of pulmonary hypertension by HIV status. We then estimated hazard ratios (HRs) by HIV status using Cox proportional hazards regression. We further categorised veterans with HIV by CD4 count or HIV viral load to assess the association between pulmonary hypertension risk and HIV severity. Models included age, sex, race or ethnicity, prevalent heart failure, chronic obstructive pulmonary disease, hypertension, smoking status, diabetes, body-mass index, estimated glomerular filtration rate, hepatitis C virus infection, liver cirrhosis, and drug use as covariates. Findings Of 21 314 VACS participants with at least one measured PASP on or after April 1, 2003, 13 028 VACS participants were included in the analytic sample (4174 [32%] with HIV and 8854 [68%] without HIV). Median age was 58 years and 12 657 (97%) were male. Median follow-up time was 3·1 years (IQR 0·9-6·8) spanning from April 1, 2003, to Sept 30, 2017. Unadjusted IRs per 1000 person-years were higher in veterans with HIV (IR 28·6 [95% CI 26·1-31·3]) than in veterans without HIV (IR 23·4 [21·9-24·9]; p=0·0004). The risk of incident pulmonary hypertension was higher among veterans with HIV than among veterans without HIV (unadjusted HR 1·25 [95% CI 1·12-1·40], p

Background In this study we sought to explore the possibility of using patient centered care (PCC) documentation as a measure of the delivery of PCC in a health system. Methods We first selected 6 VA medical centers based on their scores for a measure of support for self-management subscale from a national patient satisfaction survey (the Survey for Healthcare Experience-Patients). We accessed clinical notes related to either smoking cessation or weight management consults. We then annotated this dataset of notes for documentation of PCC concepts including: patient goals, provider support for goal progress, social context, shared decision making, mention of caregivers, and use of the patient's voice. We examined the association of documentation of PCC with patients’ perception of support for self-management with regression analyses. Results Two health centers had Conclusion Providers document PCC concepts in their clinical notes. In this pilot study we explored the feasibility of using this data as a means to measure the degree to which care in a health center is patient centered. Practice Implications: clinical EHR notes are a rich source of information about PCC that could potentially be used to assess PCC over time and across systems with scalable technologies such as natural language processing.

Purpose The Veterans Health Administration (VA) is the largest single integrated healthcare system in the US and is likely the largest healthcare provider for people with minoritized sexual orientations (e.g., gay, lesbian, bisexual). The purpose of this study was to use electronic health record (EHR) data to replicate self-reported survey findings from the general US population and assess whether sexual orientation is associated with diagnosed physical health conditions that may elevate risk of COVID-19 severity among veterans who utilize the VA. Methods A retrospective analysis of VA EHR data from 10/01/1999-07-01-2019 analyzed in 2021. Veterans with minoritized sexual orientations were included if they had documentation of a minoritized sexual orientation within clinical notes identified via natural language processing. Veterans without minoritized sexual orientation documentation comprised the comparison group. Adjusted prevalence and prevalence ratios (aPR) were calculated overall and by race/ethnicity while accounting for differences in distributions of sex assigned at birth, age, calendar year of first VA visit, volumes of healthcare utilization, and VA priority group. Results Data from 108,401 veterans with minoritized sexual orientation and 6,511,698 controls were analyzed. After adjustment, veterans with minoritized sexual orientations had a statistically significant elevated prevalence of 10 of the 11 conditions. Amongst the highest disparities observed were COPD (aPR:1.24 [95% confidence interval:1.23-1.26]), asthma (1.22 [1.20-1.24]), and stroke (1.26 [1.24-1.28]). Conclusions Findings largely corroborated patterns among the general US population. Further research is needed to determine if these disparities translate to poorer COVID-19 outcomes for individuals with minoritized sexual orientation.

Background A detailed characterization of patients with COVID-19 living with obesity has not yet been undertaken. We aimed to describe and compare the demographics, medical conditions, and outcomes of COVID-19 patients living with obesity (PLWO) to those of patients living without obesity. Methods We conducted a cohort study based on outpatient/inpatient care and claims data from January to June 2020 from Spain, the UK, and the US. We used six databases standardized to the OMOP common data model. We defined two non-mutually exclusive cohorts of patients diagnosed and/or hospitalized with COVID-19; patients were followed from index date to 30 days or death. We report the frequency of demographics, prior medical conditions, and 30-days outcomes (hospitalization, events, and death) by obesity status. Results We included 627 044 (Spain: 122 058, UK: 2336, and US: 502 650) diagnosed and 160 013 (Spain: 18 197, US: 141 816) hospitalized patients with COVID-19. The prevalence of obesity was higher among patients hospitalized (39.9%, 95%CI: 39.8−40.0) than among those diagnosed with COVID-19 (33.1%; 95%CI: 33.0−33.2). In both cohorts, PLWO were more often female. Hospitalized PLWO were younger than patients without obesity. Overall, COVID-19 PLWO were more likely to have prior medical conditions, present with cardiovascular and respiratory events during hospitalization, or require intensive services compared to COVID-19 patients without obesity. Conclusion We show that PLWO differ from patients without obesity in a wide range of medical conditions and present with more severe forms of COVID-19, with higher hospitalization rates and intensive services requirements. These findings can help guiding preventive strategies of COVID-19 infection and complications and generating hypotheses for causal inference studies.

Venous thromboembolism (VTE) is a significant cause of mortality1, yet its genetic determinants remain incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank testing ~13 million DNA sequence variants for association with VTE (26,066 cases; 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 VTE cases and 167,295 controls. We identified 22 novel loci, bringing the total number of VTE-associated loci to 33 and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for VTE that identifies 5% of the population at equivalent incident VTE risk to carriers of the established F5 Leiden (p.R506Q) and prothrombin G20210A mutations. Our data provide new mechanistic insights into the genetic epidemiology of VTE and suggest a greater overlap among venous and arterial cardiovascular disease than previously suggested., Editorial summary Genome-wide analysis of venous thromboembolism identifies 22 new risk loci and facilitates construction of a polygenic risk score. Comparison to arterial vascular disease highlights shared pathophysiology and potential therapeutic strategies.

Large-scale multi-ethnic cohorts offer unprecedented opportunities to elucidate the genetic factors influencing complex traits related to health and disease among minority populations. At the same time, the genetic diversity in these cohorts presents new challenges for analysis and interpretation. We consider the utility of race and/or ethnicity categories in genome-wide association studies (GWASs) of multi-ethnic cohorts. We demonstrate that race/ethnicity information enhances the ability to understand population-specific genetic architecture. To address the practical issue that self-identified racial/ethnic information may be incomplete, we propose a machine learning algorithm that produces a surrogate variable, termed HARE. We use height as a model trait to demonstrate the utility of HARE and ethnicity-specific GWASs.

Background The development and adoption of health care common data models (CDMs) has addressed some of the logistical challenges of performing research on data generated from disparate health care systems by standardizing data representations and leveraging standardized terminology to express clinical information consistently. However, transforming a data system into a CDM is not a trivial task, and maintaining an operational, enterprise capable CDM that is incrementally updated within a data warehouse is challenging. Objectives To develop a quality assurance (QA) process and code base to accompany our incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors. Methods We designed and implemented a multistage QA) approach centered on completeness, value conformance, and relational conformance data-quality elements. For each element we describe key incremental load challenges, our extract, transform, and load (ETL) solution of data to overcome those challenges, and potential impacts of incremental load failure. Results Completeness and value conformance data-quality elements are most affected by incremental changes to the CDW, while updates to source identifiers impact relational conformance. ETL failures surrounding these elements lead to incomplete and inaccurate capture of clinical concepts as well as data fragmentation across patients, providers, and locations. Conclusion Development of robust QA processes supporting accurate transformation of OMOP and other CDMs from source data is still in evolution, and opportunities exist to extend the existing QA framework and tools used for incremental ETL QA processes.

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.

Peripheral artery disease (PAD) is a leading cause of cardiovascular morbidity and mortality; however, the extent to which genetic factors increase risk for PAD is largely unknown. Using electronic health record data, we performed a genome-wide association study in the Million Veteran Program testing ~32 million DNA sequence variants with PAD (31,307 cases and 211,753 controls) across veterans of European, African and Hispanic ancestry. The results were replicated in an independent sample of 5,117 PAD cases and 389,291 controls from the UK Biobank. We identified 19 PAD loci, 18 of which have not been previously reported. Eleven of the 19 loci were associated with disease in three vascular beds (coronary, cerebral, peripheral), including LDLR, LPL and LPA, suggesting that therapeutic modulation of low-density lipoprotein cholesterol, the lipoprotein lipase pathway or circulating lipoprotein(a) may be efficacious for multiple atherosclerotic disease phenotypes. Conversely, four of the variants appeared to be specific for PAD, including F5 p.R506Q, highlighting the pathogenic role of thrombosis in the peripheral vascular bed and providing genetic support for Factor Xa inhibition as a therapeutic strategy for PAD. Our results highlight mechanistic similarities and differences among coronary, cerebral and peripheral atherosclerosis and provide therapeutic insights.

Introduction Janssen received reports of needle detachments for Risperdal® CONSTA® and, in response, redesigned the kit. Objective The study objective was to estimate the rate of Risperdal® CONSTA® needle detachments prior to and after the introduction of a redesigned kit. Methods This retrospective study used record abstraction in the US Department of Veterans Affairs (VA). The 3 phases included: (1) a pilot study for methods evaluation in a sample of 6 hospitals with previously reported detachments; (2) a baseline study to ascertain the baseline detachment rate; and (3) a follow-up study to ascertain the rate for the redesigned kit. Administrative codes and natural language processing with clinical review were used to identify detachments. Results Pilot: we identified a subset of spontaneously reported detachments and several previously unreported events. In the baseline study (original device), from January through December 2013, 22 needle detachments were identified among 47,934 administrations of the drug in a census of administrations in the VA; an incidence of 0.0459%. In the follow-up study (redesigned device), from December 2015 through December 2016, there were 14 reported detachments in 41,819 injections, 0.0335%. This represents a reduction of 27% from the baseline. Conclusion This approach enabled us to identify needle detachments we would not have otherwise found (“solicited”). However, it likely resulted in incomplete outcome ascertainment. While this may have resulted in lower overall rates, it did not bias the comparison of the baseline and follow-up studies. The results showed that the redesigned Risperdal® CONSTA® kit reduced the incidence of needle detachment events in the VA. Funding Janssen Pharmaceuticals, Inc.

Identifying occurrences of medication side effects and adverse drug events (ADEs) is an important and challenging task because they are frequently only mentioned in clinical narrative and are not formally reported. We developed a natural language processing (NLP) system that aims to identify mentions of symptoms and drugs in clinical notes and label the relationship between the mentions as indications or ADEs. The system leverages an existing word embeddings model with induced word clusters for dimensionality reduction. It employs a conditional random field (CRF) model for named entity recognition (NER) and a random forest model for relation extraction (RE). Final performance of each model was evaluated separately and then combined on a manually annotated evaluation set. The micro-averaged F1 score was 80.9% for NER, 88.1% for RE, and 61.2% for the integrated systems. Outputs from our systems were submitted to the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE 1.0) competition (Yu et al. in http://bio-nlp.org/index.php/projects/39-nlp-challenges , 2018). System performance was evaluated in three tasks (NER, RE, and complete system) with multiple teams submitting output from their systems for each task. Our RE system placed first in Task 2 of the challenge and our integrated system achieved third place in Task 3. Adding to the growing number of publications that utilize NLP to detect occurrences of ADEs, our study illustrates the benefits of employing innovative feature engineering.

Background Estimated glomerular filtration rate (eGFR) is the clinical standard for assessing kidney function and staging chronic kidney disease. Automated reporting of eGFR using the Modification of Diet in Renal Disease (MDRD) study equation was first implemented within the Department of Veterans Affairs (VA) in 2007 with staggered adoption across laboratories. When automated eGFR are not used or unavailable, values are retrospectively calculated using clinical and demographic data that are currently available in the electronic health record (EHR). Due to the dynamic nature of EHRs, current data may not always match past data. Whether and to what extent the practice of re-calculating eGFR on retrospective data differs from using the automated values is unknown. Methods We assessed clinical data for patients enrolled in VA who had their first automated eGFR lab in 2013.We extracted the eGFR value, the corresponding serum creatinine value, and patient race, gender, and date of birth from the EHR. The MDRD equation was applied to retrospectively calculate eGFR. Stage of chronic kidney disease (CKD) was defined using both eGFR values. We used Bland–Altman plots and percent agreement to assess the difference between the automated and calculated values. We developed an algorithm to select the most parsimonious parameter set to explain the difference in values and used chart review on a small subsample of patients to determine if one approach more accurately describes the patient at the time of eGFR measurement. Results We evaluated eGFR data pairs from 266,084 patients. Approximately 33.0% (n = 86,747) of eGFR values differed between automated and retrospectively calculated methods. The majority of discordant pairs were classified as the same CKD stage (n = 74,542, 85.93%). The Bland–Altman plot showed differences in the data pairs were centered near zero (mean difference: 0.8 mL/min/1.73m2) with 95% limits of agreement between − 6.4 and 8.0. A change in recorded age explained 95.6% (n = 78,903) of discordant values and 85.02% (n = 9371) of the discordant stages. Conclusions Values of retrospectively calculated eGFR can differ from automated values, but do not always result in a significant classification change. In very large datasets these small differences could become significant.

ImportanceThe incidence and severity of coronavirus disease 19 (COVID-19) is higher in men. Sex hormones potentially offer one explanation for differences by sex. ObjectiveTo determine whether men exposed to androgen deprivation therapy (ADT) have lower incidence and severity of COVID-19. DesignWe conducted an observational study of male Veterans treated in the Veterans Health Administration from February 15th to July 15th, 2020. We developed a propensity score model to predict the likelihood to undergo Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) testing. We performed multivariable logistic regression modeling adjusted with inverse probability weighting to examine the relationship between ADT and COVID-19 incidence. We conducted logistic regression analysis among COVID-19 patients to test the association between ADT and COVID-19 severity. SettingThe U.S. Department of Veterans Affairs ParticipantsThe study sample consisted of 6,250,417 male Veterans who were alive as of February 15, 2020. ExposureExposure to ADT was defined as having any prescription for a luteinizing hormone releasing hormone analogue or an antiandrogen in the six months prior to the index date. Main Outcomes and MeasuresTo assess incidence, we used a binary variable indicating any positive reverse transcriptase polymerase chain reaction SARS-CoV-2 test result through July 15, 2020. To measure severity, we constructed a binary variable indicating whether a patient was admitted to the intensive care unit, placed on mechanical ventilation, or dead in the 60 days following a positive test up to July 15, 2020. ResultsWe identified 246,087 patients who had been tested for SARS-CoV-2, of whom 3,057 were exposed to ADT, and 36,096 patients with cancer and no ADT exposure. Of these, 295 ADT patients and 2,427 other cancer patients had COVID-19 illness. In the primary, propensity-weighted comparison of ADT patients to cancer patients not on ADT, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81-0.95]; p=0.001). ADT was associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53-0.96]; p=0.03). Conclusions and RelevanceADT is associated with reduced incidence and severity of COVID-19 amongst male Veterans. Repurposing of drugs that modulate androgen production and/or action may represent viable potential treatments for COVID-19. KEY POINTSO_ST_ABSQuestionC_ST_ABSDoes androgen deprivation therapy (ADT) lower incidence and severity of COVID-19? FindingsIn this observational study of male Veterans treated in the Veterans Healthcare System, ADT was associated with decreased likelihood of testing positive for SARS-CoV-2 (adjusted OR, 0.88 [95% CI, 0.81-0.95]; p=0.001). ADT was also associated with fewer severe COVID-19 outcomes (OR 0.72 [95% CI 0.53-0.96]; p=0.03). MeaningThe use of androgen deprivation therapy may be protective against SARS-CoV-2 infection and modulate severity of COVID-19 outcomes.

Alpha-1 blockers, often used to treat benign prostate hyperplasia (BPH), have been hypothesized to prevent COVID-19 complications by minimising cytokine storms release. We conducted a prevalent-user active-comparator cohort study to assess association between alpha-1 blocker use and risks of three COVID-19 outcomes: diagnosis, hospitalization, and hospitalization requiring intensive services. Our study included 2.6 and 0.46 million users of alpha-1 blockers and of alternative BPH therapy during the period between November 2019 and January 2020, found in electronic health records from Spain (SIDIAP) and the United States (Department of Veterans Affairs, Columbia University Irving Medical Center, IQVIA OpenClaims, Optum DOD, Optum EHR). We estimated hazard ratios using state-of-the-art techniques to minimize potential confounding, including large-scale propensity score matching/stratification and negative control calibration. We found no differential risk for any of COVID-19 outcome, pointing to the need for further research on potential COVID-19 therapies.

Objective To best meet our point-of-care research (POC-R) needs, we developed ProjectFlow, a configurable, clinical research workflow management application. In this article, we describe ProjectFlow and how it is used to manage study processes for the Diuretic Comparison Project (DCP) and the Research Precision Oncology Program (RePOP). Materials and methods The Veterans Health Administration (VHA) is the largest integrated health care system in the United States. ProjectFlow is a flexible web-based workflow management tool specifically created to facilitate conduct of our clinical research initiatives within the VHA. The application was developed using the Grails web framework and allows researchers to create custom workflows using Business Process Model and Notation. Results As of January 2021, ProjectFlow has facilitated management of study recruitment, enrollment, randomization, and drug orders for over 10 000 patients for the DCP clinical trial. It has also helped us evaluate over 3800 patients for recruitment and enroll over 370 of them into RePOP for use in data sharing partnerships and predictive analytics aimed at optimizing cancer treatment in the VHA. Discussion The POC-R study design embeds research processes within day-to-day clinical care and leverages longitudinal electronic health record (EHR) data for study recruitment, monitoring, and outcome reporting. Software that allows flexibility in study workflow creation and integrates with enterprise EHR systems is critical to the success of POC-R. Conclusions We developed a flexible web-based informatics solution called ProjectFlow that supports custom research workflow configuration and has ability to integrate data from existing VHA EHR systems.

ObjectivesTo perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions.DesignRetrospective cohort study.SettingThe Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe.ParticipantsPatients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2.Primary and secondary outcome measuresPatients were categorized as “ever-severe” or “never-severe” using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction.ResultsOf 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites.ConclusionsLaboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.

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.

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic alanine aminotransferase elevation (cALT) without other liver diseases, we performed a trans-ancestry genome-wide association study in the Million Veteran Program including 90,408 cALT cases and 128,187 controls. In the Discovery stage, seventy-seven loci exceeded genome-wide significance – including 25 without prior NAFLD or ALT associations – with one additional locus identified in European-American-only and two in African-American-only analyses (P-8). External replication in cohorts with NAFLD defined by histology (7,397 cases, 56,785 controls) or liver fat extracted from radiologic imaging (n=44,289) validated 17 SNPs (P-4) of which 9 were novel (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH, and IFI30). Pleiotropy analysis showed that 61 of 77 trans-ancestry and all 17 validated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.

Background SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the “prediction model risk of bias assessment” criteria, and it has not been externally validated. Objective The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. Methods We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. Results The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. Conclusions Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

OBJECTIVES To characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children and adolescents diagnosed or hospitalized with coronavirus disease 2019 (COVID-19) and to compare them in secondary analyses with patients diagnosed with previous seasonal influenza in 2017–2018. METHODS International network cohort using real-world data from European primary care records (France, Germany, and Spain), South Korean claims and US claims, and hospital databases. We included children and adolescents diagnosed and/or hospitalized with COVID-19 at age RESULTS A total of 242 158 children and adolescents diagnosed and 9769 hospitalized with COVID-19 and 2 084 180 diagnosed with influenza were studied. Comorbidities including neurodevelopmental disorders, heart disease, and cancer were more common among those hospitalized with versus diagnosed with COVID-19. Dyspnea, bronchiolitis, anosmia, and gastrointestinal symptoms were more common in COVID-19 than influenza. In-hospital prevalent treatments for COVID-19 included repurposed medications ( CONCLUSIONS Despite negligible fatality, complications including hospitalization, hypoxemia, and pneumonia were more frequent in children and adolescents with COVID-19 than with influenza. Dyspnea, anosmia, and gastrointestinal symptoms could help differentiate diagnoses. A wide range of medications was used for the inpatient management of pediatric COVID-19.

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic elevation of alanine aminotransferase (cALT) levels without other liver diseases, we performed a multiancestry genome-wide association study (GWAS) in the Million Veteran Program (MVP) including 90,408 cALT cases and 128,187 controls. Seventy-seven loci exceeded genome-wide significance, including 25 without prior NAFLD or alanine aminotransferase associations, with one additional locus identified in European American-only and two in African American-only analyses (P < 5 × 10(−8)). External replication in histology-defined NAFLD cohorts (7,397 cases and 56,785 controls) or radiologic imaging cohorts (n = 44,289) replicated 17 single-nucleotide polymorphisms (SNPs) (P < 6.5 × 10(−4)), of which 9 were new (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH and IFI30). Pleiotropy analysis showed that 61 of 77 multiancestry and all 17 replicated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.

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 (MR) 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=1.9×10−14) using cis-eQTL genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared eQTL 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.

Introduction: CYP2C19 reduced function (RF) alleles (*2, *3) have been shown to impair clopidogrel effectiveness following percutaneous coronary intervention (PCI) in the setting of acute coronary syndromes (ACS); however, this association has not been explored in the Veterans Health Administration. Hypothesis: CYP2C19 RF alleles are associated with major adverse cardiac events (MACE) in patients treated with clopidogrel Methods: MACE and CYP2C19 genotype were determined in MVP participants who underwent PCI between 2009-2017 with 1 year follow-up. Age was restricted to Results: Among 4,490 Veterans (age 59.1 ± 5 years, 18% African American, 6% Hispanic, 44% ACS) who were prescribed clopidogrel following PCI, 1,261 (28%) had RF CYP2C19 alleles. The overall MACE rate was 7.0%. MACE was associated with ACS as initial presentation, diagnosis of diabetes, chronic kidney disease, peripheral vascular disease, congestive heart failure, history of stroke, prior MI. Among the subgroup presenting with ACS, the adjusted risk of MACE was greater in participants with RF alleles compared to those with wildtype alleles (HR 1.38, 95% CI 1.002-1.916). There was no impact of RF alleles on MACE risk in those in the non-ACS group (HR 1.00, 95% CI 0.702-1.429). Conclusions: These data from the largest integrated health system in the US demonstrate that in Veterans presenting with ACS undergoing PCI, there was a higher risk of MACE in CYP2C19 RF carriers prescribed clopidogrel vs. wildtype carriers. The impact of the CYP2C19-clopidogrel interaction was not observed in the non-ACS patients. Further studies should explore the role of pharmacogenetic testing in Veterans.

ObjectiveMost patients severely affected by COVID-19 have been elderly and patients with underlying chronic disease such as diabetes, cardiovascular disease, or respiratory disease. People living with HIV (PLHIV) may have greater risk of contracting or developing severe COVID-19 due to the underlying HIV infection or higher prevalence of comorbidities.DesignThis is a cohort study, including PLHIV diagnosed, hospitalized, or requiring intensive services for COVID-19.MethodsData sources include routine electronic medical record or claims data from the U.S. and Spain. Patient demographics, comorbidities, and medication history are described.ResultFour data sources had a population of HIV/COVID-19 coinfected patients ranging from 288 to 4606 lives. PLHIV diagnosed with COVID-19 were younger than HIV-negative patients diagnosed with COVID-19. PLHIV diagnosed with COVID-19 diagnosis had similar comorbidities as HIV-negative COVID-19 patients with higher prevalence of those comorbidities and history of severe disease. Treatment regimens were similar between PLHIV diagnosed with COVID-19 or PLHIV requiring intensive services.ConclusionsOur study uses routine practice data to explore HIV impact on COVID-19, providing insight into patient history prior to COVID-19. We found that HIV and COVID-19 coinfected patients have higher prevalence of underlying comorbidities such as cardiovascular and respiratory disease as compared to HIV-negative COVID-19 infected patients. We also found that, across the care cascade, co-infected patients who received intensive services were more likely to have more serious underlying disease or a history of more serious events as compared to PLHIV who were diagnosed with COVID-19.

ObjectivesTo characterize the demographics, comorbidities, symptoms, in-hospital treatments, and health outcomes among children/adolescents diagnosed or hospitalized with COVID-19. Secondly, to describe health outcomes amongst children/adolescents diagnosed with previous seasonal influenza.DesignInternational network cohort.SettingReal-world data from European primary care records (France/Germany/Spain), South Korean claims and US claims and hospital databases.ParticipantsDiagnosed and/or hospitalized children/adolescents with COVID-19 at age Main outcome measuresBaseline demographics and comorbidities, symptoms, 30-day in-hospital treatments and outcomes including hospitalization, pneumonia, acute respiratory distress syndrome (ARDS), multi-system inflammatory syndrome (MIS-C), and death.ResultsA total of 55,270 children/adolescents diagnosed and 3,693 hospitalized with COVID-19 and 1,952,693 diagnosed with influenza were studied.Comorbidities including neurodevelopmental disorders, heart disease, and cancer were all more common among those hospitalized vs diagnosed with COVID-19. The most common COVID-19 symptom was fever. Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more common in COVID-19 than influenza.In-hospital treatments for COVID-19 included repurposed medications (Hospitalization was observed in 0.3% to 1.3% of the COVID-19 diagnosed cohort, with undetectable (NConclusionsDespite negligible fatality, complications including pneumonia, ARDS and MIS-C were more frequent in children/adolescents with COVID-19 than with influenza. Dyspnea, anosmia and gastrointestinal symptoms could help differential diagnosis. A wide range of medications were used for the inpatient management of pediatric COVID-19.What is already known on this topic?Most of the early COVID-19 studies were targeted at adult patients, and data concerning children and adolescents are limited.Clinical manifestations of COVID-19 are generally milder in the pediatric population compared with adults.Hospitalization for COVID-19 affects mostly infants, toddlers, and children with pre-existing comorbidities.What this study adds⍰This study comprehensively characterizes a large international cohort of pediatric COVID-19 patients, and almost 2 million with previous seasonal influenza across 5 countries.⍰Although uncommon, pneumonia, acute respiratory distress syndrome (ARDS) and multi-system inflammatory syndrome (MIS-C) were more frequent in children and adolescents diagnosed with COVID-19 than in those with seasonal influenza.⍰Dyspnea, bronchiolitis, anosmia and gastrointestinal symptoms were more frequent in COVID-19, and could help to differentiate pediatric COVID-19 from influenza.⍰A plethora of medications were used during the management of COVID-19 in children and adolescents, with great heterogeneity in the use of antiviral therapies as well as of adjunctive therapies.