ABSTRACT
BACKGROUND: Acute myocardial infarction (AMI) is an uncommon but fatal complication among patients undergoing elective spinal fusion surgery (SF), total hip arthroplasty (THA), and total knee arthroplasty (TKA). Our objective was to estimate the incidence of AMI among adults undergoing elective SF, THA, and TKA in different post-operative risk windows and characterize high-risk sub-populations in the United States. METHODS: A retrospective cohort study was conducted using data from a longitudinal electronic healthcare record (EHR) database from January 1, 2007 to June 30, 2018. ICD codes were used to identify SF, THA, TKA, AMI, and selected clinical characteristics. Incidence proportions (IPs) and 95% confidence intervals were estimated in the following risk windows: index hospitalization, ≤ 30, ≤ 90, ≤ 180, and ≤ 365 days post-operation. RESULTS: A total of 67,533 SF patients, 87,572 THA patients, and 167,480 TKA patients were eligible for the study. The IP of AMI after SF, THA, and TKA ranged from 0.36, 0.28, and 0.25% during index hospitalization to 1.05, 0.93, and 0.85% ≤ 365 days post-operation, respectively. The IP of AMI was higher among patients who were older, male, with longer hospital stays, had a history of AMI, and had a history of diabetes. CONCLUSION: The IP of post-operative AMI was generally highest among the SF cohort compared to the THA and TKA cohorts. Additionally, potential high-risk populations were identified. Future studies in this area are warranted to confirm these findings via improved confounder control and to identify effect measure modifiers.
ABSTRACT
BACKGROUND: There has been increased interest in using multiple observational databases to understand the safety profile of medical products during the postmarketing period. However, it is challenging to perform analyses across these heterogeneous data sources. The Observational Medical Outcome Partnership (OMOP) provides a Common Data Model (CDM) for organizing and standardizing databases. OMOP's work with the CDM has primarily focused on US databases. As a participant in the OMOP Extended Consortium, we implemented the OMOP CDM on the UK Electronic Healthcare Record database-The Health Improvement Network (THIN). OBJECTIVE: The aim of the study was to evaluate the implementation of the THIN database in the OMOP CDM and explore its use for active drug safety surveillance. METHODS: Following the OMOP CDM specification, the raw THIN database was mapped into a CDM THIN database. Ten Drugs of Interest (DOI) and nine Health Outcomes of Interest (HOI), defined and focused by the OMOP, were created using the CDM THIN database. Quantitative comparison of raw THIN to CDM THIN was performed by execution and analysis of OMOP standardized reports and additional analyses. The practical value of CDM THIN for drug safety and pharmacoepidemiological research was assessed by implementing three analysis methods: Proportional Reporting Ratio (PRR), Univariate Self-Case Control Series (USCCS) and High-Dimensional Propensity Score (HDPS). A published study using raw THIN data was selected to examine the external validity of CDM THIN. RESULTS: Overall demographic characteristics were the same in both databases. Mapping medical and drug codes into the OMOP terminology dictionary was incomplete: 25 % medical codes and 55 % drug codes in raw THIN were not listed in the OMOP terminology dictionary, representing 6 % condition occurrence counts, 4 % procedure occurrence counts and 7 % drug exposure counts in raw THIN. Seven DOIs had <0.3 % and three DOIs had 1 % of unmapped drug exposure counts; each HOI had at least one definition with no or minimal (≤0.2 %) issues with unmapped condition occurrence counts, except for the upper gastrointestinal (UGI) ulcer hospitalization cohort. The application of PRR, USCCS and HDPS found, respectively, a sensitivity of 67, 78 and 50 %, and a specificity of 68, 59 and 76 %, suggesting that safety issues defined as known by the OMOP could be identified in CDM THIN, with imperfect performance. Similar PRR scores were produced using both CDM THIN and raw THIN, while the execution time was twice as fast on CDM THIN. There was close replication of demographic distribution, death rate and prescription pattern and trend in the published study population and the cohort of CDM THIN. CONCLUSIONS: This research demonstrated that information loss due to incomplete mapping of medical and drug codes as well as data structure in the current CDM THIN limits its use for all possible epidemiological evaluation studies. Current HOIs and DOIs predefined by the OMOP were constructed with minimal loss of information and can be used for active surveillance methodological research. The OMOP CDM THIN can be a valuable tool for multiple aspects of pharmacoepidemiological research when the unique features of UK Electronic Health Records are incorporated in the OMOP library.
