Preliminary exploration of survival analysis using the OHDSI common data model: a case study of intrahepatic cholangiocarcinoma

Abstract

BackgroundData heterogeneity is a common phenomenon related to the secondary use of electronic health records (EHR) data from different sources. The Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM) organizes healthcare data into standard data structures using concepts that are explicitly and formally specified through standard vocabularies, thereby facilitating large-scale analysis. The objective of this study is to design, develop, and evaluate generic survival analysis routines built using the OHDSI CDM.MethodsWe used intrahepatic cholangiocarcinoma (ICC) patient data to implement CDM-based survival analysis methods. Our methods comprise the following modules: 1) Mapping local terms to standard OHDSI concepts. The analytical expression of variables and values related to demographic characteristics, medical history, smoking status, laboratory results, and tumor feature data. These data were mapped to standard OHDSI concepts through a manual analysis; 2) Loading patient data into the CDM using the concept mappings; 3) Developing an R interface that supports the portable survival analysis on top of OHDSI CDM, and comparing the CDM-based analysis results with those using traditional statistical analysis methods.ResultsOur dataset contained 346 patients diagnosed with ICC. The collected clinical data contains 115 variables, of which 75 variables were mapped to the OHDSI concepts. These concepts mainly belong to four domains: condition, observation, measurement, and procedure. The corresponding standard concepts are scattered in six vocabularies: ICD10CM, ICD10PCS, SNOMED, LOINC, NDFRT, and READ. We loaded a total of 25,950 patient data records into the OHDSI CDM database. However, 40 variables failed to map to the OHDSI CDM as they mostly belong to imaging data and pathological data.ConclusionsOur study demonstrates that conducting survival analysis using the OHDSI CDM is feasible and can produce reusable analysis routines. However, challenges to be overcome include 1) semantic loss caused by inaccurate mapping and value normalization; 2) incomplete OHDSI vocabularies describing imaging data, pathological data, and modular data representation.

Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership's Common Data Model: a pilot feasibility study

“…For example, a study assessing anti-seizure drug-related adverse reactions in 1344 target epilepsy cohorts determined that the detection rate of the adverse drug reaction based on CDM-format data was comparable to previously published results obtained using traditional data analysis techniques 21 . In addition, it is possible to implement various designs of research by constructing a target cohort corresponding to a study entry population and an outcome cohort corresponding to a disease outcome population 25 , 26 . Examples include a prognostic model validation study predicting hemorrhagic transformation of acute ischemic stroke within a CDM dataset of more than 600,000 patients via the OHDSI international network 25 , and a survival analysis study using 115 variables in 346 patients diagnosed with intrahepatic cholangiocarcinoma 26 .…”

Feasibility of Using Common Data Model for Orthopedic Research: Analysis of Risk Factors for Periprosthetic Joint Infection after Total Joint Arthroplasty

“…The algorithm that EMR data converted to CDM is as follows; mapping EMR to standard concepts, extraction-transformation-loading (ETL) of patient data into CDM, and evaluation of the CDM-based results [11] (Fig. 2).…”

Feasibility of Using Common Data Model for Orthopedic Research: Analysis of Risk Factors for Periprosthetic Joint Infection after Total Joint Arthroplasty

“…The algorithm that EMR data converted to CDM is as follows; mapping EMR to standard concepts, extractiontransformation-loading (ETL) of patient data into CDM, and evaluation of the CDM-based results [11] (Fig. 2).…”

“…The CDM is designed to include all observational data derived from the EMR to support the generation of reliable evidence [11,25]. It is important to obtain what we want from the study by properly designing the algorithm with the parameters currently available in the CDM.…”

“…Creating mappings the variable EMR data into the target CDM concepts is also crucial to improve patient data standardization [14,22]. Thus, in previous studies, cohort studies have been mainly focused on the pharmacoepidemiological research as treatment of diseases and epidemiological analysis of deaths from certain diseases [11,12,19,23,32,33]. In our case, we focused on parameters related to risk factors for PJI after TJA and constructed the algorithm directly using SQL, not through programs already created within the CDM, to achieve the desired results in our study.…”