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Introduction: vaccine-induced thrombotic thrombocytopenia (VITT) has been identified as a rare but serious adverse event associated with coronavirus disease 2019 (COVID-19) vaccines. Objectives: in this study, we explored the pre-pandemic co-occurrence of thrombosis with thrombocytopenia (TWT) using 17 observational health data sources across the world. We applied multiple TWT definitions, estimated the background rate of TWT, characterized TWT patients, and explored the makeup of thrombosis types among TWT patients. Methods: we conducted an international network retrospective cohort study using electronic health records and insurance claims data, estimating background rates of TWT amongst persons observed from 2017 to 2019. Following the principles of existing VITT clinical definitions, TWT was defined as patients with a diagnosis of embolic or thrombotic arterial or venous events and a diagnosis or measurement of thrombocytopenia within 7 days. Six TWT phenotypes were considered, which varied in the approach taken in defining thrombosis and thrombocytopenia in real world data. Results: overall TWT incidence rates ranged from 1.62 to 150.65 per 100,000 person-years. Substantial heterogeneity exists across data sources and by age, sex, and alternative TWT phenotypes. TWT patients were likely to be men of older age with various comorbidities. Among the thrombosis types, arterial thrombotic events were the most common. Conclusion: our findings suggest that identifying VITT in observational data presents a substantial challenge, as implementing VITT case definitions based on the co-occurrence of TWT results in large and heterogeneous incidence rate and in a cohort of patints with baseline characteristics that are inconsistent with the VITT cases reported to date. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 806968. The JU receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. Funders had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

Background: Characterization studies of COVID-19 patients with chronic obstructive pulmonary disease (COPD) are limited in size and scope. The aim of the study is to provide a large-scale characterization of COVID-19 patients with COPD. Methods: We included thirteen databases contributing data from January-June 2020 from North America (US), Europe and Asia. We defined two cohorts of patients with COVID-19 namely a ‘diagnosed’ and ‘hospitalized’ cohort. We followed patients from COVID-19 index date to 30 days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes among COPD patients with COVID-19. Results: The study included 934,778 patients in the diagnosed COVID-19 cohort and 177,201 in the hospitalized COVID-19 cohort. Observed COPD prevalence in the diagnosed cohort ranged from 3.8% (95%CI 3.5-4.1%) in French data to 22.7% (95%CI 22.4-23.0) in US data, and from 1.9% (95%CI 1.6-2.2) in South Korean to 44.0% (95%CI 43.1-45.0) in US data, in the hospitalized cohorts. COPD patients in the hospitalized cohort had greater comorbidity than those in the diagnosed cohort, including hypertension, heart disease, diabetes and obesity. Mortality was higher in COPD patients in the hospitalized cohort and ranged from 7.6% (95%CI 6.9-8.4) to 32.2% (95%CI 28.0-36.7) across databases. ARDS, acute renal failure, cardiac arrhythmia and sepsis were the most common outcomes among hospitalized COPD patients. Conclusion: COPD patients with COVID-19 have high levels of COVID-19-associated comorbidities and poor COVID-19 outcomes. Further research is required to identify patients with COPD at high risk of worse outcomes.

ObjectivesTo assess the uptake of second-line antihyperglycemic agents among patients with type-2 diabetes mellitus (T2DM) receiving metformin.DesignSerial cross-sectional study (2011-2021).SettingTen US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network.Participants4.8 million patients with T2DM receiving metformin.Main Outcomes MeasuresCalendar-year trends in the proportional initiation of second-line antihyperglycemic agents, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), sodium-glucose cotransporter 2 inhibitors (SGLT2is), dipeptidyl peptidase-4 inhibitors, and sulfonylureas, for each database. We also evaluated the relative drug class-level uptake across cardiovascular risk groups.ResultsWe identified 4.6 million patients with T2DM in US databases, 61,382 from Spain, 32,442 from Germany, 25,173 from the UK, 13,270 from France, 5,580 from Scotland, 4,614 from Hong Kong, and 2,322 from Australia. During 2011-2021, the combined proportional initiation of cardioprotective antihyperglycemic agents, GLP-1 RAs and SGLT2is, increased across all data sources, with the combined initiation of these drugs as second-line agents in 2021 ranging from 35.2% to 68.2% in the US databases, 15.4% in France, 34.7% in Spain, 50.1% in Germany, and 54.8% in Scotland. From 2016 to 2021, in some US and non-US databases, uptake of GLP-1 RAs and SGLT2is increased more significantly among populations without cardiovascular disease compared to those with established cardiovascular disease, without any data source providing evidence of a greater increase in their uptake in the populations with cardiovascular disease.ConclusionsDespite the increase in overall uptake of cardioprotective antihyperglycemic agents as second-line treatment for T2DM, their uptake was lower in patients with cardiovascular disease over the last decade. A strategy to ensure medication use concordant with guideline recommendations is essential to improve outcomes of patients with T2DM.

BackgroundAdministrative healthcare claims databases are used in drug safety research but are limited for investigating the impacts of prenatal exposures on neonatal and pediatric outcomes without mother-infant pair identification.ObjectiveWe developed a mother-infant linkage algorithm and evaluated it in two, large US commercially insured populations.Study DesignWe used two US commercial health insurance claims databases during the years 2000 to 2021. Mother-infant links were constructed where persons of female sex 12-55 years of age with a pregnancy episode ending in live birth were associated with a person who was 0 years of age at database entry, who shared a common insurance plan ID, had overlapping insurance coverage time, and whose date of birth was within ±60-days of the mother’s pregnancy episode live birth date. We compared the characteristics of linked vs non-linked mothers and infants to assess similarity.ResultsThe algorithm linked 3,477,960 mothers to 4,160,284 infants in the two databases. Linked mothers and linked infants comprised 73.6% of all mothers and 49.1% of all-infants, respectively. 94.9% of linked infants’ dates of birth were within ±30-days of the associated mother’s pregnancy episode end dates. Linked mothers were older, had longer pregnancy episodes, and had greater post-pregnancy observation time than mothers with live births who did not meet linkage algorithm criteria. Linked infants had less observation time and greater healthcare utilization than non-linked infants. Other characteristics were similar in linked vs non-linked mothers and infants.ConclusionWe developed a mother-infant linkage algorithm and applied it to two US commercial healthcare claims databases that achieved a high linkage proportion and demonstrated that linked and non-linked mother and infant cohorts were similar. Transparent, reusable algorithms applied to large databases enables large-scale research on exposures during pregnancy and pediatric outcomes with relevance to drug safety. These features suggest that prenatal exposure causal risk assessment that uses this algorithm can produce valid and generalizable evidence to inform clinical, policy, and regulatory decisions.Key pointsA. Why was this study conducted?-This study establishes reliable mother-infant links in two US commercial healthcare databases to facilitate research on prenatal exposures and infant health outcomesB. What are the key findings?-Linked mothers with live births comprise 73.6% of all mothers with live births and linked infants comprise 49.1% of all infants-Linked vs. non-linked mother and infant cohorts have similar demographic and clinical profiles-Substantial linked coverage and linked vs non-linked characteristic similarity suggests that prenatal exposure causal risk assessment using the linked cohorts will produce valid and generalizable evidenceC. What does this study add to what is already known?-This study created large mother-infant linked cohorts to enable research on rare exposures and outcomes available in healthcare claims databases-Linked mother and infant coverage is similar to that reported in previous linkage studies-Descriptive comparisons between linked vs. non-linked mother and infant cohorts increases confidence that results from research on linked cohorts also apply to mother and infant populations that do not meet linkage algorithm criteria-This mother-infant linkage algorithm is publicly available and easily implemented in databases converted to a common data model

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.

Background We estimated stroke risk associated with new exposure to haloperidol, or any typical antipsychotic, versus atypical antipsychotic among patients aged ≥65 years regardless of dementia status. Methods IBM MarketScan Medicare Supplemental Database data (January 1, 2001 to December 31, 2017) were used. Stroke risk for new users of typical antipsychotics (T1 cohort) or haloperidol (T2 cohort) was compared with new users of atypical antipsychotics (C1 cohort) aged ≥65 years. Crude incidence rate (IR) and incidence proportion of stroke were estimated within each cohort and gender subgroup. Three propensity score (PS) matching strategies were employed: Unadjusted (crude), Sentinel PS replication, and a large-scale regularized regression model (adapted PS). Results Overall, 36,734 (T1), 24,074 (T2), and 226,990 (C1) patients were included. Crude IRs for stroke per 1000 person-years were 17.67 (T1), 23.74 (T2), and 14.17 (C1). In preplanned analyses, PS-matched calibrated hazard ratio (cHR) for stroke T1 versus C1 cohort was 1.08 (95% calibrated confidence interval [cCI] = 0.75, 1.55) with Sentinel PS strategy and 1.31 (95% cCI = 1.07, 1.60) with adapted PS strategy. The cHR for stroke in patients of T2 versus C1 was 1.69 (95% cCI = 1.08, 2.75) with Sentinel PS strategy and 1.45 (95% cCI = 1.17, 1.80) with adapted PS strategy. Conclusion Stroke risk in elderly new users of haloperidol was elevated compared to new users of atypical antipsychotics and was elevated for typical antipsychotics using the adapted PS strategy.

Objective:Large international comparisons describing the clinical characteristics of patients with COVID-19 are limited. The aim of the study was to perform a large-scale descriptive characterization of COVID-19 patients with asthma. Methods:We included nine databases contributing data from January to June 2020 from the US, South Korea (KR), Spain, UK and the Netherlands. We defined two cohorts of COVID-19 patients (‘diagnosed’ and ‘hospitalized’) based on COVID-19 disease codes. We followed patients from COVID-19 index date to 30days or death. We performed descriptive analysis and reported the frequency of characteristics and outcomes in people with asthma defined by codes and prescriptions.Results:The diagnosed and hospitalized cohorts contained 666,933 and 159,552 COVID-19 patients respectively. Exacerbation in people with asthma was recorded in 1.6–8.6% of patients at presentation. Asthma prevalence ranged from 6.2% (95% CI 5.7–6.8) to 18.5% (95% CI 18.2–18.8) in the diagnosed cohort and 5.2% (95% CI 4.0–6.8) to 20.5% (95% CI 18.6–22.6) in the hospitalized cohort. Asthma patients with COVID-19 had high prevalence of comorbidity including hypertension, heart disease, diabetes and obesity. Mortality ranged from 2.1% (95% CI 1.8–2.4) to 16.9% (95% CI 13.8–20.5) and similar or lower compared to COVID-19 patients without asthma. Acute respiratory distress syndrome occurred in 15–30% of hospitalized COVID-19 asthma patients. Conclusion:The prevalence of asthma among COVID-19 patients varies internationally. Asthma patients with COVID-19 have high comorbidity. The prevalence of asthma exacerbation at presentation was low. Whilst mortality was similar among COVID-19 patients with and without asthma, this could be confounded by differences in clinical characteristics. Further research could help identify high-risk asthma patients.

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

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).

BackgroundFew datasets have been established that capture the full breadth of COVID-19 patient interactions with a health system. Our first objective was to create a COVID-19 dataset that linked primary care data to COVID-19 testing, hospitalisation, and mortality data at a patient level. Our second objective was to provide a descriptive analysis of COVID-19 outcomes among the general population and describe the characteristics of the affected individuals.MethodsWe mapped patient-level data from Catalonia, Spain, to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). More than 3,000 data quality checks were performed to assess the readiness of the database for research. Subsequently, to summarise the COVID-19 population captured, we established a general population cohort as of the 1st March 2020 and identified outpatient COVID-19 diagnoses or positive test results for SARS-CoV-2, hospitalisations with COVID-19, and COVID-19 deaths during follow-up, which went up until 30th June 2021.FindingsMapping data to the OMOP CDM was performed and high data quality was observed. The mapped database was used to identify a total of 5,870,274 individuals, who were included in the general population cohort as of 1st March 2020. Over follow up, 604,472 had either an outpatient COVID-19 diagnosis or positive test result, 58,991 had a hospitalisation with COVID-19, 5,642 had an ICU admission with COVID-19, and 11,233 had a COVID-19 death. People who were hospitalised or died were more commonly older, male, and with more comorbidities. Those admitted to ICU with COVID-19 were generally younger and more often male than those hospitalised in general and those who died.InterpretationWe have established a comprehensive dataset that captures COVID-19 diagnoses, test results, hospitalisations, and deaths in Catalonia, Spain. Extensive data checks have shown the data to be fit for use. From this dataset, a general population cohort of 5.9 million individuals was identified and their COVID-19 outcomes over time were described.FundingGeneralitat de Catalunya and European Health Data and Evidence Network (EHDEN).

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.

Objective Advances in standardization of observational healthcare data have enabled methodological breakthroughs, rapid global collaboration, and generation of real-world evidence to improve patient outcomes. Standardizations in data structure, such as use of common data models, need to be coupled with standardized approaches for data quality assessment. To ensure confidence in real-world evidence generated from the analysis of real-world data, one must first have confidence in the data itself. Materials and Methods We describe the implementation of check types across a data quality framework of conformance, completeness, plausibility, with both verification and validation. We illustrate how data quality checks, paired with decision thresholds, can be configured to customize data quality reporting across a range of observational health data sources. We discuss how data quality reporting can become part of the overall real-world evidence generation and dissemination process to promote transparency and build confidence in the resulting output. Results The Data Quality Dashboard is an open-source R package that reports potential quality issues in an OMOP CDM instance through the systematic execution and summarization of over 3300 configurable data quality checks. Discussion Transparently communicating how well common data model-standardized databases adhere to a set of quality measures adds a crucial piece that is currently missing from observational research. Conclusion Assessing and improving the quality of our data will inherently improve the quality of the evidence we generate.

Background Previous studies have reported an increased risk of stroke in patients taking antipsychotics. However, most of these studies have been conducted in the elderly population. Objective We estimated stroke risk in new users of any first-generation antipsychotic or haloperidol, vs second-generation antipsychotics among patients aged 18–64 years without a recent dementia diagnosis and, separately, regardless of a recent dementia diagnosis. Methods Data were obtained from IBM MarketScan® Commercial Database (1 January, 2001–31 December, 2017). Among new users without a recent dementia diagnosis, stroke risk for first-generation antipsychotics (FGAw/oD cohort) or haloperidol (HALw/oD cohort) was compared with second-generation antipsychotics (SGAw/oD cohort). A similar comparison was conducted among new users regardless of dementia diagnosis: first-generation antipsychotics (FGA cohort) or haloperidol (HAL cohort) vs second-generation antipsychotics (SGA cohort). Crude incident stroke rates within each cohort were determined. For hazard ratios, three propensity score matching strategies were used: unadjusted (crude), Sentinel propensity score strategy, and large-scale regularized regression model (adapted propensity score strategy). Results Each cohort included ≥12,000 patients. The incident rates for stroke per 1000 person-years were 3.10 (FGAw/oD), 5.99 (HALw/oD), 0.85 (SGAw/oD), 3.14 (FGA), 6.12 (HAL), and 0.90 (SGA). Pre-planned analysis with adapted propensity score strategy matching yielded calibrated hazard ratios for stroke: FGAw/oD vs SGAw/oD: 2.05 (calibrated confidence interval 1.13–3.89); HALw/oD vs SGAw/oD: 2.47 (1.14–5.48), FGA vs SGA: 1.64 (0.94–2.97), and HAL vs SGA: 1.98 (0.99–4.00). A post-hoc sensitivity analysis to address potential bias introduced by the 2015 change from the International Classification of Diseases, Ninth Revision to the International Classification of Diseases, Tenth Revision yielded calibrated hazard ratios for FGAw/oD vs SGAw/oD: 1.59 (0.87–3.01), HALw/oD vs SGAw/oD: 2.79 (1.24–6.42), FGA vs SGA: 1.41 (0.79–2.62), and HAL vs SGA: 3.47 (1.63–7.92). Conclusions Among adults aged ≤64 years, without a recent dementia diagnosis, stroke risk is higher among those exposed to haloperidol compared with those exposed to second-generation antipsychotics. Supplementary Information The online version contains supplementary material available at 10.1007/s40801-021-00267-2.

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.

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.

Objective To estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). Design A network cohort study. Setting Six databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. Patients Patients hospitalized with a clinical diagnosis or a positive test result for COVID-19. Interventions Dialysis, tracheostomy, and ECMO. Measurements and Main Results 240,392 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 139,971 from IQVIA Open Claims, 29,061 from Optum EHR, 4,336 from OPTUM SES, 36,019 from Premier, and 8,118 from VA-OMOP). Across the six databases, 9,703 (4.04% [95% CI: 3.96% to 4.11%]) patients received dialysis, 1,681 (0.70% [0.67% to 0.73%]) had a tracheostomy, and 398 (0.17% [95% CI: 0.15% to 0.18%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was generally concentrated among patients who were younger, male, and with fewer comorbidities except for obesity. Tracheostomy was used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. Conclusion Use of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial and can be expected to continue grow given the continuing spread of the 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.

Early identification of symptoms and comorbidities most predictive of COVID-19 is critical to identify infection, guide policies to effectively contain the pandemic, and improve health systems’ response. Here, we characterised socio-demographics and comorbidity in 3,316,107persons tested and 219,072 persons tested positive for SARS-CoV-2 since January 2020, and their key health outcomes in the month following the first positive test. Routine care data from primary care electronic health records (EHR) from Spain, hospital EHR from the United States (US), and claims data from South Korea and the US were used. The majority of study participants were women aged 18-65 years old. Positive/tested ratio varied greatly geographically (2.2:100 to 31.2:100) and over time (from 50:100 in February-April to 6.8:100 in May-June). Fever, cough and dyspnoea were the most common symptoms at presentation. Between 4%-38% required admission and 1-10.5% died within a month from their first positive test. Observed disparity in testing practices led to variable baseline characteristics and outcomes, both nationally (US) and internationally. Our findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave.

OBJECTIVESTo describe comorbidities, symptoms at presentation, medication use, and 30-day outcomes after a diagnosis of COVID-19 in pregnant women, in comparison to pregnant women with influenza.DESIGNMultinational network cohortSETTINGA total of 6 databases consisting of electronic medical records and claims data from France, Spain, and the United States.PARTICIPANTSPregnant women with ≥ 1 year in contributing databases, diagnosed and/or tested positive, or hospitalized with COVID-19. The influenza cohort was derived from the 2017-2018 influenza season.OUTCOMESBaseline patient characteristics, comorbidities and presenting symptoms; 30-day inpatient drug utilization, maternal complications and pregnancy-related outcomes following diagnosis/hospitalization.RESULTS8,598 women diagnosed (2,031 hospitalized) with COVID-19 were included. Hospitalized women had, compared to those diagnosed, a higher prevalence sof pre-existing comorbidities including renal impairment (2.2% diagnosed vs 5.1% hospitalized) and anemia (15.5% diagnosed vs 21.3% hospitalized).The ten most common inpatient treatments were systemic corticosteroids (29.6%), enoxaparin (24.0%), immunoglobulins (21.4%), famotidine (20.9%), azithromycin (18.1%), heparin (15.8%), ceftriaxone (7.9%), aspirin (7.0%), hydroxychloroquine (5.4%) and amoxicillin (3.5%).Compared to 27,510 women with influenza, dyspnea and anosmia were more prevalent in those with COVID-19. Women with COVID-19 had higher frequency of cesarean-section (4.4% vs 3.1%), preterm delivery (0.9% vs 0.5%), and poorer maternal outcomes: pneumonia (12.0% vs 2.7%), ARDS (4.0% vs 0.3%) and sepsis (2.1% vs 0.7%). COVID-19 fatality was negligible (NCONCLUSIONSComorbidities that were more prevalent with COVID-19 hospitalization (compared to COVID-19 diagnosed) in pregnancy included renal impairment and anemia. Multiple medications were used to treat pregnant women hospitalized with COVID-19, some with little evidence of benefit. Anosmia and dyspnea were indicative symptoms of COVID-19 in pregnancy compared to influenza, and may aid differential diagnosis. Despite low fatality, pregnancy and maternal outcomes were worse in COVID-19 than influenza.WHAT IS ALREADY KNOWN ON THIS TOPICCompared to non-pregnant women of reproductive age, pregnant women are less likely to experience typical COVID-19 symptoms, such as fever and myalgia.Obesity, high maternal age, and comorbid hypertension and diabetes are risk factors for severe COVID-19 among pregnant women.Despite relatively high rates of pneumonia and need for oxygen supplementation, fatality rates in pregnant women with COVID-19 are generally low (WHAT THIS STUDY ADDSAlthough not often recorded, dyspnea and anosmia were more often seen in pregnant women with COVID-19 than in women with seasonal influenza, in 6 databases from 3 countries (US, France, Spain).Renal impairment and anemia were more common among hospitalized than diagnosed women with COVID-19 during pregnancy.Despite limited data on benefit-risk in pregnancy, a large number of medications were used for inpatient management of COVID-19 in pregnant women: approximately 1 in 3 received corticosteroids (some may have been given for a pregnancy-related indication rather than for COVID-19 treatment), 1 in 4 enoxaparin, and 1 in 5 immunoglobulin, famotidine and azithromycin.Compared to influenza, there was a higher frequency of pregnancy-related complications (cesarean section and preterm deliveries), as well as poorer maternal outcomes (pneumonia, acute respiratory distress syndrome, sepsis, acute kidney injury, and cardiovascular and thromboembolic events) seen in pregnant women diagnosed with COVID-19.

Reports suggest low levels of cholesterol are associated with depression. However, results have not been replicated, the direction of the associations among types of cholesterol levels is not consistent, there is large study heterogeneity, and many studies have small samples.The objective of the study was to assess the association of cholesterol with depression.This is a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES). The NHANES is a research program that collects health information from a representative U.S.We included subjects aged ≥18 years who responded to NHANES surveys from 2009 to 2015. Subjects were classified as having major depression if the Patient Health Questionnaire scores were ≥10. Exposures were total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride levels. We considered the age, body mass index, gender, smoking, alcohol use, health status, and exposure to statins and antipsychotics as potential confounders. To assess the association of the exposures with depression, we used decision tree and logistic regression models.A total of 19,527 subjects were analyzed, and 8% had depression. Subjects with depression were more likely to be women and smokers, and to have higher body mass index, poor health, higher levels of total cholesterol and triglycerides and lower levels of high-density lipoprotein cholesterol than subjects with no depression. After adjustment, low levels of total cholesterol (129 mg/dL) were associated with decreased risk of depression compared with higher levels, OR = 0.64 and 95% CI (0.42-0.98).This large population-based study found no association of low cholesterol or any other lower type of cholesterol levels with increased risk of depression. These findings are generalizable to the U.S.

Background Depression that does not respond to antidepressants is treatment-resistant depression (TRD). TRD definitions include assessments of treatment response, dose and duration, and implementing these definitions in claims databases can be challenging. We built a data-driven TRD definition and evaluated its performance. Methods We included adults with depression, ≥1 antidepressant, and no diagnosis of mania, dementia, or psychosis. Subjects were stratified into those with and without proxy for TRD. Proxies for TRD were electroconvulsive therapy, deep brain, or vagus nerve stimulation. The index date for subjects with proxy for TRD was the procedure date, and for subjects without, the date of a randomly selected visit. We used three databases. We fit decision tree predictive models. We included number of distinct antidepressants, with and without adequate doses and duration, number of antipsychotics and psychotherapies, and expert-based definitions, 3, 6, and 12 months before index date. To assess performance, we calculated area under the curve (AUC) and transportability. Results We analyzed 33,336 subjects with no proxy for TRD, and 3,566 with the proxy. Number of antidepressants and antipsychotics were selected in all periods. The best model was at 12 months with an AUC = 0.81. The rule transported well and states that a subject with ≥1 antipsychotic or ≥3 antidepressants in the last year has TRD. Applying this rule, 15.8% of subjects treated for depression had TRD. Conclusion The definition that best discriminates between subjects with and without TRD considers number of distinct antidepressants (≥3) or antipsychotics (≥1) in the last year.

Aim Assess the association of flossing with periodontitis. Materials and Methods This was a cross-sectional study using the National Health and Nutrition Examination Survey (NHANES) years 2011-2014. We used three categories of flossing: 0–1, 2–4 and ≥5 days in the past week and the CDC definition of periodontitis. We calculated odds ratios controlling for age, gender, smoking, drinking, income and dentist visits. Results A total of 6939 adult subjects were included, 35% flossed ≤1 time a week, and 40% had periodontitis. After adjustment, the odds of periodontitis were 17% lower for subjects who flossed >1 time a week than for subjects who flossed less often (odds ratio=0.83, 95% CI 0.72–0.97). A dose response was not observed. Men were twice as likely as women to have periodontitis. Younger subjects, non-smokers and subjects with the highest incomes had lower odds of having periodontitis. Conclusions Flossing was associated with a modestly lower prevalence of periodontitis. Older age, being male, smoking, low income and less frequent dental visits were associated with a higher prevalence of periodontitis. Flossing 2–4 days a week could be as beneficial as flossing more frequently. This is a cross-sectional study so a causal relation between flossing and periodontitis cannot be established.

The Data Quality Dashboard (DQD) developed as part of EHDEN Work Package 4 provides a comprehensive, customizable, and transparent way to both evaluate and communicate the quality of an OMOP CDM instance. It provides both the code to run data quality checks against an OMOP CDM instance, as well as visualising the results in a web application. The data quality checks were organised using the widely accepted Kahn Framework for data quality. This groups the checks types in categories: Conformance, Completeness and Plausibility. Each category has one or more subcategories and are evaluated in two contexts: Validation and Verification. Each SME and data partner will be expected to run the Data Quality Dashboard on the data at their site once it is converted to the OMOP CDM. Initially it can be used to assess whether OMOP Standards such as primary key constraints and concept domain restrictions are being followed. Thinking specifically of the EHDEN federated network, providing an interactive data quality report for each participating site will provide evidence not only that OMOP specifications were followed correctly but also to demonstrate that the necessary due diligence was done to ensure that the data are of research quality. We believe the DQD is a strong foundation for data quality reporting, and a key tool to ensure confidence in the evidence generated through the EHDEN federated network.

Background: Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine. Objective: The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice. Methods: Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations. Results: The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation. Conclusions: We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

BACKGROUND Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine. OBJECTIVE The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice. METHODS Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations. RESULTS The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation. CONCLUSIONS We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Background The objective of this study was to estimate how commonly patients with pharmacologically treated depression (PTD) do not receive adequate doses of antidepressant (AD) medications. Such prescribing would have epidemiologic and clinical implications. Patients with PTD have treatment-resistant depression (TRD) if they do not benefit from ≥ 2 AD medications taken with reasonable compliance for adequate durations at adequate doses. Some database studies of TRD do not assess AD medication dose and would, therefore, overestimate TRD incidence unless physicians treating PTD patients routinely prescribe AD medications at adequate doses before changing medications. Methods Using data from 3 US health services databases from September 1, 2010, through December 31, 2014, we created PTD cohorts and defined an AD medication era as a sequence of dispensings with ≤ 30 days between the end of the days' supply of each dispensing and the start of the next. We classified AD medication eras according to whether they had ≥ 1 dispensing at or above the minimum therapeutic dose. Results The proportion of AD medication eras with ≥ 1 dose at or above the minimum therapeutic dose varied from 59.6% in the Medicaid database to 66.0% in a database of privately insured patients. Conclusions In the population at risk for TRD, a substantial proportion of AD medication dispensing eras do not reach the minimum therapeutic dose. TRD incidence is likely to be overestimated in database studies that do not take account of dose. Clinicians should be aware that AD medication regimens are often stopped without reaching the minimum therapeutic dose, which may cause unnecessary switching.

To assess the association of probiotics with depression, a large population-based cross-sectional study was conducted. National Health and Nutrition Examination Survey adult participants from 2005 through 2012 were included. Exposure was defined as having consumed any probiotic food or supplement on any of the interview days. Subjects were classified as depressed if Patient Health Questionnaire scores were ≥10. Of the 18,019 subjects included, 14.11% consumed probiotics. Unadjusted analysis suggested that subjects who consumed probiotics had lower odds of depression (OR=0.58, 95% CI=0.45-0.75). After adjustment for characteristics associated with depression and probiotic exposure, the effect was attenuated (OR=0.82, 95% CI=0.61-1.1) and no longer significant. Use of probiotics is not associated with lower rates of depression in this national sample.

To assess the clinical relevance of sleep duration, hours slept were compared by health status, presence of insomnia, and presence of depression, and the association of sleep duration with BMI and cardiovascular risk was quantified.Cross-sectional analysis of subjects in the US National Health and Nutrition Examination Surveys using adjusted linear and logistic regressions.A total of 22,281 adults were included, 37% slept ≤ 6 hours, 36% were obese, and 45% reported cardiovascular conditions. Mean sleep duration was 6.87 hours. Better health was associated with more hours of sleep. Subjects with poor health reported sleeping 46 min, (95% CI -56.85 to -35.67) less than subjects with excellent health. Individuals with depression (vs. not depressed) reported 40 min less sleep, (95% CI -47.14 to -32.85). Individuals with insomnia (vs. without insomnia) reported 39 min less sleep, (95% CI -56.24 to -22.45). Duration of sleep was inversely related to BMI; for every additional hour of sleep, there was a decrease of 0.18 kg/m(2) in BMI, (95% CI -0.30 to -0.06). The odds of reporting cardiovascular problems were 6.0% lower for every hour of sleep (odds ratio = 0.94, 95% CI [0.91 to 0.97]). Compared with subjects who slept ≤ 6 h, subjects who slept more had lower odds of reporting cardiovascular problems, with the exception of subjects ≥ 55 years old who slept ≥ 9 hours.Long sleep duration is associated with better health. The fewer the hours of sleep, the greater the BMI and reported cardiovascular disease. A difference of 30 minutes of sleep is associated with substantive impact on clinical well-being.