Abstract
BackgroundAccurate and rapid clinical decisions based on real-world evidence are essential for patients with cancer. However, the complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases.ObjectiveThe aim of this study is to compare the anticancer treatment trajectories and patterns of clinical events according to regimen type using the chemotherapy episodes determined by an algorithm.MethodsWe developed an algorithm to extract the regimen-level abstracted chemotherapy episodes from medication records in a conventional Observational Medical Outcomes Partnership (OMOP) common data model (CDM) database. The algorithm was validated on the Ajou University School Of Medicine (AUSOM) database by manual review of clinical notes. Using the algorithm, we extracted episodes of chemotherapy from patients in the EHR database and the claims database. We also developed an application software for visualizing the chemotherapy treatment patterns based on the treatment episodes in the OMOP-CDM database. Using this software, we generated the trends in the types of regimen used in the institutions, the patterns of the iterative chemotherapy use, and the trajectories of cancer treatment in two EHR-based OMOP-CDM databases. As a pilot study, the time of onset of chemotherapy-induced neutropenia according to regimen was measured using the AUSOM database. The anticancer treatment trajectories for patients with COVID-19 were also visualized based on the nationwide claims database.ResultsWe generated 178,360 treatment episodes for patients with colorectal, breast, and lung cancer for 85 different regimens. The algorithm precisely identified the type of chemotherapy regimen in 400 patients (average positive predictive value >98%). The trends in the use of routine clinical chemotherapy regimens from 2008-2018 were identified for 8236 patients. For a total of 12 regimens (those administered to the largest proportion of patients), the number of repeated treatments was concordant with the protocols for standard chemotherapy regimens for certain cases. In addition, the anticancer treatment trajectories for 8315 patients were shown, including 62 patients with COVID-19. A comparative analysis of neutropenia showed that its onset in colorectal cancer regimens tended to cluster between days 9-15, whereas it tended to cluster between days 2-8 for certain regimens for breast cancer or lung cancer.ConclusionsWe propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the OMOP-CDM databases. These proof-of-concept studies demonstrated the usability, scalability, and interoperability of the proposed framework through a distributed research network.
Highlights
BackgroundIn cancer research, real-world data, with the exception of cancer registries, have been relatively underused despite recent advances in information technology and the availability of data from electronic health records (EHRs) or administrative claims databases [1]
We propose a method for generating chemotherapy episodes for introduction into the oncology extension module of the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) databases
This study was composed of two main processes: (1) the development of an algorithm to identify anticancer treatment episodes from the OMOP-CDM database and (2) the analysis of the trends and trajectories in cancer treatment or clinical events based on the algorithm-derived episode records using the visualization software
Summary
BackgroundIn cancer research, real-world data, with the exception of cancer registries, have been relatively underused despite recent advances in information technology and the availability of data from electronic health records (EHRs) or administrative claims databases [1]. One of the major challenges to the active use of EHRs or claims databases in cancer research is the limited availability of clinically relevant structured data elements. Researchers developed algorithms to replace the manual endeavor of capturing the details of chemotherapy from medication histories [5,6,7,8]. Even though these studies carved paths toward the use of real-world evidence in cancer research, none of them focused on identifying and addressing organizational barriers. The complexity of chemotherapy regimens for cancer impedes retrospective research that uses observational health databases
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