Observational Health Data Sciences And Informatics Research Articles

Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model.

We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) and a single reference table from Observational Health Data Sciences And Informatics (OHDSI). The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research Datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics® Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient Database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible results.

The pivotal role of data harmonization in revolutionizing global healthcare: a framework and a case study

Aim: Data harmonization standardizes healthcare information, enhancing accessibility and interoperability, which is crucial for improving patient outcomes and driving medical research and innovation. It enables precise diagnoses and personalized treatments, and boosts AI model efficiency. However, significant challenges such as ethical concerns, technical barriers in the data lifecycle, AI biases, and varied regional regulations impede progress, underscoring the need for solutions like adopting universal standards such as HL7 FHIR, where the lack of generalized harmonization efforts is significant. Methods: We propose an advanced, holistic framework that utilizes FAIR-compliant reference ontologies (based on the FAIRplus and FAIR CookBook criteria) to make data findable, accessible, interoperable, and reusable enriched with terminologies from OHDSI (Observational Health Data Sciences and Informatics) vocabularies and word embeddings to identify lexical and conceptual overlaps across heterogeneous data models. Results: The proposed approach was applied to autoimmune diseases, cardiovascular diseases, and mental disorders using unstructured data from EU cohorts involving 7,551 patients with primary Sjogren’s Syndrome, 25,000 patients with cardiovascular diseases, and 3,500 patients with depression and anxiety. Metadata from these datasets were structured into dictionaries and linked with three newly developed reference ontologies (ROPSS, ROCVD, and ROMD), which are accessible on GitHub. These ontologies facilitated data interoperability across different systems and helped identify common terminologies with high precision within each domain. Conclusion: Through the proposed framework, we aim to urge the adoption of data harmonization as a priority, emphasizing the need for global cooperation, investment in technology and infrastructure, and adherence to ethical data usage practices toward a more efficient and patient-centered global healthcare system.

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

Objective or PurposeA) To characterize the incidence of kidney failure associated with intravitreal anti-vascular endothelial growth factor (VEGF) exposure, and B) compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DesignRetrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. Subjects, Participants, and/or ControlsSubjects 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, Intervention, or TestingA) The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. B) 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 MeasuresIncidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. ResultsOf 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 to 2389), and incidence rate 743 per 100,000 person-years (0 to 2661). The meta-analysis HR of kidney failure comparing aflibercept to ranibizumab was 1.01 (95% confidence interval (CI) 0.70, 1.47, p=0.45), ranibizumab to bevacizumab 0.95 (95% CI 0.68, 1.32, p=0.62), and aflibercept to bevacizumab 0.95 (95% CI 0.65, 1.39, p=0.60). ConclusionsThere was no substantially different relative risk for kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk for 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.

Towards global model generalizability: independent cross-site feature evaluation for patient-level risk prediction models using theOHDSI network.

Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability. Electronic health records (EHRs) from 4 countries (United States, United Kingdom, Finland, and Korea) were mapped to the OMOP Common Data Model (CDM), 2008-2019. Machine learning (ML) models were developed to predict post-surgery prolonged opioid use (POU) risks using data collected 6 months before surgery. Both local and cross-site feature selection methods were applied in the development and external validation datasets. Models were developed using Observational Health Data Sciences and Informatics (OHDSI) tools and validated on separate patient cohorts. Model development included 41929 patients, 14.6% with POU. The external validation included 31932 (UK), 23100 (US), 7295 (Korea), and 3934 (Finland) patients with POU of 44.2%, 22.0%, 15.8%, and 21.8%, respectively. The top-performing model, Lasso logistic regression, achieved an area under the receiver operating characteristic curve (AUROC) of 0.75 during local validation and 0.69 (SD = 0.02) (averaged) in external validation. Models trained with cross-site feature selection significantly outperformed those using only features from the development site through external validation (P < .05). Using EHRs across four countries mapped to the OMOP CDM, we developed generalizable predictive models for POU. Our approach demonstrates the significant impact of cross-site feature selection in improving model performance, underscoring the importance of incorporating diverse feature sets from various clinical settings to enhance the generalizability and utility of predictive healthcare models.

Conceptual design of a generic data harmonization process for OMOP common data model

BackgroundTo gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM.MethodsFor this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps.ResultsFrom 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps.ConclusionsThe generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data

The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared. Designed to run across a wide array of technical environments, including different operating systems and database platforms, HADES uses continuous integration with a large set of unit tests to maintain reliability. HADES implements OHDSI best practices, and is used in almost all published OHDSI studies, including some that have directly informed regulatory decisions.

P1156 Characteristics and outcomes of over a million Inflammatory Bowel Disease patients in seven countries: a multinational cohort study

Abstract Background Population-based studies are crucial for gaining insights into epidemiology, risk factors, and outcomes of IBD. However, studies that characterize patient cohorts typically analyze a single resource, apply diverse eligibility criteria, and extract different sets of attributes. This study aims to address this gap by employing a unified data and analysis framework developed by the Observational Health Data Sciences and Informatics (OHDSI) community to longitudinally describe and compare IBD patient cohorts across multiple geographic regions. Methods Using routinely collected healthcare data harmonized to the OMOP Common Data Model, we longitudinally characterized IBD patient cohorts across diverse geographic regions. Data sources included insurance claims and electronic health records from primary and tertiary settings. IBD cohorts were defined based on diagnoses and medications, and stratified into various groups (e.g. by sex and age at diagnosis). Results We characterized disease trajectory of 462,502 Crohn’s disease (CD) and 589,118 ulcerative colitis (UC) subjects, in 16 databases (DBs) from seven countries worldwide (table 1). There was a noticeable trend toward a decrease in the average age at CD diagnosis in Europe and North America (e.g., 47.5 in 2005-2010 period to 44.3 in 2020-2022 at a US DB) contrasting with more stable patterns in Southeast Asia (e.g., 33.7 to 33.9 respectively in Japan). A similar, though weaker, decrease in diagnosis age has been observed in UC subjects. Prevalence rates of pre-diagnosis symptoms (abdominal pain, diarrhea, and rectal bleeding) were relatively stable over time, with minor differences between DBs. The proportion of females with anxiety was consistently higher across all datasets compared to males. Similarly, depression prevalence ranged between 11% to 46% in females and 0% to 33% in males. Examining surgical outcomes for CD patients, our study uncovered nuanced trends. While there hasn't been a significant decrease in colonic resection rates within one year following diagnosis between the early 2000s and 2020 in US populations, some DBs demonstrated increased resection rates over time (from 11% in 2005 to 14% in 2020 and from 5% to 11% in two different US DBs). A similar trend was observed when examining the colonic resection rate 3 years post diagnosis. Small bowel resection rates were stable along time among most DBs one- and three-years following diagnosis. Conclusion This study, leveraging OHDSI network data and tools, provides a comprehensive characterization of IBD patient cohorts from various areas. It furnishes valuable insights into disease trends, demographic variations, outcome, and pre-diagnosis symptoms in CD and UC contributing to a deeper understanding of IBD epidemiology

OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization.

The Observational Health Data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data harmonization. We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent concepts. The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard reporting. OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

A framework for understanding an open scientific community using automated harvesting of public artifacts.

The Observational Health Data Sciences and Informatics (OHDSI) community has emerged as a leader in observational research on real-world clinical data for promoting evidence for healthcare and decision-making. The community has seen rapid growth in publications, citations, and the number of authors. Components of its successful uptake have been attributed to an open science and collaborative culture for research and development. Investigating the adoption of OHDSI as a field of study provides an opportunity to understand how communities embrace new ideas, onboard new members, and enhance their impact. To track, study, and evaluate an open scientific community's growth and impact. We present a modern architecture leveraging open application programming interfaces to capture publicly available data (PubMed, YouTube, and EHDEN) on open science activities (publication, teaching, and engagement). Three interactive dashboard were implemented for each publicly available artifact (PubMed, YouTube, and EHDEN). Each dashboard provides longitudinal summary analysis and has a searchable table, which differs in the available features related to each public artifact. We discuss the insights enabled by our approach to monitor the growth and impact of the OHDSI community by capturing artifacts of learning, teaching, and creation. We share the implications for different users based on their functional needs. As other scientific networks adopt open-source frameworks, our framework serves as a model for tracking the growth of their community, driving the perception of their development, engaging their members, and attaining higher impact.

Assessing the Use of German Claims Data Vocabularies for Research in the Observational Medical Outcomes Partnership Common Data Model: Development and Evaluation Study.

National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP CDM. This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based research. To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health Data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP CDM. Our vocabulary preparation approach was able to map 3 missing German vocabularies to standard concepts and prepare 8 vocabularies as new 2-billion concepts. The completeness evaluation showed that the prepared vocabularies cover 44.3% (3288/7417) of the source codes contained in German claims data. The correctness evaluation revealed that the specified validity periods in the OMOP CDM are compliant for the majority (705,531/706,032, 99.9%) of source codes and associated dates in German claims data. The calculation of the vocabulary coverage showed a noticeable decrease of missing vocabularies from 55% (11/20) to 10% (2/20) due to our preparation approach. By preparing 10 vocabularies, we showed that our approach is applicable to any type of vocabulary used in a source data set. The prepared vocabularies are currently limited to German vocabularies, which can only be used in national OMOP CDM research projects, because the mapping of new 2-billion concepts to standard concepts is missing. To participate in international OHDSI network studies with German claims data, future work is required to map the prepared 2-billion concepts to standard concepts.

Prescribing Patterns of Oral Antibiotics and Isotretinoin for Acne in a Colorado Hospital System: Retrospective Cohort Study.

Guidelines established by the American Academy of Dermatology recommend oral antibiotics as first-line therapy for mild, moderate, and severe acne. However, it is recommended to minimize the duration of oral antibiotic use, and there is increasing support for other systemic agents for acne. We sought to characterize the use of oral antibiotics and isotretinoin for the treatment of acne in the pediatric and young adult population aged 10 through 20 years and the adult population aged 21 to 45 years from 2011 to 2019. We conducted a retrospective, observational cohort study using electronic data from the enterprise data warehouse of the University of Colorado Anschutz Medical Campus and its affiliates, with data in the format of the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) common data model. Categorical values (sex, race, and ethnicity) were compared using chi-square tests, and continuous variables (age) were compared using 2-tailed t tests. Our cohort of 15,704 patients was composed of mostly White (12,776/15,704, 81.4%), non-Hispanic or Latino (13,307/15,704, 84.7%), and female (11,093/15,704, 70.6%) patients. Among the 4605 male patients in the eligible cohort, 1810 (39%) received an oral antibiotic treatment, in comparison to 3109 (28%) of the 11,093 eligible women (P<.001). Among the 4605 men who were eligible for treatment with isotretinoin in this population, 988 (21.5%) received a course of isotretinoin, compared to only 10.4% (1159/11,093) eligible women (P<.001). Male patients were 1.67 times more likely to have received an antibiotic prescription(odds ratio [OR] 1.67, 95% CI 1.55-1.79) and over twice as likely to have received an isotretinoin prescription (OR 2.34, 95% CI 2.13-2.57) than female patients. Minocycline was the most frequently prescribed antibiotic for the treatment of acne in this study cohort. From 2015 to 2019, there was no significant change in the number of antibiotic prescriptions over time. Men were significantly more likely to receive both oral antibiotics and isotretinoin than female patients. Multiple factors could be contributing to this discrepancy, including the burden of iPLEDGE, additional systemic treatment options for female patients, and the difference in acne severity across sexes. We could not determine the difference in severity of acne between male and female patients in our cohort, and further research is needed to ascertain the variation across sexes.

ReDWINE: A clinical datamart with text analytical capabilities to facilitate rehabilitation research

Rehabilitation research focuses on determining the components of a treatment intervention, the mechanism of how these components lead to recovery and rehabilitation, and ultimately the optimal intervention strategies to maximize patients’ physical, psychologic, and social functioning. Traditional randomized clinical trials that study and establish new interventions face challenges, such as high cost and time commitment. Observational studies that use existing clinical data to observe the effect of an intervention have shown several advantages over RCTs. Electronic Health Records (EHRs) have become an increasingly important resource for conducting observational studies. To support these studies, we developed a clinical research datamart, called ReDWINE (Rehabilitation Datamart With Informatics iNfrastructure for rEsearch), that transforms the rehabilitation-related EHR data collected from the UPMC health care system to the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate rehabilitation research. The standardized EHR data stored in ReDWINE will further reduce the time and effort required by investigators to pool, harmonize, clean, and analyze data from multiple sources, leading to more robust and comprehensive research findings. ReDWINE also includes deployment of data visualization and data analytics tools to facilitate cohort definition and clinical data analysis. These include among others the Open Health Natural Language Processing (OHNLP) toolkit, a high-throughput NLP pipeline, to provide text analytical capabilities at scale in ReDWINE. Using this comprehensive representation of patient data in ReDWINE for rehabilitation research will facilitate real-world evidence for health interventions and outcomes.

Assessing the FAIRness of databases on the EHDEN portal: A case study on two Dutch ICU databases

ObjectiveTo address the growing need for effective data reuse in health research, healthcare institutions need to make their data Findable, Accessible, Interoperable, and Reusable (FAIR). A prevailing method to model databases for interoperability is the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative. A European repository for OMOP CDM-converted databases called the “European Health Data & Evidence Network (EHDEN) portal” was developed, aiming to make these databases Findable and Accessible. This paper aims to assess the FAIRness of databases on the EHDEN portal. Materials and methodsTwo researchers involved in the OMOP CDM conversion of separate Dutch Intensive Care Unit (ICU) research databases each manually assessed their own database using seventeen metrics. These were defined by the FAIRsFAIR project as a list of minimum requirements for a database to be FAIR. Each metric is given a score from zero to four based on how well the database adheres to the metric. The maximum score for each metric varies from one to four based on the importance of the metric. ResultsFourteen out of the seventeen metrics were unanimously rated: seven were rated the highest score, one was rated half of the highest score, and five were rated the lowest score. The remaining three metrics were assessed differently for the two use cases. The total scores achieved were 15.5 and 12 out of a maximum of 25. ConclusionThe main omissions in supporting FAIRness were the lack of globally unique identifiers such as Uniform Resource Identifiers (URIs) in the OMOP CDM and the lack of metadata standardization and linkage in the EHDEN portal. By implementing these in future updates, the EHDEN portal can be more FAIR.

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Clinical documentation in electronic health records contains crucial narratives and details about patients and their care. Natural language processing (NLP) can unlock the information conveyed in clinical notes and reports, and thus plays a critical role in real-world studies. The NLP Working Group at the Observational Health Data Sciences and Informatics (OHDSI) consortium was established to develop methods and tools to promote the use of textual data and NLP in real-world observational studies. In this paper, we describe a framework for representing and utilizing textual data in real-world evidence generation, including representations of information from clinical text in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), the workflow and tools that were developed to extract, transform and load (ETL) data from clinical notes into tables in OMOP CDM, as well as current applications and specific use cases of the proposed OHDSI NLP solution at large consortia and individual institutions with English textual data. Challenges faced and lessons learned during the process are also discussed to provide valuable insights for researchers who are planning to implement NLP solutions in real-world studies.

Using a data-driven approach for the development and evaluation of phenotype algorithms for systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease of unknown origin. The objective of this research was to develop phenotype algorithms for SLE suitable for use in epidemiological studies using empirical evidence from observational databases. We used a process for empirically determining and evaluating phenotype algorithms for health conditions to be analyzed in observational research. The process started with a literature search to discover prior algorithms used for SLE. We then used a set of Observational Health Data Sciences and Informatics (OHDSI) open-source tools to refine and validate the algorithms. These included tools to discover codes for SLE that may have been missed in prior studies and to determine possible low specificity and index date misclassification in algorithms for correction. We developed four algorithms using our process: two algorithms for prevalent SLE and two for incident SLE. The algorithms for both incident and prevalent cases are comprised of a more specific version and a more sensitive version. Each of the algorithms corrects for possible index date misclassification. After validation, we found the highest positive predictive value estimate for the prevalent, specific algorithm (89%). The highest sensitivity estimate was found for the sensitive, prevalent algorithm (77%). We developed phenotype algorithms for SLE using a data-driven approach. The four final algorithms may be used directly in observational studies. The validation of these algorithms provides researchers an added measure of confidence that the algorithms are selecting subjects correctly and allows for the application of quantitative bias analysis.

Methodological Issues in Using a Common Data Model of COVID-19 Vaccine Uptake and Important Adverse Events of Interest: Feasibility Study of Data and Connectivity COVID-19 Vaccines Pharmacovigilance in the United Kingdom.

BackgroundThe Data and Connectivity COVID-19 Vaccines Pharmacovigilance (DaC-VaP) UK-wide collaboration was created to monitor vaccine uptake and effectiveness and provide pharmacovigilance using routine clinical and administrative data. To monitor these, pooled analyses may be needed. However, variation in terminologies present a barrier as England uses the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), while the rest of the United Kingdom uses the Read v2 terminology in primary care. The availability of data sources is not uniform across the United Kingdom.ObjectiveThis study aims to use the concept mappings in the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to identify common concepts recorded and to report these in a repeated cross-sectional study. We planned to do this for vaccine coverage and 2 adverse events of interest (AEIs), cerebral venous sinus thrombosis (CVST) and anaphylaxis. We identified concept mappings to SNOMED CT, Read v2, the World Health Organization’s International Classification of Disease Tenth Revision (ICD-10) terminology, and the UK Dictionary of Medicines and Devices (dm+d).MethodsExposures and outcomes of interest to DaC-VaP for pharmacovigilance studies were selected. Mappings of these variables to different terminologies used across the United Kingdom’s devolved nations’ health services were identified from the Observational Health Data Sciences and Informatics (OHDSI) Automated Terminology Harmonization, Extraction, and Normalization for Analytics (ATHENA) online browser. Lead analysts from each nation then confirmed or added to the mappings identified. These mappings were then used to report AEIs in a common format. We reported rates for windows of 0-2 and 3-28 days postvaccine every 28 days.ResultsWe listed the mappings between Read v2, SNOMED CT, ICD-10, and dm+d. For vaccine exposure, we found clear mapping from OMOP to our clinical terminologies, though dm+d had codes not listed by OMOP at the time of searching. We found a list of CVST and anaphylaxis codes. For CVST, we had to use a broader cerebral venous thrombosis conceptual approach to include Read v2. We identified 56 SNOMED CT codes, of which we selected 47 (84%), and 15 Read v2 codes. For anaphylaxis, our refined search identified 60 SNOMED CT codes and 9 Read v2 codes, of which we selected 10 (17%) and 4 (44%), respectively, to include in our repeated cross-sectional studies.ConclusionsThis approach enables the use of mappings to different terminologies within the OMOP CDM without the need to catalogue an entire database. However, Read v2 has less granular concepts than some terminologies, such as SNOMED CT. Additionally, the OMOP CDM cannot compensate for limitations in the clinical coding system. Neither Read v2 nor ICD-10 is sufficiently granular to enable CVST to be specifically flagged. Hence, any pooled analysis will have to be at the less specific level of cerebrovascular venous thrombosis. Overall, the mappings within this CDM are useful, and our method could be used for rapid collaborations where there are only a limited number of concepts to pool.

PheValuator 2.0: Methodological improvements for the PheValuator approach to semi-automated phenotype algorithm evaluation

PurposePhenotype algorithms are central to performing analyses using observational data. These algorithms translate the clinical idea of a health condition into an executable set of rules allowing for queries of data elements from a database. PheValuator, a software package in the Observational Health Data Sciences and Informatics (OHDSI) tool stack, provides a method to assess the performance characteristics of these algorithms, namely, sensitivity, specificity, and positive and negative predictive value. It uses machine learning to develop predictive models for determining a probabilistic gold standard of subjects for assessment of cases and non-cases of health conditions. PheValuator was developed to complement or even replace the traditional approach of algorithm validation, i.e., by expert assessment of subject records through chart review. Results in our first PheValuator paper suggest a systematic underestimation of the PPV compared to previous results using chart review. In this paper we evaluate modifications made to the method designed to improve its performance. MethodsThe major changes to PheValuator included allowing all diagnostic conditions, clinical observations, drug prescriptions, and laboratory measurements to be included as predictors within the modeling process whereas in the prior version there were significant restrictions on the included predictors. We also have allowed for the inclusion of the temporal relationships of the predictors in the model. To evaluate the performance of the new method, we compared the results from the new and original methods against results found from the literature using traditional validation of algorithms for 19 phenotypes. We performed these tests using data from five commercial databases. ResultsIn the assessment aggregating all phenotype algorithms, the median difference between the PheValuator estimate and the gold standard estimate for PPV was reduced from −21 (IQR −34, −3) in Version 1.0 to 4 (IQR −3, 15) using Version 2.0. We found a median difference in specificity of 3 (IQR 1, 4.25) for Version 1.0 and 3 (IQR 1, 4) for Version 2.0. The median difference between the two versions of PheValuator and the gold standard for estimates of sensitivity was reduced from −39 (-51, −20) to −16 (-34, −6). ConclusionPheValuator 2.0 produces estimates for the performance characteristics for phenotype algorithms that are significantly closer to estimates from traditional validation through chart review compared to version 1.0. With this tool in researcher’s toolkits, methods, such as quantitative bias analysis, may now be used to improve the reliability and reproducibility of research studies using observational data.

170. Utilizing big data to determine the safety profile of recombinant human BMP-2 in spinal fusion surgery: an analysis of 5 databases from 2003 to 2017

<h3>BACKGROUND CONTEXT</h3> The use of recombinant human bone morphogenetic protein-2 (rhBMP-2) in spinal fusion remains controversial. While rhBMP-2 safety concerns revealed misrepresentations in industry-sponsored research, previous observational studies reached conflicting conclusions regarding rhBMP-2 safety and effectiveness. rhBMP-2 is FDA approved for lumbar fusion with specific fusion devices. <h3>PURPOSE</h3> The purpose of the study was to investigate the complications associated with the use of rhBMP-2 for spinal fusion at a population-based level to determine its risk. <h3>STUDY DESIGN/SETTING</h3> The study design was a retrospective observational comparative cohort design comparing a target group to a comparator group for the risk of an outcome. <h3>PATIENT SAMPLE</h3> The patient sample comprised of two groups. Our target group consisted of first-time recipients of any spinal fusion procedure, of at least 18 years of age at the time of procedure, who received rhBMP-2 at the time of the procedure. Our comparator group consisted of first-time recipients of any spinal fusion procedure, who did not receive rhBMP-2. We restricted the study dates from 1/1/2003, when rhBMP-2 was introduced to the market, to 12/31/2017. <h3>OUTCOME MEASURES</h3> The outcome measures investigated included any reoperations requiring refusion and postoperative complications, which comprised of infection, seroma/hematoma, radiculitis, heterotopic ossification and cancer. The analysis of cancer included 18 subtypes of benign and malignant neoplasms. The observation period for these outcomes started after the date of surgery and concluded at the end of the study period. <h3>METHODS</h3> We utilized research tools developed in the Observational Health Data Sciences and Informatics (OHDSI) community (Hripcsak et al, 2015) to develop a common research protocol and data vocabulary encompassing the four claims databases and one electronic records database. The four insurance claims databases were IBM MarketScan Commercial Claims and Encounters (US employer-based private payer; patient aged 65 years or older), Optum ClinFormatics (US private payer; primarily aged 65 years or younger), IBM MarketScan Medicare Supplemental Beneficiaries (US retirees; patients aged > 65 years), IBM MarketScan Multi-state Medicaid (US Medicaid enrollees; all ages). The electronic health record database is Optum Pan-Therapeutic (US health systems; all ages). We then implemented a suite of methods and analyses to address confounding and bias inherent to observation studies, including propensity scoring and estimation of hazard ratios using a stratified Cox proportional hazard model. We combined the estimates from the data sources into a summary HR using a random effects model meta-analysis. To control for unmeasured confounding, we employed negative and positive controls to quantify the systemic bias in the system and compute adjusted HR estimates and confidence intervals. <h3>RESULTS</h3> Across all five databases, 60,427 patients were identified in the target group and 349,771 patients were identified in the comparator group, totaling 161,213 and 923,822 years of patient observation, respectively. rhBMP-2 was associated with statistically significantly fewer postoperative infections, with a hazard ratio of 0.88 (95% CI: 0.78-0.99). No significant difference was seen with the other outcomes, including reoperations for refusion, seroma/hematoma, radiculitis, heterotopic ossification, and development of benign or malignant neoplasms. Substantial control of measured confounding was demonstrated by propensity score adjustment, and negative and positive controls revealed noticeable residual bias that was controlled for through confidence interval calibration. <h3>CONCLUSIONS</h3> In the largest longitudinal observational study to date, rhBMP-2 was determined to be safe and effective as compared to non-rhBMP-2 usage in spinal fusion, with lower rate of postoperative infections. Importantly, no increased rates of cancer were detected. Coding error and the retrospective design may be an important source of bias. Despite these limitations, the large population size and statistical design give considerable significance to the results and conclusions of this study. <h3>FDA DEVICE/DRUG STATUS</h3> Medtronic Infuse (Approved for this indication).

Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale

BackgroundSince the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates.MethodsThe project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM.Expected resultsThis study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini (“data node”), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda.DiscussionThe project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.