Background Several type B adverse drug reactions (ADRs), especially severe cutaneous adverse reactions (SCARs), are associated with particular human leukocyte antigen (HLA) genotypes. However, pre‐stored HLA information obtained from other clinical workups has not been used to prevent ADRs. We aimed to simulate the preemptive use of pre‐stored HLA information in electronic medical records to evaluate whether this information can prevent ADRs. Methods We analyzed the incidence and the risk of ADRs for selected HLA alleles (HLA‐B*57:01, HLA‐B*58:01, HLA‐A*31:01, HLA‐B*15:02, HLA‐B*15:11, HLA‐B*13:01, HLA‐B*59:01, and HLA‐A*32:01) and seven drugs (abacavir, allopurinol, carbamazepine, oxcarbazepine, dapsone, methazolamide, and vancomycin) using pre‐stored HLA information of transplant patients based on the Pharmacogenomics Knowledge Base guidelines and experts' consensus. Results Among 11,988 HLA‐tested transplant patients, 4092 (34.1%) had high‐risk HLA alleles, 4583 (38.2%) were prescribed risk drugs, and 580 (4.8%) experienced type B ADRs. Patients with HLA‐B*58:01 had a significantly higher incidence of type B ADR and SCARs associated with allopurinol use than that of patients without HLA‐B*58:01 (17.2% vs. 11.9%, odds ratio [OR] 1.53 [95% confidence interval {CI} 1.09–2.13], p = 0.001, 2.3% versus 0.3%, OR 7.13 [95% CI 2.19–22.69], p < 0.001). Higher risks of type B ADR and SCARs were observed in patients taking carbamazepine or oxcarbazepine if they had one of HLA‐A*31:01, HLA‐B*15:02, or HLA‐B*15:11 alleles. Vancomycin and dapsone use in HLA‐A*32:01 and HLA‐B*13:01 carriers, respectively, showed trends toward increased risk of type B ADRs. Conclusion Utilization of pre‐stored HLA data can prevent type B ADRs including SCARs by screening high‐risk patients.

Background In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. Objective We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. Methods The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution’s anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. Results Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. Conclusions The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

BACKGROUND In recent decades, real-world evidence (RWE) in oncology has rapidly gained traction for its potential to answer clinical questions that cannot be directly addressed by randomized clinical trials. Integrating real-world data (RWD) into clinical research promises to contribute to more sustainable research designs, including extension, augmentation, enrichment, and pragmatic designs. Nevertheless, clinical research using RWD is still limited because of concerns regarding the shortage of best practices for extracting, harmonizing, and analyzing RWD. In particular, pragmatic screening methods to determine whether the content of a data source is sufficient to answer the research questions before conducting the research with RWD have not yet been established. OBJECTIVE We examined the PAR (Preliminary Attainability Assessment of Real-World Data) framework and assessed its utility in breast cancer brain metastasis (BCBM), which has an unmet medical need for data attainability screening at the preliminary step of observational studies that use RWD. METHODS The PAR framework was proposed to assess data attainability from a particular data source during the early research process. The PAR framework has four sequential stages, starting with clinical question clarification: (1) operational definition of variables, (2) data matching (structural/semantic), (3) data screening and extraction, and (4) data attainability diagramming. We identified 5 clinical questions to be used for PAR framework evaluation through interviews and validated them with a survey of breast cancer experts. We used the Samsung Medical Center Breast Cancer Registry, a hospital-based real-time registry implemented in March 2021, leveraging the institution’s anonymized and deidentified clinical data warehouse platform. The number of breast cancer patients in the registry was 45,129; it covered the period from June 1995 to December 2021. The registry consists of 24 base data marts that represent disease-specific breast cancer characteristics and care pathways. The outcomes included screening results of the clinical questions via the PAR framework and a procedural diagram of data attainability for each research question. RESULTS Data attainability was tested for study feasibility according to the PAR framework with 5 clinical questions for BCBM. We obtained data sets that were sufficient to conduct studies with 4 of 5 clinical questions. The research questions stratified into 3 types when we developed data fields for clearly defined research variables. In the first, only 1 question could be answered using direct data variables. In the second, the other 3 questions required surrogate definitions that combined data variables. In the third, the question turned out to be not feasible for conducting further analysis. CONCLUSIONS The adoption of the PAR framework was associated with more efficient preliminary clinical research using RWD from BCBM. Furthermore, this framework helped accelerate RWE generation through clinical research by enhancing transparency and reproducibility and lowering the entry barrier for clinical researchers.

Background: Pretreatment endocrine symptoms in premenopausal patients might be considered as a potential marker of poor prognosis. We conducted a cohort study to evaluate the association between endocrine symptoms prior to treatment and recurrence-free survival (RFS) among premenopausal patients with breast cancer aged ⩽40 years. Methods: Data were obtained from a prospective cohort study (NCT03131089) conducted at the Samsung Medical Center from 2013 to 2021. We included patients aged ⩽40 years who had been diagnosed with breast cancer. The primary outcome measure was RFS. Endocrine symptoms were measured using the Functional Assessment of Cancer Therapy – Endocrine Symptoms (FACT-ES). We also calculated the hazard ratio (HR) for recurrence or all-cause mortality by comparing the tertiles of the FACT-ES score at diagnosis. Results: Among the 977 participants, the mean (standard deviation) age was 35.3 (3.9) years. At diagnosis, 17.2% of the patients had at least one severe endocrine symptom. During 3512 person-years of follow-up, the high symptom group had a worse RFS than the low-symptom group [HR = 2.05; 95% confidence interval (CI) = 1.19–3.54]. In particular, hot flashes (HR = 5.59; 95% CI = 1.96–15.93) and breast sensitivity (HR = 1.82; 95% CI = 1.00–3.32) were associated with reduced RFS. Conclusion: Close monitoring of pretreatment endocrine symptoms may be important in patients diagnosed with breast cancer at a young age.