Abstract P3-03-16: A methodology for using real-world data from electronic health records to assess chemotherapy administration in women with breast cancer

Abstract

Abstract Introduction Chemotherapy administration in real-world cancer care can differ extensively from clinical trials. It is important to understand real-world practice to identify dose reductions, delays, regimen changes and early discontinuations that impact cancer outcomes. Such variables require knowledge of intended regimens, which may not be well-documented in structured data in electronic health records (EHRs). We examined EHR data from the Kaiser Permanente Northern California (KPNC) site of the Optimal Breast Cancer Chemotherapy Dosing (OBCD) study to develop a process to identify each patient’s intended regimen. Methods In this study of women diagnosed and treated with primary stage I-IIIA breast cancer at KPNC from 2006-2019, and ages 18+y at diagnosis, we analyzed treatment patterns using structured EHR data on the drugs, dosages, and dates at which they were administered (from which intervals and total length can be derived). Chemotherapy agents were identified using the NCI’s CANMED database augmented with other sources. We used these data to categorize patients into the 22 drug combinations described in the National Cancer Care Network (NCCN) guidelines for breast cancer treatment. Within these 22 drug combinations, women were then subcategorized into 45 distinct chemotherapy administration schedules, defined as NCCN guideline regimens (NGRs). For this step, algorithms were developed that categorized patients into NGRs if they received the exact regimen described in the guidelines. For the second step, we conducted a manual review of the EHR data for patients who were unable to be categorized. This enabled us to gradually loosen the criteria (in terms of cycle intervals or number of cycles) so patients whose chemotherapy administration aligned closely with NGRs were categorized into each of the 45 NGRs. Clear patterns emerged of regimens that were administered to multiple patients, despite being outside of the NCCN guidelines, which we have defined as non-standard NGRs. For example, in the drug combination TC (cyclophosphamide and docetaxel) the NGR was TC every 21 days for 4 cycles. We found approximately 1 in 10 patients received 6 cycles, which we defined as a non-NGR. For the remaining uncategorized patients, medical chart abstraction was undertaken as a third step, at which point patients were categorized into either existing regimens or new non-NGRs if their intended regimen had not been previously described in the guidelines. Results Among 31,418 women with breast cancer, 12,427 (39.6%) received chemotherapy. We determined the intended chemotherapy regimens for 6,559 (52.8%) receiving the 45 NGRs using EHR data. We further expanded the algorithms through a manual review of the EHR data, which enabled us to categorize 2,977 (24.0%) additional women into their intended regimens. Abstracted medical notes were reviewed for the remaining patients for whom we had not been able to identify the intended regimen. Across both the manual review and abstraction processes, we were able to identify additional non-standard NGR regimens. In total, 9,536 (76.7%) of women were categorized into their intended regimen through the algorithm/manual review process, while 2891 (23.3%) of women underwent medical chart abstraction to identify the intended regimen. Conclusion Here, we describe the challenges and approaches to operationalize complex, real-world data to identify intended chemotherapy regimens at a granularity and scale not seen previously. We are adapting this method at a second OBCD study site, Kaiser Permanente Washington, where all women have undergone medical chart abstraction. We hope this methodology leads to increased feasibility and efficiency of use of large-scale clinical data, in turn improving cancer care delivery, patient outcome evaluation, and other real-world questions. Citation Format: Jenna Bhimani, Kelli O’Connell, Rachael P. Burganowski, Isaac J. Ergas, Marilyn J. Foley, Grace B. Gallagher, Jennifer J. Griggs, Narre Heon, Tatjana Kolevska, Yuriy Kotsurovskyy, Candyce H. Kroenke, Kanichi G. Nakata, Sonia Persaud, Donna R. Rivera, Janise M. Roh, Sara Tabatabai, Emily Valice, Erin J. Bowles, Elisa V. Bandera, Lawrence H. Kushi, Elizabeth D. Kantor. A methodology for using real-world data from electronic health records to assess chemotherapy administration in women with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-03-16.