Contextualising adverse events of special interest to characterise the baseline incidence rates in 24 million patients with COVID-19 across 26 databases: a multinational retrospective cohort study

BACKGROUND: Adverse events of special interest (AESIs) were pre-specified to be monitored for the COVID-19 vaccines. Some AESIs are not only associated with the vaccines, but with COVID-19. Our aim was to characterise the incidence rates of AESIs following SARS-CoV-2 infection in patients and compare these to historical rates in the general population. METHODS: A multi-national cohort study with data from primary care, electronic health records, and insurance claims mapped to a common data model. This study's evidence was collected between Jan 1, 2017 and the conclusion of each database (which ranged from Jul 2020 to May 2022). The 16 pre-specified prevalent AESIs were: acute myocardial infarction, anaphylaxis, appendicitis, Bell's palsy, deep vein thrombosis, disseminated intravascular coagulation, encephalomyelitis, Guillain- Barré syndrome, haemorrhagic stroke, non-haemorrhagic stroke, immune thrombocytopenia, myocarditis/pericarditis, narcolepsy, pulmonary embolism, transverse myelitis, and thrombosis with thrombocytopenia. Age-sex standardised incidence rate ratios (SIR) were estimated to compare post-COVID-19 to pre-pandemic rates in each of the databases. FINDINGS: Substantial heterogeneity by age was seen for AESI rates, with some clearly increasing with age but others following the opposite trend. Similarly, differences were also observed across databases for same health outcome and age-sex strata. All studied AESIs appeared consistently more common in the post-COVID-19 compared to the historical cohorts, with related meta-analytic SIRs ranging from 1.32 (1.05 to 1.66) for narcolepsy to 11.70 (10.10 to 13.70) for pulmonary embolism. INTERPRETATION: Our findings suggest all AESIs are more common after COVID-19 than in the general population. Thromboembolic events were particularly common, and over 10-fold more so. More research is needed to contextualise post-COVID-19 complications in the longer term. FUNDING: None

Histone H4K20 methylation mediated chromatin compaction threshold ensures genome integrity by limiting DNA replication licensing

Cell cycle and replication need to be tightly regulated to ensure genome stability in mammalian cells. Here the authors provide a link between chromatin structure and DNA replication regulation by showing that chromatin compaction limits replication licensing thereby promoting genome integrity

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.</p

Characteristics and outcomes of over 300,000 patients with COVID-19 and history of cancer in the United States and Spain

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 ¼ 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.</p

An abundance of good things : Paul Ricoeur\u27s cultural imagination and a phenomenology of Deuteronomism

https://place.asburyseminary.edu/ecommonsatsdissertations/2489/thumbnail.jp

Frequency of nonaspirin NSAID-relevant coexisting medical conditions in the primary-care setting: a retrospective database review

Leslie Bloom,1 Kathleen E Boyle,2 Andrew E Myers,1 Claire Blacketer,3 Rachel Weinstein3 1Johnson &amp; Johnson Consumer Inc, McNeil Consumer Healthcare Division, Fort Washington, PA, USA; 2KE Boyle Consultants, Exton, PA, USA; 3Janssen Research and Development, Titusville, NJ, USA Background: Coexisting medical conditions and concomitant medications contribute to treatment challenges primary-care professionals (PCPs) face daily. The current study assessed the extent and distribution of nonaspirin NSAID-relevant coexisting medical conditions of interest (CMCOI) in patients visiting PCPs. Methods: This retrospective database review analyzed data from three large health-care claim databases to identify the frequency of nonaspirin NSAID-relevant CMCOI among adults aged &ge;18 years with a PCP visit in 2013. Claim databases employed were the Truven Health MarketScan&reg; Commercial Claims and Encounters database, representative of the privately insured (PI) population; Truven Health MarketScan Multi-State Medicaid, representative of the Medicaid population (Medicaid); and Truven MarketScan Medicare Supplemental, representative of the Medicare population with employer-based supplemental Medicare insurance (Medicare-Supplement). Nonaspirin NSAID-relevant CMCOI, asthma, cardiovascular risk factors, gastrointestinal bleeding risk factors, and renal insufficiency were chosen based on US NSAID over-the-counter Drug Facts label warnings. Frequency of CMCOI was determined for those without and with a musculoskeletal diagnosis. Results: In each database, &ge;19% (19.0% PI, 29.9% Medicaid, 33.6% Medicare-Supplement) had a musculoskeletal diagnosis. A greater proportion of individuals with a musculoskeletal diagnosis had one or more CMCOI compared with those without a musculoskeletal diagnosis (61.3% vs 50.4% PI, 78.1% vs 66.8% Medicaid, 87.1% vs 82.3% Medicare-Supplement). The frequency of one or more CMCOI increased with age in each database. Across databases among CMCOI, cardiovascular risk factors were most common, followed by gastrointestinal bleeding risk factors, and proportions were higher among those with a musculoskeletal diagnosis. Conclusion: These data confirm the high frequency of nonaspirin NSAID-relevant CMCOI among patients presenting to PCPs for musculoskeletal diagnosis, as well as among older patients. These analyses reinforce the critical role health-care professionals can play in identifying patients with nonaspirin NSAID-relevant CMCOI, providing those patients with ongoing guidance on appropriate choice and use of over-the-counter analgesics, and educating patients about the impact aging, health status, concomitant conditions, and medicines have on selection of all medicines, including analgesics. Keywords: coexisting medical conditions, NSAIDs, musculoskeletal disease, health claim databases, OTC analgesic

European Health Data &amp; Evidence Network-learnings from building out a standardized international health data network

ObjectiveHealth data standardized to a common data model (CDM) simplifies and facilitates research. This study examines the factors that make standardizing observational health data to the Observational Medical Outcomes Partnership (OMOP) CDM successful.Materials and methodsTwenty-five data partners (DPs) from 11 countries received funding from the European Health Data Evidence Network (EHDEN) to standardize their data. Three surveys, DataQualityDashboard results, and statistics from the conversion process were analyzed qualitatively and quantitatively. Our measures of success were the total number of days to transform source data into the OMOP CDM and participation in network research.ResultsThe health data converted to CDM represented more than 133 million patients. 100%, 88%, and 84% of DPs took Surveys 1, 2, and 3. The median duration of the 6 key extract, transform, and load (ETL) processes ranged from 4 to 115 days. Of the 25 DPs, 21 DPs were considered applicable for analysis of which 52% standardized their data on time, and 48% participated in an international collaborative study.DiscussionThis study shows that the consistent workflow used by EHDEN proves appropriate to support the successful standardization of observational data across Europe. Over the 25 successful transformations, we confirmed that getting the right people for the ETL is critical and vocabulary mapping requires specific expertise and support of tools. Additionally, we learned that teams that proactively prepared for data governance issues were able to avoid considerable delays improving their ability to finish on time.ConclusionThis study provides guidance for future DPs to standardize to the OMOP CDM and participate in distributed networks. We demonstrate that the Observational Health Data Sciences and Informatics community must continue to evaluate and provide guidance and support for what ultimately develops the backbone of how community members generate evidence

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objective As a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). Methods We show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. Results Our open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. Conclusion Our results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world