Abstract P4-02-27: Preliminary Attainability Assessment of Real-World Data for Answering Clinical Questions about Breast Cancer Brain Metastases

Abstract Background Brain metastases is one of breast cancer’s leading causes of death, and the incidence is increasing with advances in diagnosis and treatment techniques. Nevertheless, these patients have fewer opportunities to participate in prospective randomized clinical trials because of the design challenges of increasing target population heterogeneity or differentiating the definition of endpoints and how the endpoints are evaluated. Real-world data has the potential that reaches the information at a blinded area of RCT approaches. In recent years, observational studies using real-world data have gotten attention for their capability to enhance an understanding of hard-to-reach RCT approaches. However, the yet incomplete gold standard of the study protocol and the unpredictable ’hidden labor’ of secondary use of clinical data become barriers to clinical researchers. In the study, we demonstrate data attainability assessment focused on clinicians’ clinical unmet needs in breast cancer brain metastases (BCBM) as a preliminary step of the observational study for screening study feasibility on a certain data source. Methods A breast cancer registry based on the clinical data warehouse (CDW) of Samsung Medical Center has been used as the data source (N=45,219, up to 31 Dec 2021). A total of 5 clinical questions (CQ) were presented as the result of the interview of two breast cancer experts about BCBM for the study (Table 1). Additionally, we conducted an attainability assessment for the population size and core variables constrained by a clinical question and a survey of 7 breast cancer clinicians for evaluation of the recognized clinical significance and research methods’ suitability for each CQ. A working group was formed for data attainability tests withal a person with experience in clinical research for at least several years across interdisciplinary areas, including clinical expertise, medical informatics, and epidemiology. For the first step, we declare the operational definition for the study population and core variables in which the research question is inherent conceptually. And appropriate data fields and value sets are figured out from the data set. Data attainability is examined via RWD extraction on the population size and availability of core variables. Result The collected five CQs were mostly about the relationship between brain metastases, systemic metastases, and systemic treatments. Other CQs were related to the relationship between brain metastases incidence and the type of neoadjuvant treatment or patients’ described symptoms. Assessment of the importance of clinical questions aligned well, especially for the questions with higher scores. (r=- · 98) For CQ E, filtered out not to fit the SMC BCR dataset at the early stage of the data attainability screening process due to lack of explicit data field or semantically related value sets for one of the core variables, ‘neurological symptom.’ For CQ A, B, and D, we could confirm enough population size and dataset for conducting the study, but additional logical reconstruction of data elements had needed. For example, we used the ‘clinical subtype’ variable, which is provided from SMC BCR, as pre-processed feature variable using an expert-knowledge-based algorithm. For CQ C, we were able to obtain pertinent data for the study only with direct data field match and explicit code set definition. Conclusion To effectively use CDW, the variables have to be interpreted and defined in a clinically meaningful way with the cooperation of clinicians and data science experts. For observational studies based on RWD, understanding the data source contents and clarifying the research question enough to translate to the data level is essential. The step-by-step protocol presented in the study could be applied to the other clinical researcher using RWD at preliminary feasibility screening. Table 1. List of clinical questions from expert interviews Citation Format: Min-Jeong Kim, Hyo Jung Kim, Danbee Kang, Hee Kyung Ahn, Soo-Yong Shin, Seri Park, Juhee Cho, Yeon Hee Park. Preliminary Attainability Assessment of Real-World Data for Answering Clinical Questions about Breast Cancer Brain Metastases [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 P4-02-27.