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Generalizability
OHDSI Study Protocol: OHDSI_Study_Protocol_v1.0
This study aims to evaluate and characterize the generalizability or coverage of the OMOP vocabulary terms included in the OMOP2OBO mapping set to OMOP vocabulary terms utilized in the Observational Health Data Sciences and Informatics (OHDSI) Concept Prevalence study sites.
As described here, the Concept Prevalence study was designed to provide researchers with additional context regarding the frequency at which different clinical codes occur across the OHDSI research network:
We want to study the usage patterns of Concepts across different OMOP CDM instances. This in itself could be useful information to answer many questions, but we have a concrete reason: For any one medical entity, the granularity of codes captured in a data source can vary greatly. For example, Chronic Kidney Disorder stage II can be coded as ICD9 code 585.2 Chronic kidney disease, Stage II (mild); 585.9 Chronic kidney disease, unspecified or even as 586 Renal failure, unspecified. However, this information is key for any cohort definition. Currently, researchers have no way of knowing whether a certain concept with high granularity is even available for selection, or whether they have to use a generic concept in combination with some auxiliary information to define the cohort correctly. Each data source instance is a black box and knowledge about the distribution of the concepts is limited to the very instance researchers have access to. But OHDSI Network Studies are dependent on cohort definitions that work across the network.
The main research question is how does the coverage of the OMOP vocabulary terms present in the OMOP2OBO mappings differ across the OHDSI Concept Prevalence study sites?
The specific aims of this study are as follows:
- Examine
OMOP2OBOcoverage across the Concept Prevalence sites by identifying:- OMOP vocabulary terms that exist in OMOP2OBO and one or more site.
- OMOP vocabulary terms only present in OMOP2OBO and none of the Concept Prevalence sites
- OMOP vocabulary terms only present in one or more the site.
- Demonstrate the potential for [molecular] biological inference of OMOP2OBO by characterizing differences in OBO ontology term enrichment across the Concept Prevalence sites when varying different aspects of data provenance (e.g. site type, clinical specialty, and site location).
In addition to the Concept Prevalence study sites (n=22), data was obtained from two independent academic medical centers. High-level descriptions of each site, including the total number of records and concepts are provided below.
| Database | Type | Location | Record Count | Concept Count |
|---|---|---|---|---|
| Ajou University Database (Ajou) | EHR | Non-US | 30,238,709 | 6,055 |
| Australian Electronic practice based research network (AU-ePBRN) | EHR | Non-US | 11,658,378 | 5,027 |
| Columbia University Medical Center Database (CUMC) | EHR | US | 938,078,465 | 21,502 |
| IBM MarketScan Commercial Database (CCAE) | CLAIMS | US | 12,649,562,658 | 31,570 |
| IBM MarketScan Medicare Supplemental Database (MDCR) | CLAIMS | US | 2,770,787,154 | 25,121 |
| IBM MarketScan Multi-State Medicaid Database (MDCD) | CLAIMS | US | 4,283,172,117 | 19,133 |
| IQVIA Disease Analyzer (DA) France | EHR | Non-US | 39,632,134 | 3,423 |
| IQVIA Disease Analyzer (DA) Germany | EHR | Non-US | 851,853,377 | 9,276 |
| IQVIA Longitudinal Patient Data (LPD) Australia | EHR | Non-US | 56,940,803 | 5,833 |
| IQVIA US Ambulatory EMR (AmbEMR) | EHR | US | 10,634,058,375 | 62,161 |
| IQVIA US Hospital Charge Data Master (CDM) | EHR | US | 4,857,228,360 | 19,352 |
| IQVIA US LRxDx Open Claims (Open Claims) | CLAIMS | US | 71,678,847,042 | 20,083 |
| Japan Medical Data Center database (JMDC) | EHR | Non-US | 1,184,325,523 | 6,833 |
| Korea National Health Insurance Service / National Sample Cohort (NHIS/NSC Korea) | CLAIMS | Non-US | 323,096,899 | 6,667 |
| Medical Information Mart for Intensive Care III (MIMIC3) | EHR | US | 124,127,038 | 3,781 |
| Optum De-Identified Clinformatics Data-Mart-Database— Socio-Economic Status (SES) | CLAIMS | US | 13,369,194,028 | 36,943 |
| Optum De-Identified Clinformatics Data-Mart-Database—Date of Death (DOD) | CLAIMS | US | 9,716,879,363 | 34,853 |
| Optum De-identified Electronic Health Record Dataset (PANTHER) | EHR | US | 27,894,204,112 | 59,777 |
| Premier Healthcare Database (PREMIER) | CLAIMS | US | 16,794,698,039 | 18,903 |
| Stanford Medicine Research Data Repository (STaRR) | EHR | US | 416,175,821 | 11,161 |
| The Healthcare Cost and Utilization ProjectNationwide Inpatient Sample (HCUP) | EHR | US | 744,807,853 | 9,391 |
| Tufts Medical Center Database (Tufts) | EHR | US | 66,863,985 | 21,118 |
| UCHealth | EHR | US | 1,215,613,326 | 19,073 |
| USC PScanner | EHR | US | 29,703,213 | 11,476 |
For each data site, standard concepts used at least once in practice were obtained from the Condition Occurrence (i.e. SNOMED-CT), Drug Exposure (i.e. ingredient-level; RxNorm), and Measurement (i.e. LOINC) tables. For all concepts, the total frequency was obtained and consistent with the Concept Prevalence study, all concepts occurring fewer than 10 times were ignored and all remaining concepts occurring fewer than 100 times were assigned a count of 100.
Results are presented below by clinical domain. Overall, the OMOP vocabulary terms included in the OMOP2OBO mapping set provided exceptional coverage, which differed both by Concept Prevalence study site and clinical domain.