Abstract
Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). However, current PSG data are scattered across numerous sleep laboratories and have different formats in the electronic health record (EHR). Hence, this study aimed to convert EHR PSG into a standardized data format—the Observational Medical Outcome Partnership (OMOP) common data model (CDM). We extracted the PSG data of a university hospital for the period from 2004 to 2019. We designed and implemented an extract–transform–load (ETL) process to transform PSG data into the OMOP CDM format and verified the data quality through expert evaluation. We converted the data of 11,797 sleep studies into CDM and added 632,841 measurements and 9,535 observations to the existing CDM database. Among 86 PSG parameters, 20 were mapped to CDM standard vocabulary and 66 could not be mapped; thus, new custom standard concepts were created. We validated the conversion and usefulness of PSG data through patient-level prediction analyses for the CDM data. We believe that this study represents the first CDM conversion of PSG. In the future, CDM transformation will enable network research in sleep medicine and will contribute to presenting more relevant clinical evidence.
Highlights
Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA)
The Observational Medical Outcome Partnership’s (OMOP) common data model (CDM), which is utilized by Observational Health Data Sciences and Informatics (OHDSI) as a standard data format, serves as a guide for the standardization of heterogeneous representations of healthcare data obtained from disparate sources
We considered all PSGs performed at the Sleep Center of Seoul National University Bundang Hospital (SNUBH) as target data to be converted into OMOP CDM, including full-night PSGs, split-night PSGs, PSGs for continuous positive airway pressure (CPAP) titration, and multiple sleep latency tests (MSLTs)
Summary
Well-defined large-volume polysomnographic (PSG) data can identify subgroups and predict outcomes of obstructive sleep apnea (OSA). This study aimed to convert EHR PSG into a standardized data format—the Observational Medical Outcome Partnership (OMOP) common data model (CDM). Conversion of health and medical databases into the CDM format is expected to enable interdisciplinary collaborative large-scale analyses Such large-scale analyses using open-source analytic tools based on standardized datasets are, in turn, expected to improve the speed and efficiency of population-level estimation and patient-level prediction, thereby enhancing the reliability of clinical decision-making[11,12]. Linking the diverse data obtained from PSG with the extensive EHR database in a structured CDM format is expected to facilitate multi-center studies and strengthen general analytic power. We aimed to convert EHR PSG data into the standardized OMOP CDM data format and conduct a pilot feasibility test. Through a pilot feasibility study, we attempted to confirm the possibility of developing a predictive model using existing CDM data and additional PSG data, and to verify the usefulness of the integrated data
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