Search

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.

Objective: Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk for acute pancreatitis (AP) versus those without T2DM. A small number of AP events were reported in clinical trials of the sodium glucose co-transporter 2 inhibitor canagliflozin, though no imbalances were observed between treatment groups. This observational study evaluated risk of AP among new users of canagliflozin compared with new users of six classes of other antihyperglycemic agents (AHAs). Methods: Three US claims databases were analyzed based on a prespecified protocol approved by the European Medicines Agency. Propensity score adjustment controlled for imbalances in baseline covariates. Cox regression models estimated the hazard ratio of AP with canagliflozin compared with other AHAs using on-treatment (primary) and intent-to-treat approaches. Sensitivity analyses assessed robustness of findings. Results: Across the three databases, there were between 12,023–80,986 new users of canagliflozin; the unadjusted incidence rates of AP (per 1000 person-years) were between 1.5–2.2 for canagliflozin and 1.1–6.6 for other AHAs. The risk of AP was generally similar for new users of canagliflozin compared with new users of glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors, sulfonylureas, thiazolidinediones, insulin, and other AHAs, with no consistent between-treatment differences observed across databases. Intent-to-treat and sensitivity analysis findings were qualitatively consistent with on-treatment findings. Conclusions: In this large observational study, incidence rates of AP in patients with T2DM treated with canagliflozin or other AHAs were generally similar, with no evidence suggesting that canagliflozin is associated with increased risk of AP compared with other AHAs.

Background Understanding how patients are treated in the real-world is vital to identifying potential gaps in care. We describe the current pharmacologic treatment patterns for the treatment of depression. Methods Patients with depression were identified from four large national claims databases during 1/1/2014–1/31/2019. Patients had ≥2 diagnoses for depression or an inpatient hospitalization with a diagnosis of depression. Patients were required to have enrollment in the database ≥1 year prior to and 3 years following their first depression diagnosis. Treatment patterns were captured at the class level and included selective serotonin reuptake inhibitors (SSRIs), serotonin and norepinephrine reuptake inhibitors, tricyclic antidepressants, other antidepressants, anxiolytics, hypnotics/sedatives, and antipsychotics. Treatment patterns were captured during all available follow-up. Results We identified 269,668 patients diagnosed with depression. The proportion not receiving any pharmacological treatment during follow-up ranged from 29 to 52%. Of the treated, approximately half received ≥2 different classes of therapy, a quarter received ≥3 classes and more than 10% received 4 or more. SSRIs were the most common first-line treatment; however, many patients received an anxiolytic, hypnotic/sedative, or antipsychotic prior to any antidepressive treatment. Treatment with a combination of classes ranged from approximately 20% of first-line therapies to 40% of fourth-line. Conclusions Many patients diagnosed with depression go untreated and many others receive a non-antidepressant medication class as their first treatment. More than half of patients received more than one type of treatment class during the study follow up, suggesting that the first treatment received may not be optimal for most 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

Background: Observational health data has the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Col-laboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Methods: 15 Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. Results: All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance is-sues but showed less of an impact on completeness and plausibility checks. Conclusions: This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.

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.

Purpose To compare the incidence of diabetic ketoacidosis (DKA) among patients with type 2 diabetes mellitus (T2DM) who were new users of sodium glucose co‐transporter 2 inhibitors (SGLT2i) versus other classes of antihyperglycemic agents (AHAs). Methods Patients were identified from four large US claims databases using broad (all T2DM patients) and narrow (intended to exclude patients with type 1 diabetes or secondary diabetes misclassified as T2DM) definitions of T2DM. New users of SGLT2i and seven groups of comparator AHAs were matched (1:1) on exposure propensity scores to adjust for imbalances in baseline covariates. Cox proportional hazards regression models, conditioned on propensity score‐matched pairs, were used to estimate hazard ratios (HRs) of DKA for new users of SGLT2i versus other AHAs. When I2

The true risk of a COVID-19 resurgence as states reopen businesses is unknown. In this paper, we used anonymized cell-phone data to quantify the potential risk of COVID-19 transmission in business establishments by building a Business Risk Index that measures transmission risk over time. The index was built using two metrics, visits per square foot and the average duration of visits, to account for both density of visits and length of time visitors linger in the business. We analyzed trends in traffic patterns to 1,272,260 businesses across eight states from January 2020 to June 2020. We found that potentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. There are some notable differences in trends across states and industries. Finally, we showed that the time series of the average Business Risk Index is useful for forecasting future COVID-19 cases at the county-level (P 0.001). We found that an increase in a county's average Business Risk Index is associated with an increase in positive COVID-19 cases in 1 week (IRR: 1.16, 95% CI: (1.1-1.26)). Our risk index provides a way for policymakers and hospital decision-makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. This can serve as an important metric as states monitor and evaluate their reopening strategies.

ImportanceThe United States has the highest number of confirmed COVID-19 cases in the world, with over 150,000 COVID-19-related deaths as of July 31, 20201. The true risk of a COVID-19 resurgence as states prepare to reopen businesses is unknown.ObjectiveTo quantify the potential risk of COVID-19 transmission in business establishments by building a risk index for each business that measures transmission risk over time.DesignThis retrospective case series study uses anonymized cell phone GPS data to analyze trends in traffic patterns to businesses that may be potentially high-risk from January 2020 to June 2020.SettingMassachusetts, Rhode Island, Connecticut, New Hampshire, Vermont, Maine, New York, and California.Participants1,272,260 businesses within 8 states from January 2020 – June 2020.Exposure(s)We monitored business traffic before the pandemic, during the pandemic and after early phases of reopening in 8 states.Main OutcomeOur primary outcome is our business risk index. The index was built using two metrics: visitors per square foot and the average duration of visits. Visitors per square foot account for how densely visitors are packed into businesses. The average duration of visits accounts for the length of time visitors are spending in a business.ResultsPotentially risky traffic behaviors at businesses decreased by 30% by April. Since the end of April, the risk index has been increasing as states reopen. On average, it has increased between 10 to 20 percentage points since April and is moving towards pre-pandemic levels of traffic. There are some notable differences in trends across states and industries.ConclusionOur risk index provides a way for policymakers and hospital decision makers to monitor the potential risk of COVID-19 transmission from businesses based on the frequency and density of visits to businesses. Traffic is slowly moving towards pre-pandemic levels. This can serve as an important metric as states monitor and evaluate their reopening strategies.

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.

Background To better understand the profile of individuals with severe coronavirus disease 2019 (COVID-19), we characterised individuals hospitalised with COVID-19 and compared them to individuals previously hospitalised with influenza. Methods We report the characteristics (demographics, prior conditions and medication use) of patients hospitalised with COVID-19 between December 2019 and April 2020 in the US (Columbia University Irving Medical Center [CUIMC], STAnford Medicine Research data Repository [STARR-OMOP], and the Department of Veterans Affairs [VA OMOP]) and Health Insurance Review & Assessment [HIRA] of South Korea. Patients hospitalised with COVID-19 were compared with patients previously hospitalised with influenza in 2014–19. Results 6,806 (US: 1,634, South Korea: 5,172) individuals hospitalised with COVID-19 were included. Patients in the US were majority male (VA OMOP: 94%, STARR-OMOP: 57%, CUIMC: 52%), but were majority female in HIRA (56%). Age profiles varied across data sources. Prevalence of asthma ranged from 7% to 14%, diabetes from 18% to 43%, and hypertensive disorder from 22% to 70% across data sources, while between 9% and 39% were taking drugs acting on the renin-angiotensin system in the 30 days prior to their hospitalisation. Compared to 52,422 individuals hospitalised with influenza, patients admitted with COVID-19 were more likely male, younger, and, in the US, had fewer comorbidities and lower medication use. Conclusions Rates of comorbidities and medication use are high among individuals hospitalised with COVID-19. However, COVID-19 patients are more likely to be male and appear to be younger and, in the US, generally healthier than those typically admitted with influenza.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19., Detailed knowledge of the characteristics of COVID-19 patients helps with public health planning. Here, the authors use routinely-collected data from seven databases in three countries to describe the characteristics of >30,000 patients admitted with COVID-19 and compare them with those admitted for influenza in previous years.

Sodium glucose co-transporter 2 inhibitors (SGLT2i) are indicated for treatment of type 2 diabetes mellitus (T2DM); some SGLT2i have reported cardiovascular benefit, and some have reported risk of below-knee lower extremity (BKLE) amputation. This study examined the real-world comparative effectiveness within the SGLT2i class and compared with non-SGLT2i antihyperglycaemic agents.Data from 4 large US administrative claims databases were used to characterize risk and provide population-level estimates of canagliflozin's effects on hospitalization for heart failure (HHF) and BKLE amputation vs other SGLT2i and non-SGLT2i in T2DM patients. Comparative analyses using a propensity score-adjusted new-user cohort design examined relative hazards of outcomes across all new users and a subpopulation with established cardiovascular disease.Across the 4 databases (142 800 new users of canagliflozin, 110 897 new users of other SGLT2i, 460 885 new users of non-SGLT2i), the meta-analytic hazard ratio estimate for HHF with canagliflozin vs non-SGLT2i was 0.39 (95% CI, 0.26-0.60) in the on-treatment analysis. The estimate for BKLE amputation with canagliflozin vs non-SGLT2i was 0.75 (95% CI, 0.40-1.41) in the on-treatment analysis and 1.01 (95% CI, 0.93-1.10) in the intent-to-treat analysis. Effects in the subpopulation with established cardiovascular disease were similar for both outcomes. No consistent differences were observed between canagliflozin and other SGLT2i.In this large comprehensive analysis, canagliflozin and other SGLT2i demonstrated HHF benefits consistent with clinical trial data, but showed no increased risk of BKLE amputation vs non-SGLT2i. HHF and BKLE amputation results were similar in the subpopulation with established cardiovascular disease. This study helps further characterize the potential benefits and harms of SGLT2i in routine clinical practice to complement evidence from clinical trials and prior observational studies.

Aims To examine the incidence of amputation in patients with type 2 diabetes mellitus (T2DM) treated with sodium glucose co‐transporter 2 (SGLT2) inhibitors overall, and canagliflozin specifically, compared with non‐SGLT2 inhibitor antihyperglycaemic agents (AHAs). Materials and Methods Patients with T2DM newly exposed to SGLT2 inhibitors or non‐SGLT2 inhibitor AHAs were identified using the Truven MarketScan database. The incidence of below‐knee lower extremity (BKLE) amputation was calculated for patients treated with SGLT2 inhibitors, canagliflozin, or non‐SGLT2 inhibitor AHAs. Patients newly exposed to canagliflozin and non‐SGLT2 inhibitor AHAs were matched 1:1 on propensity scores, and a Cox proportional hazards model was used for comparative analysis. Negative controls (outcomes not believed to be associated with any AHA) were used to calibrate P values. Results Between April 1, 2013 and October 31, 2016, 118 018 new users of SGLT2 inhibitors, including 73 024 of canagliflozin, and 226 623 new users of non‐SGLT2 inhibitor AHAs were identified. The crude incidence rates of BKLE amputation were 1.22, 1.26 and 1.87 events per 1000 person‐years with SGLT2 inhibitors, canagliflozin and non‐SGLT2 inhibitor AHAs, respectively. For the comparative analysis, 63 845 new users of canagliflozin were matched with 63 845 new users of non‐SGLT2 inhibitor AHAs, resulting in well‐balanced baseline covariates. The incidence rates of BKLE amputation were 1.18 and 1.12 events per 1000 person‐years with canagliflozin and non‐SGLT2 inhibitor AHAs, respectively; the hazard ratio was 0.98 (95% confidence interval 0.68–1.41; P = .92, calibrated P = .95). Conclusions This real‐world study observed no evidence of increased risk of BKLE amputation for new users of canagliflozin compared with non‐SGLT2 inhibitor AHAs in a broad population of patients with T2DM.

Observational evidence suggests that patients with type 2 diabetes mellitus (T2DM) are at increased risk of AP. A small number of AP events were reported in clinical trials of the SGLT2 inhibitor CANA, with no imbalances noted between treatment groups. This observational study evaluated the risk of AP among new users of CANA vs. 6 classes of other AHAs. Three U.S. claims databases were analyzed based on a prespecified protocol approved by the European Medicines Agency. Propensity score adjustment controlled for imbalances in baseline covariates. Cox regression models were used to estimate the hazard ratio of AP with CANA vs. other AHAs using on-treatment and intent-to-treat (ITT) approaches. Sensitivity analyses assessed the robustness of study findings. Across the 3 databases, there were between 12,023 to 80,986 new users of CANA and the crude incidence rates of AP (events per 1000 person-years) were between 1.5 to 2.2 for CANA and 1.1 to 6.6 for other AHAs. AP risk in patients newly treated with CANA or other AHAs was generally similar (Table). Findings from the ITT approach and sensitivity analyses were qualitatively consistent with the on-treatment approach. In this large observational study, incidence rates of AP in patients with T2DM treated with CANA or other AHAs were generally similar, with no evidence to suggest that CANA is associated with an increased risk of AP compared with other AHAs. Disclosure Z. Yuan: Employee; Self; Janssen Research & Development. F. Defalco: Employee; Self; Janssen Research & Development. L. Wang: Employee; Self; Johnson & Johnson. L.L. Hester: Employee; Self; Janssen Research & Development. J.N. Swerdel: Employee; Self; Janssen Pharmaceuticals, Inc. A. Freedman: Employee; Self; Janssen Research & Development. P. Ryan: Employee; Self; Janssen Research & Development. M. Schuemie: Employee; Self; Janssen Research & Development. R. Qiu: Employee; Self; Janssen Research & Development. J. Yee: Employee; Self; Janssen Research & Development. G. Meininger: Employee; Self; Janssen Research & Development. J.A. Berlin: Employee; Self; Janssen Research & Development. Stock/Shareholder; Self; Janssen Research & Development. N. Rosenthal: Employee; Self; Janssen Research & Development. Funding Janssen Research & Development, LLC

Introduction: Diabetic ketoacidosis (DKA) is a serious acute metabolic complication of diabetes. Rare DKA events have occurred in patients taking sodium glucose co-transporter 2 inhibitors (SGLT2i). This study evaluated the risk of DKA in patients with type 2 diabetes mellitus (T2DM) taking SGLT2i versus other antihyperglycemic agents (AHAs) in clinical practice. Methods: This study, per protocol reviewed and approved by the European Medicines Agency, identified patients from 4 large US claims databases using broad and narrow definitions of T2DM; the broad definition captured all patients with a T2DM diagnosis and the narrow definition was intended to exclude T1DM misclassified as T2DM. DKA was identified from diagnosis codes in inpatient or emergency room claims. Eligible new users of SGLT2i and 7 groups of AHA comparators were matched (1:1) on exposure propensity scores (PS) to adjust for imbalances in baseline covariates. Cox proportional hazard models conditioned on PS-matched pairs were used to estimate hazard ratios (HR) of DKA risk for new users of SGLT2i versus other AHAs. P values were calibrated using negative control outcomes to address potential residual bias. Pooled HRs were calculated when I 2 was Results: The number of new users of SGLT2i in each database ranged from 11,141 to 152,728 using the broad T2DM definition and from 7,779 to 130,708 using the narrow definition. Across databases, the unadjusted incidence rates of DKA (events per 1000 patient-years) ranged from 2.75 to 8.84 with SGLT2i and 1.38 to 15.82 with other AHAs using the broad T2DM definition and from 1.15 to 3.91 with SGLT2i and 0.75 to 7.94 with other AHAs using the narrow definition. Using the broad T2DM definition, a significantly increased risk of DKA was observed among new users of SGLT2i versus 5 groups of other AHAs; when using the narrow definition, an increased risk of DKA with SGLT2i was observed only compared with sulfonylureas ( Figure ). Conclusion: In this claims database study, an increased risk of DKA was observed for new users of SGLT2i versus new users of several non-SGLT2i AHAs when T2DM was defined broadly. When T2DM was defined narrowly to exclude possible misdiagnosed T1DM patients, an increased risk of DKA with SGLT2i was observed compared to sulfonylureas.

Sodium glucose co-transporter 2 inhibitors (SGLT2i) are indicated for treatment of T2DM; some SGLT2i have reported a CV benefit and some reported a risk of BKA. U.S. claims databases were analyzed using a prespecified protocol to examine CANA-associated effects on BKA and hospitalization for heart failure (HHF) vs. other SGLT2i and non-SGLT2i. Analyses used a propensity score adjusted new user design with numerous sensitivity analyses. The 4 databases included 142K new users of CANA, 110K of other SGLT2i, and 460K of non-SGLT2i AHAs. Meta-analysis results are reported when heterogeneity across databases was not substantial (I2 Disclosure P. Ryan: Employee; Self; Janssen Research & Development, LLC. J.B. Buse: Other Relationship; Self; ADOCIA, AstraZeneca, Dexcom, Inc., Elcelyx Therapeutics, Inc., Eli Lilly and Company, Fractyl Laboratories, Inc., Intarcia Therapeutics, Inc., Lexicon Pharmaceuticals, Inc., Metavention, NovaTarg, Novo Nordisk A/S, Sanofi, VTV Therapeutics. Research Support; Self; Boehringer Ingelheim GmbH, Johnson & Johnson Services, Inc., Theracos, Inc.. Other Relationship; Self; Shenzhen Hightide Biopharmaceutical, Ltd.. Research Support; Self; National Heart, Lung, and Blood Institute, National Center for Advancing Translational Sciences. Other Relationship; Self; National Institute of Diabetes and Digestive and Kidney Diseases, American Diabetes Association. Research Support; Self; Patient-Centered Outcomes Research Institute. Other Relationship; Self; National Institute of Environmental Health Sciences. M. Schuemie: Employee; Self; Janssen Research & Development, LLC. F. Defalco: Employee; Self; Janssen Research & Development, LLC. Z. Yuan: Employee; Self; Janssen Research & Development, LLC. P. Stang: Employee; Self; Janssen Research & Development, LLC. J.A. Berlin: Employee; Self; Johnson & Johnson, LLC. N. Rosenthal: Employee; Self; Janssen Research & Development.

Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results. Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour. Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases. Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.

Aims To estimate and compare incidence of diabetes ketoacidosis (DKA) among patients with type 2 diabetes who are newly treated with SGLT2 inhibitors (SGLT2i) versus non-SGLT2i antihyperglycemic agents (AHAs) in actual clinical practice. Methods A new-user cohort study design using a large insurance claims database in the US. DKA incidence was compared between new users of SGLT2i and new users of non-SGLT2i AHAs pair-matched on exposure propensity scores (EPS) using Cox regression models. Results Overall, crude incidence rates (95% CI) per 1000 patient-years for DKA were 1.69 (1.22–2.30) and 1.83 (1.58–2.10) among new users of SGLT2i ( n = 34,442) and non-SGLT2i AHAs ( n = 126,703). These rates more than doubled among patients with prior insulin prescriptions but decreased by more than half in analyses that excluded potential autoimmune diabetes (PAD). The hazard ratio (95% CI) for DKA comparing new users of SGLT2i to new users of non-SGLT2i AHAs was 1.91 (0.94–4.11) ( p = 0.09) among the 30,196 EPS-matched pairs overall, and 1.13 (0.43–3.00) ( p = 0.81) among the 27,515 EPS-matched pairs that excluded PAD. Conclusions This was the first observational study that compared DKA risk between new users of SGLT2i and non-SGLT2i AHAs among patients with type 2 diabetes, and overall no statistically significant difference was detected.

Introduction: Light chain (AL) amyloidosis is a rare clonal plasma cell disorder characterized by the organ deposition of amyloid derived from misfolded immunoglobulin light chains. The involvement of vital organs, especially the heart, results in severe morbidity and high early mortality. Diagnostic delays of greater than 1 year from clinical evidence of AL have been reported in surveys and retrospective single-center studies. Earlier diagnosis prior to developing advanced organ dysfunction is a key unmet need in AL. To detect patients earlier, a better understanding of the patterns of AL-related signs/symptoms (AL-S/Sx) and healthcare utilization preceding AL amyloidosis diagnosis is necessary. Methods: Using the Optum De-Identified Clinformatics® Data Mart healthcare claims database (Optum), we identified US commercially-insured patients who had one inpatient code or two outpatient codes of ICD-9-CM 277.30 or ICD-10-CM E85.81, E85.89, or E85.9 and treatment with one multiple myeloma drug within 90 days of diagnosis (01Jan06-31Dec18). Twenty-three AL-S/Sx were defined with sets of diagnosis codes, flagged in prior condition lists, and grouped by system affected (cardiac, nervous, renal/ureter, gastrointestinal, or other). The time between the first occurrence of each symptom and AL diagnosis was summarized and healthcare utilization patterns were described to better characterize the patient journey to diagnosis. Results: Among 1313 AL patients, the median time from the first potential AL-S/Sx to AL diagnosis was 2.5 years. The most common AL-S/Sx were malaise/fatigue (53%), edema and swelling (57%), dyspnea (53%), and abdominal pain (52%), which occurred a median of 1-1.8 years prior to diagnosis. Arrhythmia (51%), cardiomegaly (27%), and cardiomyopathy (21%) were identifiable between a median of 1-1.5 years before diagnosis. Chronic kidney disease (45%) and edema (57%) were seen a median of 2.3 years prior to diagnosis. Patients with a first symptom of nephrotic syndrome presented less than 1 year before diagnosis. Peripheral nerve disease (46%), neuralgias (14%), and paresthesia/anesthesia (20%) appeared a median of 2.8 years prior to diagnosis. The cumulative incidence of each AL-S/Sx by organ system in the 5 years prior to diagnosis is shown in Figure 1. Sequencing of symptoms varied greatly among patients. The most common first symptoms in the sequences were abdominal pain, malaise/fatigue/weakness, peripheral nerve disease, and arrhythmia. Between initial AL-S/Sx and diagnosis, 20% of patients had 3 or more ER visits and 16% had 3 or more hospitalizations. More than 90% of patients visited 5 or more provider types between the first AL-S/Sx and AL diagnosis, with the most frequent specialties visited being cardiology (67%), hematology/oncology (43%), and gastroenterology (40%). About one quarter of patients had a monoclonal gammopathy detected at a median of 1 year prior to diagnosis; detailed analyses of this cohort will be presented at the meeting. Conclusion: AL patients have a prolonged prodrome, including evidence of cardiac and renal dysfunction, with 50% of patients with a first AL-S/Sx observed more than 2.5 years before AL diagnosis. Only one-quarter of patients had a monoclonal protein detected before diagnosis, indicating a low level of suspicion for AL. Limitations of this study include underestimation of AL S/Sx if diagnosis codes are underutilized in claims. Future studies will use controls to understand background rates of symptoms. Overall, these findings suggest that novel approaches to early diagnosis have significant potential to improve outcomes in AL. Figure 1. Cumulative incidence of the first occurrence of potential symptoms prior to the first light chain (AL) amyloidosis diagnosis Figure 1 Disclosures Hester: Janssen R&D, LLC: Employment, Equity Ownership. Gifkins:Janssen R&D, LLC: Employment. Bellew:Janssen R&D, LLC: Employment. Vermeulen:Janssen R&D, LLC: Employment, Equity Ownership. Schecter:Janssen R&D, LLC: Employment, Equity Ownership. DeFalco:Janssen R&D, LLC: Employment, Equity Ownership. Weiss:Janssen R&D, LLC: Employment.

Background Stroke mainly occurs in patients without atrial fibrillation (AF). This study explored risk prediction models for ischemic stroke and transient ischemic attack (TIA) in patients without AF. Methods Three US-based healthcare databases (Truven MarketScan Commercial Claims and Encounters [CCAE], Medicare Supplemental [MDCR], and Optum Clinformatics [Optum]) were used to establish patient cohorts without AF during the index period of 2008-2012. The performance of 2 existing models (CHADS 2 and CHA 2 DS 2 -VASc) for predicting stroke and TIA was examined by fitting a logistic regression to a training dataset and evaluating predictive accuracy in a validation dataset (area under the curve, AUC) using patients with complete follow-up of 1 or 3 years, separately. Results The commercial populations were younger and had fewer comorbidities than Medicare-eligible population. The incidence proportions of ischemic stroke and TIA during 1 and 3 years of follow-up were .5% and 1.9% (CCAE), .6% and 2.2% (Optum), and 4.6% and 13.1% (MDCR), respectively. The models performed consistently across all 3 databases, with the AUC ranging from .69 to .77 and from .68 to .73 for 1- and 3-year prediction, respectively. Predictive accuracy was lower than the initial work of CHADS 2 evaluation in patients with AF (AUC: .82), but consistent with a subsequent meta-analysis of CHADS 2 (.60-.80) and CHA 2 DS 2 -VASc performance (.64-.79). Conclusion Although the existing schemes for predicting ischemic stroke and TIA in patients with AF can be applied to patients without AF with comparable predictive accuracy, the evidence suggests that there is room for improvement in these models' performance.

Introduction: Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community.Methods: We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations.Results: We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation.Discussion: Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.

Databases of medical records contain a wealth of information critical to many areas of research including drug safety, health outcomes, clinical epidemiology and translational medicine. Through commercially available databases, researchers can gain a better understanding of the impact of exposure to drugs and medical devices, identify populations at risk for adverse effects, estimate the prevalence and natural history of medical conditions, and assess drug utilization across different demographic groups. However, the daunting size and complexity of these databases as well as lack of convenient tools to mine them have made this information largely inaccessible to all but a few experts with advanced data management and statistical programming skills. Using a combination of a relational data management strategy and a graphical user front-end, we have developed an approach that allows any medical researcher to perform a number of common searches and analyses in a consistent, intuitive and interactive manner, without the assistance of an expert programmer. Moreover, the optimization work done on the database and application sides have dramatically reduced the time needed to analyze the data and, thus, increased the number of studies that can be performed. A crucial part of any such study is the selection of code lists for diseases, procedures, medications, etc., and we have supported this effort by allowing definitions to be queried using common ontologies and shared conveniently across the organization.

In addition to methods that can identify common variants associated with susceptibility to common diseases, there has been increasing interest in approaches that can identify rare genetic variants. We use the simulated data provided to the participants of Genetic Analysis Workshop 17 (GAW17) to identify both rare and common single-nucleotide polymorphisms and pathways associated with disease status. We apply a rare variant collapsing approach and the usual association tests for common variants to identify candidates for further analysis using pathway-based and tree-based ensemble approaches. We use the mean log p-value approach to identify a top set of pathways and compare it to those used in simulation of GAW17 dataset. We conclude that the mean log p-value approach is able to identify those pathways in the top list and also related pathways. We also use the stochastic gradient boosting approach for the selected subset of single-nucleotide polymorphisms. When compared the result of this tree-based method with the list of single-nucleotide polymorphisms used in dataset simulation, in addition to correct SNPs we observe number of false positives.