Publications by authors named "Anna Ostropolets"

18 Publications

  • Page 1 of 1

Phenotype Concept Set Construction from Concept Pair Likelihoods.

AMIA Annu Symp Proc 2020 25;2020:1080-1089. Epub 2021 Jan 25.

Columbia University, New York, NY.

Phenotyping algorithms are essential tools for conducting clinical research on observational data. Manually devel- oped phenotyping algorithms, such as those curated within the eMERGE (electronic Medical Records and Genomics) Network, represent the gold standard but are time consuming to create. In this work, we propose a framework for learning from the structure of eMERGE phenotype concept sets to assist construction of novel phenotype definitions. We use eMERGE phenotypes as a source of reference concept sets and engineer rich features characterizing the con- cept pairs within each set. We treat these pairwise relationships as edges in a concept graph, train models to perform edge prediction, and identify candidate phenotype concept sets as highly connected subgraphs. Candidate concept sets may then be interrogated and composed to construct novel phenotype definitions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075469PMC
January 2021

Characterizing database granularity using SNOMED-CT hierarchy.

AMIA Annu Symp Proc 2020 25;2020:983-992. Epub 2021 Jan 25.

Columbia University, New York, NY, USA.

Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075504PMC
January 2021

Characterizing the incidence of adverse events of special interest for COVID-19 vaccines across eight countries: a multinational network cohort study.

medRxiv 2021 Mar 28. Epub 2021 Mar 28.

Background: As large-scale immunization programs against COVID-19 proceed around the world, safety signals will emerge that need rapid evaluation. We report population-based, age- and sex- specific background incidence rates of potential adverse events of special interest (AESI) in eight countries using thirteen databases.

Methods: This multi-national network cohort study included eight electronic medical record and five administrative claims databases from Australia, France, Germany, Japan, Netherlands, Spain, the United Kingdom, and the United States, mapped to a common data model. People observed for at least 365 days before 1 January 2017, 2018, or 2019 were included. We based study outcomes on lists published by regulators: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain-Barre syndrome, hemorrhagic and non-hemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, and transverse myelitis. We calculated incidence rates stratified by age, sex, and database. We pooled rates across databases using random effects meta-analyses. We classified meta-analytic estimates into Council of International Organizations of Medical Sciences categories: very common, common, uncommon, rare, or very rare.

Findings: We analysed 126,661,070 people. Rates varied greatly between databases and by age and sex. Some AESI (e.g., myocardial infarction, Guillain-Barre syndrome) increased with age, while others (e.g., anaphylaxis, appendicitis) were more common in young people. As a result, AESI were classified differently according to age. For example, myocardial infarction was very rare in children, rare in women aged 35-54 years, uncommon in men and women aged 55-84 years, and common in those aged ≥85 years.

Interpretation: We report robust baseline rates of prioritised AESI across 13 databases. Age, sex, and variation between databases should be considered if background AESI rates are compared to event rates observed with COVID-19 vaccines.
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http://dx.doi.org/10.1101/2021.03.25.21254315DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010764PMC
March 2021

COVID-19 in patients with autoimmune diseases: characteristics and outcomes in a multinational network of cohorts across three countries.

Rheumatology (Oxford) 2021 Mar 16. Epub 2021 Mar 16.

Real-World Evidence, Trial, Barcelona, Spain, Form Support.

Objective: 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.

Methods: A multinational network cohort study was conducted using electronic health records data from Columbia University Irving Medical Center (CUIMC) (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. Outcomes were death and complications within 30 days of hospitalisation.

Results: We studied 133 589 patients diagnosed and 48 418 hospitalised with COVID-19 with prevalent autoimmune diseases. Most patients were female, aged ≥50 years with previous comorbidities. The prevalence of hypertension (45.5-93.2%), chronic kidney disease (14.0-52.7%) and heart disease (29.0-83.8%) was higher in hospitalised vs diagnosed patients with COVID-19. Compared with 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% vs 6.3% to 24.6%).

Conclusions: Compared with influenza, COVID-19 is a more severe disease, leading to more complications and higher mortality.
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http://dx.doi.org/10.1093/rheumatology/keab250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7989171PMC
March 2021

Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS.

Res Sq 2021 Mar 1. Epub 2021 Mar 1.

Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. We conducted a descriptive cohort 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 11 June 2020 and are iteratively updated via GitHub [4]. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical 886,193 , and 113,627 . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
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http://dx.doi.org/10.21203/rs.3.rs-279400/v1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941629PMC
March 2021

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

JMIR Med Inform 2021 Apr 5;9(4):e21547. Epub 2021 Apr 5.

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.

Background: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated.

Objective: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases.

Methods: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia.

Results: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68.

Conclusions: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.
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http://dx.doi.org/10.2196/21547DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023380PMC
April 2021

Incorporation of Korean Electronic Data Interchange Vocabulary into Observational Medical Outcomes Partnership Vocabulary.

Healthc Inform Res 2021 Jan 31;27(1):29-38. Epub 2021 Jan 31.

Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea.

Objectives: We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea.

Methods: We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies.

Results: We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary.

Conclusions: The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.
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http://dx.doi.org/10.4258/hir.2021.27.1.29DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7921574PMC
January 2021

Metformin Is Associated With a Lower Risk of Atrial Fibrillation and Ventricular Arrhythmias Compared With Sulfonylureas: An Observational Study.

Circ Arrhythm Electrophysiol 2021 Mar 7;14(3):e009115. Epub 2021 Feb 7.

Department of Medicine (P.A.E., M.V.R., E.Y.W., U.B.P., G.H., J.P.M.), College of Physicians and Surgeons of Columbia University, NY.

[Figure: see text].
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http://dx.doi.org/10.1161/CIRCEP.120.009115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969445PMC
March 2021

Metformin Is Associated With a Lower Risk of Atrial Fibrillation and Ventricular Arrhythmias Compared With Sulfonylureas: An Observational Study.

Circ Arrhythm Electrophysiol 2021 Mar 7;14(3):e009115. Epub 2021 Feb 7.

Department of Medicine (P.A.E., M.V.R., E.Y.W., U.B.P., G.H., J.P.M.), College of Physicians and Surgeons of Columbia University, NY.

[Figure: see text].
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http://dx.doi.org/10.1161/CIRCEP.120.009115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969445PMC
March 2021

Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis.

medRxiv 2020 Nov 27. Epub 2020 Nov 27.

Objective: 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.

Design: Multinational network cohort study.

Setting: 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).

Participants: 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.

Main Outcome Measures: 30-day complications during hospitalisation and death.

Results: 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%).

Conclusions: 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.

What Is Already Known About This Topic: 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.

What This Study Adds: 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.
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http://dx.doi.org/10.1101/2020.11.24.20236802DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7709171PMC
November 2020

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials.

J Am Med Inform Assoc 2021 01;28(1):14-22

Department of Biomedical Informatics, Columbia University, New York, New York, USA.

Objective: This research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.

Materials And Methods: On June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020-June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death.

Results: There were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4-28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event.

Discussion: By adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients.

Conclusions: This research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.
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http://dx.doi.org/10.1093/jamia/ocaa276DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7798960PMC
January 2021

Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States.

medRxiv 2020 Oct 27. Epub 2020 Oct 27.

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.
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http://dx.doi.org/10.1101/2020.10.25.20218875DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7605581PMC
October 2020

A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time.

J Am Med Inform Assoc 2020 12;27(12):1968-1976

Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.

Objective: A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time.

Materials And Methods: PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles' references. The details of design, implementation and evaluation of included CDSSs were extracted.

Results: Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing.

Conclusions: We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.
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http://dx.doi.org/10.1093/jamia/ocaa200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7824048PMC
December 2020

Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.

Nat Commun 2020 10 6;11(1):5009. Epub 2020 Oct 6.

Clinical Pharmacology Unit, Zealand University Hospital, Køge, Denmark.

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.
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http://dx.doi.org/10.1038/s41467-020-18849-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7538555PMC
October 2020

Risk of hydroxychloroquine alone and in combination with azithromycin in the treatment of rheumatoid arthritis: a multinational, retrospective study.

Lancet Rheumatol 2020 Nov 21;2(11):e698-e711. Epub 2020 Aug 21.

Janssen Research and Development, Titusville, NJ, USA.

Background: Hydroxychloroquine, a drug commonly used in the treatment of rheumatoid arthritis, has received much negative publicity for adverse events associated with its authorisation for emergency use to treat patients with COVID-19 pneumonia. We studied the safety of hydroxychloroquine, alone and in combination with azithromycin, to determine the risk associated with its use in routine care in patients with rheumatoid arthritis.

Methods: In this multinational, retrospective study, new user cohort studies in patients with rheumatoid arthritis aged 18 years or older and initiating hydroxychloroquine were compared with those initiating sulfasalazine and followed up over 30 days, with 16 severe adverse events studied. Self-controlled case series were done to further establish safety in wider populations, and included all users of hydroxychloroquine regardless of rheumatoid arthritis status or indication. Separately, severe adverse events associated with hydroxychloroquine plus azithromycin (compared with hydroxychloroquine plus amoxicillin) were studied. Data comprised 14 sources of claims data or electronic medical records from Germany, Japan, the Netherlands, Spain, the UK, and the USA. Propensity score stratification and calibration using negative control outcomes were used to address confounding. Cox models were fitted to estimate calibrated hazard ratios (HRs) according to drug use. Estimates were pooled where the value was less than 0·4.

Findings: The study included 956 374 users of hydroxychloroquine, 310 350 users of sulfasalazine, 323 122 users of hydroxychloroquine plus azithromycin, and 351 956 users of hydroxychloroquine plus amoxicillin. No excess risk of severe adverse events was identified when 30-day hydroxychloroquine and sulfasalazine use were compared. Self-controlled case series confirmed these findings. However, long-term use of hydroxychloroquine appeared to be associated with increased cardiovascular mortality (calibrated HR 1·65 [95% CI 1·12-2·44]). Addition of azithromycin appeared to be associated with an increased risk of 30-day cardiovascular mortality (calibrated HR 2·19 [95% CI 1·22-3·95]), chest pain or angina (1·15 [1·05-1·26]), and heart failure (1·22 [1·02-1·45]).

Interpretation: Hydroxychloroquine treatment appears to have no increased risk in the short term among patients with rheumatoid arthritis, but in the long term it appears to be associated with excess cardiovascular mortality. The addition of azithromycin increases the risk of heart failure and cardiovascular mortality even in the short term. We call for careful consideration of the benefit-risk trade-off when counselling those on hydroxychloroquine treatment.

Funding: National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, NIHR Senior Research Fellowship programme, US National Institutes of Health, US Department of Veterans Affairs, Janssen Research and Development, IQVIA, Korea Health Industry Development Institute through the Ministry of Health and Welfare Republic of Korea, Versus Arthritis, UK Medical Research Council Doctoral Training Partnership, Foundation Alfonso Martin Escudero, Innovation Fund Denmark, Novo Nordisk Foundation, Singapore Ministry of Health's National Medical Research Council Open Fund Large Collaborative Grant, VINCI, Innovative Medicines Initiative 2 Joint Undertaking, EU's Horizon 2020 research and innovation programme, and European Federation of Pharmaceutical Industries and Associations.
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http://dx.doi.org/10.1016/S2665-9913(20)30276-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442425PMC
November 2020

Characterizing physicians' information needs related to a gap in knowledge unmet by current evidence.

JAMIA Open 2020 Jul 7;3(2):281-289. Epub 2020 May 7.

Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA.

Objective: The study sought to explore information needs arising from a gap in clinicians' knowledge that is not met by current evidence and identify possible areas of use and target groups for a future clinical decision support system (CDSS), which will guide clinicians in cases where no evidence exists.

Materials And Methods: We interviewed 30 physicians in a large academic medical center, analyzed transcripts using deductive thematic analysis, and developed a set of themes of information needs related to a gap in knowledge unmet by current evidence. We conducted additional statistical analyses to identify the correlation between clinical experience, clinical specialty, settings of clinical care, and the characteristics of the needs.

Results: This study resulted in a set of themes and subthemes of information needs arising from a gap in current evidence. Experienced physicians and inpatient physicians had more questions and the number of questions did not decline with clinical experience. The main areas of information needs included patients with comorbidities, elderly and children, new drugs, and rare disorders. To address these questions, clinicians most often used a commercial tool, guidelines, and PubMed. While primary care physicians preferred the commercial tool, specialty physicians sought more in-depth knowledge.

Discussion: The current medical evidence appeared to be inadequate in covering specific populations such as patients with multiple comorbidities and elderly, and was sometimes irrelevant to complex clinical scenarios. Our findings may suggest that experienced and inpatient physicians would benefit from a CDSS that generates evidence in real time at the point of care.

Conclusions: We found that physicians had information needs, which arose from the gaps in current medical evidence. This study provides insights on how the CDSS that aims at addressing these needs should be designed.
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http://dx.doi.org/10.1093/jamiaopen/ooaa012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7382620PMC
July 2020

An international characterisation of patients hospitalised with COVID-19 and a comparison with those previously hospitalised with influenza.

medRxiv 2020 Apr 25. Epub 2020 Apr 25.

Science Policy and Research, National Institute for Health and Care Excellence, UK.

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.
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http://dx.doi.org/10.1101/2020.04.22.20074336DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239064PMC
April 2020

Adapting electronic health records-derived phenotypes to claims data: Lessons learned in using limited clinical data for phenotyping.

J Biomed Inform 2020 02 19;102:103363. Epub 2019 Dec 19.

Columbia University Medical Center, New York, NY, USA; Observational Health Data Sciences and Informatics (OHDSI), New York, NY, USA. Electronic address:

Algorithms for identifying patients of interest from observational data must address missing and inaccurate data and are desired to achieve comparable performance on both administrative claims and electronic health records data. However, administrative claims data do not contain the necessary information to develop accurate algorithms for disorders that require laboratory results, and this omission can result in insensitive diagnostic code-based algorithms. In this paper, we tested our assertion that the performance of a diagnosis code-based algorithm for chronic kidney disorder (CKD) can be improved by adding other codes indirectly related to CKD (e.g., codes for dialysis, kidney transplant, suspicious kidney disorders). Following the best practices from Observational Health Data Sciences and Informatics (OHDSI), we adapted an electronic health record-based gold standard algorithm for CKD and then created algorithms that can be executed on administrative claims data and account for related data quality issues. We externally validated our algorithms on four electronic health record datasets in the OHDSI network. Compared to the algorithm that uses CKD diagnostic codes only, positive predictive value of the algorithms that use additional codes was slightly increased (47.4% vs. 47.9-48.5% respectively). The algorithms adapted from the gold standard algorithm can be used to infer chronic kidney disorder based on administrative claims data. We succeeded in improving the generalizability and consistency of the CKD phenotypes by using data and vocabulary standardized across the OHDSI network, although performance variability across datasets remains. We showed that identifying and addressing coding and data heterogeneity can improve the performance of the algorithms.
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http://dx.doi.org/10.1016/j.jbi.2019.103363DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390483PMC
February 2020