scholarly journals Characteristics and outcomes of patients with COVID-19 with and without prevalent hypertension: a multinational cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e057632
Author(s):  
Carlen Reyes ◽  
Andrea Pistillo ◽  
Sergio Fernández-Bertolín ◽  
Martina Recalde ◽  
Elena Roel ◽  
...  
Keyword(s):  
Cohort Study ◽  
Data Model ◽  
Distress Syndrome ◽  
Adverse Outcomes ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Hypertension Status ◽  
Hospitalised Patients ◽  
The Usa

ObjectiveTo characterise patients with and without prevalent hypertension and COVID-19 and to assess adverse outcomes in both inpatients and outpatients.Design and settingThis is a retrospective cohort study using 15 healthcare databases (primary and secondary electronic healthcare records, insurance and national claims data) from the USA, Europe and South Korea, standardised to the Observational Medical Outcomes Partnership common data model. Data were gathered from 1 March to 31 October 2020.ParticipantsTwo non-mutually exclusive cohorts were defined: (1) individuals diagnosed with COVID-19 (diagnosed cohort) and (2) individuals hospitalised with COVID-19 (hospitalised cohort), and stratified by hypertension status. Follow-up was from COVID-19 diagnosis/hospitalisation to death, end of the study period or 30 days.OutcomesDemographics, comorbidities and 30-day outcomes (hospitalisation and death for the ‘diagnosed’ cohort and adverse events and death for the ‘hospitalised’ cohort) were reported.ResultsWe identified 2 851 035 diagnosed and 563 708 hospitalised patients with COVID-19. Hypertension was more prevalent in the latter (ranging across databases from 17.4% (95% CI 17.2 to 17.6) to 61.4% (95% CI 61.0 to 61.8) and from 25.6% (95% CI 24.6 to 26.6) to 85.9% (95% CI 85.2 to 86.6)). Patients in both cohorts with hypertension were predominantly >50 years old and female. Patients with hypertension were frequently diagnosed with obesity, heart disease, dyslipidaemia and diabetes. Compared with patients without hypertension, patients with hypertension in the COVID-19 diagnosed cohort had more hospitalisations (ranging from 1.3% (95% CI 0.4 to 2.2) to 41.1% (95% CI 39.5 to 42.7) vs from 1.4% (95% CI 0.9 to 1.9) to 15.9% (95% CI 14.9 to 16.9)) and increased mortality (ranging from 0.3% (95% CI 0.1 to 0.5) to 18.5% (95% CI 15.7 to 21.3) vs from 0.2% (95% CI 0.2 to 0.2) to 11.8% (95% CI 10.8 to 12.8)). Patients in the COVID-19 hospitalised cohort with hypertension were more likely to have acute respiratory distress syndrome (ranging from 0.1% (95% CI 0.0 to 0.2) to 65.6% (95% CI 62.5 to 68.7) vs from 0.1% (95% CI 0.0 to 0.2) to 54.7% (95% CI 50.5 to 58.9)), arrhythmia (ranging from 0.5% (95% CI 0.3 to 0.7) to 45.8% (95% CI 42.6 to 49.0) vs from 0.4% (95% CI 0.3 to 0.5) to 36.8% (95% CI 32.7 to 40.9)) and increased mortality (ranging from 1.8% (95% CI 0.4 to 3.2) to 25.1% (95% CI 23.0 to 27.2) vs from 0.7% (95% CI 0.5 to 0.9) to 10.9% (95% CI 10.4 to 11.4)) than patients without hypertension.ConclusionsCOVID-19 patients with hypertension were more likely to suffer severe outcomes, hospitalisations and deaths compared with those without hypertension.

Maternal age at birth and daughter’s fecundability

2021 ◽  
Author(s):  
Olga Basso ◽  
Sydney K Willis ◽  
Elizabeth E Hatch ◽  
Ellen M Mikkelsen ◽  
Kenneth J Rothman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Maternal Age ◽  
Limited Information ◽  
Loss To Follow Up ◽  
Older Mothers ◽  
Registration Number ◽  
The Usa ◽  
Competing Interest

Abstract STUDY QUESTION Do daughters of older mothers have lower fecundability? SUMMARY ANSWER In this cohort study of North American pregnancy planners, there was virtually no association between maternal age ≥35 years and daughters’ fecundability. WHAT IS KNOWN ALREADY Despite suggestive evidence that daughters of older mothers may have lower fertility, only three retrospective studies have examined the association between maternal age and daughter’s fecundability. STUDY DESIGN, SIZE, DURATION Prospective cohort study of 6689 pregnancy planners enrolled between March 2016 and January 2020. PARTICIPANTS/MATERIALS, SETTING, METHODS Pregnancy Study Online (PRESTO) is an ongoing pre-conception cohort study of pregnancy planners (age, 21-45 years) from the USA and Canada. We estimated fecundability ratios (FR) for maternal age at the participant’s birth using multivariable proportional probabilities regression models. MAIN RESULTS AND THE ROLE OF CHANCE Daughters of mothers ≥30 years were less likely to have previous pregnancies (or pregnancy attempts) or risk factors for infertility, although they were more likely to report that their mother had experienced problems conceiving. The proportion of participants with prior unplanned pregnancies, a birth before age 21, ≥3 cycles of attempt at study entry or no follow-up was greater among daughters of mothers <25 years. Compared with maternal age 25–29 years, FRs (95% CI) for maternal age <20, 20–24, 30–34, and ≥35 were 0.72 (0.61, 0.84), 0.92 (0.85, 1.00), 1.08 (1.00, 1.17), and 1.00 (0.89, 1.12), respectively. LIMITATIONS, REASONS FOR CAUTION Although the examined covariates did not meaningfully affect the associations, we had limited information on the participants’ mother. Differences by maternal age in reproductive history, infertility risk factors and loss to follow-up suggest that selection bias may partly explain our results. WIDER IMPLICATIONS OF THE FINDINGS Our finding that maternal age 35 years or older was not associated with daughter’s fecundability is reassuring, considering the trend towards delayed childbirth. However, having been born to a young mother may be a marker of low fecundability among pregnancy planners. STUDY FUNDING/COMPETING INTEREST(S) PRESTO was funded by NICHD Grants (R21-HD072326 and R01-HD086742) and has received in-kind donations from Swiss Precision Diagnostics, FertilityFriend.com, Kindara.com, and Sandstone Diagnostics. Dr Wise is a fibroid consultant for AbbVie, Inc. TRIAL REGISTRATION NUMBER n/a

scholarly journals Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data

2021 ◽  
Author(s):  
Vlasios K. Dimitriadis ◽  
George I. Gavriilidis ◽  
Pantelis Natsiavas
Keyword(s):  
Information Technology ◽  
Electronic Health Records ◽  
Drug Safety ◽  
Data Model ◽  
Health Data ◽  
Common Data Model ◽  
Clinical Environment ◽  
Drug Reactions ◽  
Medical Outcomes ◽  
Integrate Data

Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership – Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.

scholarly journals LOD Development System for Medical Information Standard

2018 ◽  
Vol 7 (3.33) ◽  
pp. 225
Author(s):  
Hee-kyung Moon ◽  
Sung-kook Han ◽  
Chang-ho An
Keyword(s):  
Data Model ◽  
Medical Information ◽  
Open Data ◽  
Linked Open Data ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Systematic Analysis ◽  
Development System ◽  
Observational Database

This paper describes Linked Open Data(LOD) development system and its application of medical information standard as Observational Medical Outcomes Partnership(OMOP) Common Data Model(CDM). The OMOP CDM allows for the systematic analysis of disparate observational database in each hospital. This paper describes a LOD instance development system based on SII. It can generate the application-specified instance development system automatically. Therefore, we applied by medical information standard as OMOP CDM to LOD development system. As a result, it was confirmed that there is no problem in applying to the standardization of medical information using the LOD development system.  

scholarly journals Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models (Preprint)

2020 ◽  
Author(s):  
Seungho Jeon ◽  
Jeongeun Seo ◽  
Sukyoung Kim ◽  
Jeongmoon Lee ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Model ◽  
Personal Information ◽  
Health Data ◽  
Common Data Model ◽  
Patient Privacy ◽  
Medical Outcomes ◽  
Cloud Computing System ◽  
Privacy Models ◽  
Identification Strategy

BACKGROUND De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. OBJECTIVE This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. METHODS The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. RESULTS The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. CONCLUSIONS Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

scholarly journals Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

2015 ◽  
Vol 06 (03) ◽  
pp. 536-547 ◽  
Author(s):  
F.S. Resnic ◽  
S.L. Robbins ◽  
J. Denton ◽  
L. Nookala ◽  
D. Meeker ◽  
...  
Keyword(s):  
Health Systems ◽  
Data Model ◽  
Comparative Effectiveness ◽  
Large Scale ◽  
Medication Administration ◽  
Use Cases ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Data Set ◽  
Case Examples

SummaryBackground: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system‘s medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. A Case Report on Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6: 536–547http://dx.doi.org/10.4338/ACI-2014-12-CR-0121

scholarly journals Protocol for the ROBUST (Registry Of type B aortic dissection with the Utility of STent graft) study: an ambispective, multicentre, open cohort study

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e019317 ◽  
Author(s):  
Dan Rong ◽  
Yangyang Ge ◽  
Yan Xue ◽  
Feng Liu ◽  
Kai Lu ◽  
...  
Keyword(s):  
Cohort Study ◽  
Aortic Dissection ◽  
Stent Graft ◽  
Cox Proportional Hazards ◽  
Adverse Outcomes ◽  
Imaging Features ◽  
Type B ◽  
Type B Aortic Dissection ◽  
Satisfactory Outcome

IntroductionThoracic endovascular aortic repair (TEVAR) is widely used for type B aortic dissection, although with satisfactory outcome in a limited proportion of patients. To better inform patient prognostication, the Registry Of type B aortic dissection with the Utility of STent graft (ROBUST) study aims to identify imaging-based predictors of post-TEVAR adverse outcomes up to 10-year follow-up.Methods and analysisROBUST is designed as an ambispective, multicentre, open cohort study. All patients undergoing TEVAR from 1 January 2008 to 1 July 2027 at participating centres will be invited to join the study. It is conservatively estimated that over 2000 patients will join the study. Data on demographics, disease history, procedural details, imaging features and follow-up will be collected after discharge. Cox proportional-hazards analysis will be used to identify independent predictors of primary outcomes. Stratification analysis will be performed to identify which subgroup of patients would benefit the most from TEVAR.Ethics and disseminationThe protocol has been approved by the ethics committee of the coordinating centre. Findings will be disseminated in professional peer-reviewed journals to promote understanding of the rehabilitation process.Trial registration numberChiCTR-POC-17011726; Pre-results.

scholarly journals Extract, Transform, Load Framework for the Conversion of Health Databases to OMOP

2021 ◽  
Author(s):  
Juan C. Quiroz ◽  
Tim Chard ◽  
Zhisheng Sa ◽  
Angus Ritchie ◽  
Louisa Jorm ◽  
...  
Keyword(s):  
Data Model ◽  
Web Application ◽  
Research Community ◽  
Common Data Model ◽  
Medical Outcomes ◽  
Data Manipulation ◽  
Community Needs ◽  
Mapping Process ◽  
The Cost ◽  
Manipulation Language

ABSTRACTObjectiveDevelop an extract, transform, load (ETL) framework for the conversion of health databases to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) that supports transparency of the mapping process, readability, refactoring, and maintainability.Materials and MethodsWe propose an ETL framework that is metadata-driven and generic across source datasets. The ETL framework reads mapping logic for OMOP tables from YAML files, which organize SQL snippets in key-value pairs that define the extract and transform logic to populate OMOP columns.ResultsWe developed a data manipulation language (DML) for writing the mapping logic from health datasets to OMOP, which defines mapping operations on a column-by-column basis. A core ETL pipeline converts the DML in YAML files and generates an ETL script. We provide access to our ETL framework via a web application, allowing users to upload and edit YAML files and obtain an ETL SQL script that can be used in development environments.DiscussionThe structure of the DML and the mapping operations defined in column-by-column operations maximizes readability, refactoring, and maintainability, while minimizing technical debt, and standardizes the writing of ETL operations for mapping to OMOP. Our web application allows institutions and teams to reuse the ETL pipeline by writing their own rules using our DML.ConclusionThe research community needs tools that reduce the cost and time effort needed to map datasets to OMOP. These tools must support transparency of the mapping process for mapping efforts to be reused by different institutions.

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