Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study.

Background: SGLT2 inhibitors (SGLT2is) and GLP-1 receptor agonists (GLP1-RAs) reduce major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head trials. Methods: Across the LEGEND-T2DM network, we included ten federated international data sources, spanning 1992-2021. We identified 1,492,855 patients with T2DM and established cardiovascular disease (CVD) on metformin monotherapy who initiated one of four second-line agents (SGLT2is, GLP1-RAs, dipeptidyl peptidase 4 inhibitor [DPP4is], sulfonylureas [SUs]). We used large-scale propensity score models to conduct an active comparator, target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, we fit on-treatment Cox proportional hazard models for 3-point MACE (myocardial infarction, stroke, death) and 4-point MACE (3-point MACE + heart failure hospitalization) risk, and combined hazard ratio (HR) estimates in a random-effects meta-analysis. Findings: Across cohorts, 16·4%, 8·3%, 27·7%, and 47·6% of individuals with T2DM initiated SGLT2is, GLP1-RAs, DPP4is, and SUs, respectively. Over 5·2 million patient-years of follow-up and 489 million patient-days of time at-risk, there were 25,982 3-point MACE and 41,447 4-point MACE events. SGLT2is and GLP1-RAs were associated with a lower risk for 3-point MACE compared with DPP4is (HR 0·89 [95% CI, 0·79-1·00] and 0·83 [0·70-0·98]), and SUs (HR 0·76 [0·65-0·89] and 0·71 [0·59-0·86]). DPP4is were associated with a lower 3-point MACE risk versus SUs (HR 0·87 [0·79-0·95]). The pattern was consistent for 4-point MACE for the comparisons above. There were no significant differences between SGLT2is and GLP1-RAs for 3-point or 4-point MACE (HR 1·06 [0·96-1·17] and 1·05 [0·97-1·13]). Interpretation: In patients with T2DM and established CVD, we found comparable cardiovascular risk reduction with SGLT2is and GLP1-RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of GLP1-RAs and SGLT2is should be prioritized as second-line agents in those with established CVD. Funding: National Institutes of Health, United States Department of Veterans Affairs.

Bridging information gaps in menopause status classification through natural language processing.

Objective: To use natural language processing (NLP) of clinical notes to augment existing structured electronic health record (EHR) data for classification of a patient's menopausal status. Materials and methods: A rule-based NLP system was designed to capture evidence of a patient's menopause status including dates of a patient's last menstrual period, reproductive surgeries, and postmenopause diagnosis as well as their use of birth control and menstrual interruptions. NLP-derived output was used in combination with structured EHR data to classify a patient's menopausal status. NLP processing and patient classification were performed on a cohort of 307 512 female Veterans receiving healthcare at the US Department of Veterans Affairs (VA). Results: NLP was validated at 99.6% precision. Including the NLP-derived data into a menopause phenotype increased the number of patients with data relevant to their menopausal status by 118%. Using structured codes alone, 81 173 (27.0%) are able to be classified as postmenopausal or premenopausal. However, with the inclusion of NLP, this number increased 167 804 (54.6%) patients. The premenopausal category grew by 532.7% with the inclusion of NLP data. Discussion: By employing NLP, it became possible to identify documented data elements that predate VA care, originate outside VA networks, or have no corresponding structured field in the VA EHR that would be otherwise inaccessible for further analysis. Conclusion: NLP can be used to identify concepts relevant to a patient's menopausal status in clinical notes. Adding NLP-derived data to an algorithm classifying a patient's menopausal status significantly increases the number of patients classified using EHR data, ultimately enabling more detailed assessments of the impact of menopause on health outcomes.

Genetic risk and likelihood of prostate cancer detection on first biopsy by ancestry.

Despite differences in prostate cancer risk across ancestry groups, relative performance of prostate cancer genetic risks scores (GRS) for positive biopsy prediction in different ancestry groups is unknown. This cross-sectional retrospective analysis examines the association between a polygenic hazard score (PHS290) and risk of prostate cancer diagnosis upon first biopsy in male veterans using 2-sided tests. Our analysis included 36 717 veterans (10 297 of African ancestry). Unadjusted rates of positive first prostate biopsy increased with higher genetic risk (low risk: 34%, high risk: 58%; P < .001). Among men of African ancestry, higher genetic risk was associated with increased prostate cancer detection on first biopsy (odds ratio = 2.18, 95% confidence interval = 1.93 to 2.47), but the effect was stronger among men of European descent (odds ratio = 3.89, 95% confidence interval = 3.62 to 4.18). These findings suggest that incorporating genetic risk into prediction models could better personalize biopsy decisions, although further study is needed to achieve equitable genetic risk stratification among ancestry groups.

An NLP Framework for the Extraction of Concept Measurements from Radiology and Pathology Notes.

Natural language processing (NLP) tools can automate the identification of cancer patients eligible for specific pathways. We developed and validated a cancer agnostic, rules-based NLP framework to extract the dimensions and measurements of several concepts from pathology and radiology reports. This framework was then efficiently and cost-effectively deployed to identify patients eligible for breast, lung, and prostate cancers clinical pathways.

Toward Real-World Reproducibility: Verifying Value Sets for Clinical Research.

Standardized operational definitions are an important tool to improve reproducibility of research using secondary real-world healthcare data. This approach was leveraged for studies evaluating the effectiveness of AZD7442 as COVID-19 pre-exposure prophylaxis across multiple healthcare systems. Value sets were defined, grouped, and mapped. Results of this exercise were reviewed and recorded. Value sets were updated to reflect findings.

Development of a Natural Language Processing System to Identify Clinical Documentation of Electronic Cigarette Use.

Electronic Nicotine Delivery Systems (ENDS) use has increased substantially in the United States since 2010. To date, there is limited evidence regarding the nature and extent of ENDS documentation in the clinical note. In this work we investigate the effectiveness of different approaches to identify a patient's documented ENDS use. We report on the development and validation of a natural language processing system to identify patients with explicit documentation of ENDS using a large national cohort of patients at the United States Department of Veterans Affairs.

Association between prediagnostic prostate-specific antigen and prostate cancer probability in Black and non-Hispanic White men.

BACKGROUND: Although Black men are more likely than non-Hispanic White men to develop and die from prostate cancer, limited data exist to guide prostate-specific antigen (PSA) screening protocols in Black men. This study investigated whether the risk for prostate cancer was higher than expected among self-identified Black than White veterans based on prebiopsy PSA level. METHODS: Multivariable logistic regression models were estimated to predict the likelihood of prostate cancer diagnosis on first biopsy for 75,295 Black and 207,658 White male veterans. Self-identified race, age at first PSA test, prebiopsy PSA, age at first biopsy, smoking status, statin use, and socioeconomic factors were used as predictors. The adjusted predicted probabilities of cancer detection on first prostate biopsy from the logistic models at different PSA levels were calculated. RESULTS: After controlling for PSA and other covariates, Black veterans were 50% more likely to receive a prostate cancer diagnosis on their first prostate biopsy than White veterans (odds ratio [OR], 1.50; 95% CI, 1.47-1.53; p < .001). At a PSA level of 4.0 ng/mL, the probability of prostate cancer for a Black man was 49% compared with 39% for a White man. This model indicated that Black veterans with a PSA of 4.0 ng/mL have an equivalent risk of prostate cancer as White veterans with a PSA of 13.4 ng/mL. CONCLUSIONS: The findings indicate that, at any given PSA level, Black men are more likely to harbor prostate cancer than White men. Prospective studies are needed to better evaluate risks and benefits of PSA screening in Black men and other high-risk populations.

Pharmacogenetic testing and monitoring of complete blood counts among Veterans newly prescribed thiopurine treatments: a retrospective cohort study.

Pharmacogenetic (PGx) testing before initiation of thiopurine treatment and CBC monitoring post-initiation helps avoid adverse events and ensure patient safety. This study aims to evaluate trends in PGx testing and CBC monitoring among Veterans prescribed azathioprine, thioguanine, or mercaptopurine to demonstrate VA's efforts to improve medication safety after an adverse event. To assess testing patterns, we used VA electronic health report data to identify 20,524 Veterans who first began thiopurine treatment between January 1, 2010, to December 31, 2021. Aggregate monthly counts of thiopurine prescriptions and associated lab tests were tabulated, and the trend in the proportion of patients tested was analyzed using the Mann-Kendall test. The proportion of patients undergoing PGx testing rose from 30.0% in 2010 to 47.5% in late 2014 (July-December). However, PGx testing and overall testing only increased slightly after the sentinel event, and orders levelled off over time at slightly lower levels than before the sentinel event. Very little change was seen in the overall proportion of individuals receiving any testing across all patients with new prescriptions from the time of the sentinel event in 2014 to the end of 2021. A large portion of patients prescribed thiopurine drugs did not receive testing that could help prevent the development of potential adverse events, leading to a predominantly reactive approach. Increased PGx testing may result in a more proactive approach to the prevention of adverse events due to genetic interaction.

CYP2C19 Polymorphisms and Clinical Outcomes Following Percutaneous Coronary Intervention (PCI) in the Million Veterans Program.

Background: CYP2C19 loss-of-function (LOF) alleles decrease the antiplatelet effect of clopidogrel following percutaneous coronary intervention (PCI) in patients presenting with acute coronary syndrome (ACS). The impact of genotype in stable ischemic heart disease (SIHD) is unclear. Objectives: Determine the association of CYP2C19 genotype with major adverse cardiac events (MACE) after PCI for ACS or SIHD. Methods: Million Veterans Program (MVP) participants age <65 years with a PCI documented in the VA Clinical Assessment, Reporting and Tracking (CART) Program between 1/1/2009 to 9/30/2017, treated with clopidogrel were included. Time to MACE defined as the composite of all-cause death, stroke or myocardial infarction within 12 months following PCI. Results: Among 4,461 Veterans (mean age 59.1 ± 5.1 years, 18% Black); 44% had ACS, 56% had SIHD and 29% carried a CYP2C19 LOF allele. 301 patients (6.7%) experienced MACE while being treated with clopidogrel, 155 (7.9%) in the ACS group and 146 (5.9%) in the SIHD group. Overall, MACE was not significantly different between LOF carriers vs. noncarriers (adjusted hazard ratio [HR] 1.18, confidence interval [95%CI] 0.97-1.45, p=0.096). Among patients presenting with ACS, MACE risk in LOF carriers versus non-carriers was numerically higher (HR 1.30, 95%CI 0.98-1.73, p=0.067). There was no difference in MACE risk in patients with SIHD (HR 1.09, 95%CI 0.82-1.44; p=0.565). Conclusions: CYP2C19 LOF carriers presenting with ACS treated with clopidogrel following PCI experienced a numerically greater elevated risk of MACE events. CYP2C19 LOF genotype is not associated with MACE among patients presenting with SIHD.

Development and Validation of a Tool to Identify Patients Diagnosed With Castration-Resistant Prostate Cancer.

PURPOSE: Several novel therapies for castration-resistant prostate cancer (CRPC) have been approved with randomized phase III studies with continuing observational research either planned or ongoing. Accurately identifying patients with CRPC in electronic health care data is critical for quality observational research, resource allocation, and quality improvement. Previous work in this area has relied on either structured laboratory results and medication data or natural language processing (NLP) methods. However, a computable phenotype using both structured data and NLP identifies these patients with more accuracy. METHODS: The Corporate Data Warehouse (CDW) of the Veterans Health Administration (VHA) was used to collect PCa diagnoses, prostate-specific antigen test results, and information regarding patient characteristics and medication use. The final system used for validation and subsequent analysis combined the NLP system and an algorithm of structured laboratory and medication data to identify patients as being diagnosed with CRPC. Patients with both a documented diagnosis of CRPC and a documented diagnosis of metastatic PCa were classified as having mCRPC by this system. RESULTS: Among 1.2 million veterans with PCa, the International Classification of Diseases (ICD)-10 diagnosis code for CRPC (Z19.2) identifies 3,791 patients from 2016 when the code was created until 2022, compared with the combined algorithm which identifies 14,103, 10,312 more than ICD-10 codes alone, from 2016 to 2022. The combined algorithm showed a sensitivity of 97.9% and a specificity of 99.2%. CONCLUSION: ICD-10 codes proved to be insufficient for capturing CRPC in the VHA CDW data. Using both structured and unstructured data identified more than double the number of patients compared with ICD-10 codes alone. Application of this combined approach drastically improved identification of real-world patients and enables high-quality observational research in mCRPC.

Multinational patterns of second line antihyperglycaemic drug initiation across cardiovascular risk groups: federated pharmacoepidemiological evaluation in LEGEND-T2DM.

Objective: To assess the uptake of second line antihyperglycaemic drugs among patients with type 2 diabetes mellitus who are receiving metformin. Design: Federated pharmacoepidemiological evaluation in LEGEND-T2DM. Setting: 10 US and seven non-US electronic health record and administrative claims databases in the Observational Health Data Sciences and Informatics network in eight countries from 2011 to the end of 2021. Participants: 4.8 million patients (≥18 years) across US and non-US based databases with type 2 diabetes mellitus who had received metformin monotherapy and had initiated second line treatments. Exposure: The exposure used to evaluate each database was calendar year trends, with the years in the study that were specific to each cohort. Main outcomes measures: The outcome was the incidence of second line antihyperglycaemic drug use (ie, glucagon-like peptide-1 receptor agonists, sodium-glucose cotransporter-2 inhibitors, dipeptidyl peptidase-4 inhibitors, and sulfonylureas) among individuals who were already receiving treatment with metformin. The relative drug class level uptake across cardiovascular risk groups was also evaluated. Results: 4.6 million patients were identified in US databases, 61 382 from Spain, 32 442 from Germany, 25 173 from the UK, 13 270 from France, 5580 from Scotland, 4614 from Hong Kong, and 2322 from Australia. During 2011-21, the combined proportional initiation of the cardioprotective antihyperglycaemic drugs (glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors) increased across all data sources, with the combined initiation of these drugs as second line drugs 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 glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors increased more significantly among populations with no cardiovascular disease compared with patients with established cardiovascular disease. No data source provided evidence of a greater increase in the uptake of these two drug classes in populations with cardiovascular disease compared with no cardiovascular disease. Conclusions: Despite the increase in overall uptake of cardioprotective antihyperglycaemic drugs as second line treatments for type 2 diabetes mellitus, their uptake was lower in patients with cardiovascular disease than in people with no cardiovascular disease over the past decade. A strategy is needed to ensure that medication use is concordant with guideline recommendations to improve outcomes of patients with type 2 diabetes mellitus.

Agent Orange exposure and prostate cancer risk in the Million Veteran Program.

Purpose: Exposure to Agent Orange, a known carcinogen, might increase risk of prostate cancer (PCa). We sought to investigate the association of Agent Orange exposure and PCa risk when accounting for race/ethnicity, family history, and genetic risk in a diverse population of US Vietnam War veterans. Methods & Materials: This study utilized the Million Veteran Program (MVP), a national, population-based cohort study of United States military veterans conducted 2011-2021 with 590,750 male participants available for analysis. Agent Orange exposure was obtained using records from the Department of Veterans Affairs (VA) using the US government definition of Agent Orange exposure: active service in Vietnam while Agent Orange was in use. Only veterans who were on active duty (anywhere in the world) during the Vietnam War were included in this analysis (211,180 participants). Genetic risk was assessed via a previously validated polygenic hazard score calculated from genotype data. Age at diagnosis of any PCa, diagnosis of metastatic PCa, and death from PCa were assessed via Cox proportional hazards models. Results: Exposure to Agent Orange was associated with increased PCa diagnosis (HR 1.04, 95% CI 1.01-1.06, p=0.003), primarily among Non-Hispanic White men (HR 1.09, 95% CI 1.06- 1.12, p<0.001). When accounting for race/ethnicity and family history, Agent Orange exposure remained an independent risk factor for PCa diagnosis (HR 1.06, 95% CI 1.04-1.09, p<0.05). Univariable associations of Agent Orange exposure with PCa metastasis (HR 1.08, 95% CI 0.99-1.17) and PCa death (HR 1.02, 95% CI 0.84-1.22) did not reach significance on multivariable analysis. Similar results were found when accounting for polygenic hazard score. Conclusions: Among US Vietnam War veterans, Agent Orange exposure is an independent risk factor for PCa diagnosis, though associations with PCa metastasis or death are unclear when accounting for race/ethnicity, family history, and/or polygenic risk.

Diversity and Scale: Genetic Architecture of 2,068 Traits in the VA Million Veteran Program.

Genome-wide association studies (GWAS) have underrepresented individuals from non-European populations, impeding progress in characterizing the genetic architecture and consequences of health and disease traits. To address this, we present a population-stratified phenome-wide GWAS followed by a multi-population meta-analysis for 2,068 traits derived from electronic health records of 635,969 participants in the Million Veteran Program (MVP), a longitudinal cohort study of diverse U.S. Veterans genetically similar to the respective African (121,177), Admixed American (59,048), East Asian (6,702), and European (449,042) superpopulations defined by the 1000 Genomes Project. We identified 38,270 independent variants associating with one or more traits at experiment-wide P<4.6×10-11 significance; fine-mapping 6,318 signals identified from 613 traits to single-variant resolution. Among these, a third (2,069) of the associations were found only among participants genetically similar to non-European reference populations, demonstrating the importance of expanding diversity in genetic studies. Our work provides a comprehensive atlas of phenome-wide genetic associations for future studies dissecting the architecture of complex traits in diverse populations.

A deep learning approach for medication disposition and corresponding attributes extraction.

OBJECTIVE: This article summarizes our approach to extracting medication and corresponding attributes from clinical notes, which is the focus of track 1 of the 2022 National Natural Language Processing (NLP) Clinical Challenges(n2c2) shared task. METHODS: The dataset was prepared using Contextualized Medication Event Dataset (CMED), including 500 notes from 296 patients. Our system consisted of three components: medication named entity recognition (NER), event classification (EC), and context classification (CC). These three components were built using transformer models with slightly different architecture and input text engineering. A zero-shot learning solution for CC was also explored. RESULTS: Our best performance systems achieved micro-average F1 scores of 0.973, 0.911, and 0.909 for the NER, EC, and CC, respectively. CONCLUSION: In this study, we implemented a deep learning-based NLP system and demonstrated that our approach of (1) utilizing special tokens helps our model to distinguish multiple medications mentions in the same context; (2) aggregating multiple events of a single medication into multiple labels improves our model's performance.

Tumor testing and treatment patterns in veterans with metastatic castration-resistant prostate cancer.

INTRODUCTION: In 2016, the Department of Veterans Affairs (VA) and Prostate Cancer Foundation (PCF) began a partnership to improve access to testing. The primary objective of this analysis was to describe the use of tumor testing and treatment patterns in Veterans who progressed to metastatic castration-resistant prostate cancer (mCRPC) from 2016 to 2021. Secondary objectives including identifying factors associated with receipt of tumor testing, and reporting HRR mutation results among a subset who were tested. METHODS AND MATERIALS: Natural language processing algorithms were applied to VA electronic health record data to identify a nationwide cohort of veterans with mCRPC. Tumor testing over time and by region were reported, alongside first-, second-, and third-line treatment patterns. Factors associated with receipt of tumor testing were identified using generalized linear mixed models with binomial distributions and logit links to account for clustering by VA facility. RESULTS: Of the 9,852 veterans analyzed, 1,972 (20%) received tumor testing, with 73% of testing occurring in 2020-2021. Factors associated with tumor testing included younger age, later diagnosis year, being treated in the Midwest, or Puerto Rico or other compared to the South, and being treated at a PCF-VA Center of Excellence. Fifteen percent of tests were positive for a pathogenic HRR mutation. Seventy-six percent of the study cohort received first-line treatment, and among those, a subsequent 52% received second-line treatment. A subsequent 46% received third-line treatment. CONCLUSION: After the VA-PCF partnership, one-fifth of veterans with mCRPC received tumor testing, with most tests occurring in 2020-2021.

Actionable Genomic Alterations in Prostate Cancer Among Black and White United States Veterans.

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

Reproducible variability: assessing investigator discordance across 9 research teams attempting to reproduce the same observational study.

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

Characteristics and outcomes of COVID-19 patients with and without asthma from the United States, South Korea, and Europe.

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 30 days 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.[Box: see text]Supplemental data for this article is available online at https://doi.org/10.1080/02770903.2021.2025392 .