Health Equity Achievement in Radiation Therapy (HEART) Score: A Social Prognosis

PURPOSE/OBJECTIVE(S)

The aim of this study was to develop a Health Equity Achievement in Radiation Therapy (HEART) score that can help identify patients at risk of experiencing suboptimal quality-of-care (QoC) early on in the patient-provider encounter and prior to initiation of treatment. Such a score may improve shared decision making to improve QoC.

MATERIALS/METHODS

A retrospective analysis was conducted using the National Cancer Database (NCDB) for prostate cancer cases between 2004-2017. Sociodemographic factors, clinical characteristics, and treatment information were collected. A composite HEART score was built to predict suboptimal QoC, defined as treatment refusal, incomplete treatment, or treatment delay. 70% of the data was allocated to training and 30% to validating a logistic regression model through which a nomogram was constructed.

RESULTS

A total of 1,599,785 patients were included in the analysis, of whom 126,917 (7.9%) had at least one suboptimal QoC. The strongest predictors were Black race, uninsured status, lower educational status, geographic location, and nodal disease (Table). The nomogram demonstrated a fair ability to predict quality metrics, with an area under the receiver operating characteristic curve (AUC) of 0.57 in the test group. The nomogram facilitated graphic interpretation of systemic factors in contributing to suboptimal QoC.

CONCLUSION

With observed potential for predicting suboptimal QoC outcomes in patients with prostate cancer by considering systemic barriers, this NCDB-based nomogram has potential utility as a tool for identifying patients who may benefit from additional social support, including the financial resources associated with these services, to improve access to care. Further validation in diverse datasets is needed to improve performance and generalizability to broader patient populations and different disease sites.

Operational Ontology for Radiation Oncology (OORO): A Professional Society-Based, Multi-Stakeholder Consensus Driven Informatics Standard Supporting Clinical and Research Use of Real-World Data

PURPOSE/OBJECTIVE(S)

There is a critical need for large-scale, multi-institutional "real-world" data to evaluate patient, diagnosis and treatment factors affecting oncology patient outcomes. However, lack of data standardization undermines the potential for automated learning from the vast amount of information routinely archived in electronic health records (EHRs), Radiation Oncology Information Systems and other cancer care databases. As next step to promote data standardization beyond the American Association of Physicists in Medicine (AAPM)'s TG-263 guidance for radiotherapy (RT) nomenclature, the AAPM's Big Data Subcommittee (BDSC) has led an international RT professional society collaboration to develop the Operational Ontology for Radiation Oncology (OORO).

MATERIALS/METHODS

Initiated July 2019 to explore issues that typically compromise formation of large inter- and intra- institutional databases from EHRs, the AAPM's BDSC membership includes representatives from the AAPM, American Society of Radiation Oncology (ASTRO), Canadian Organization of Medical Physicists (COMP), Canadian Association of Radiation Oncology (CARO), European Society of Therapeutic Radiation Oncology (ESTRO) and clinical trials experts from NRG Oncology. Multiple external stakeholders were engaged, including government agencies, vendors and RT community members through the iterative and consensus-driven approach to OORO development.

RESULTS

The OORO includes 42 key elements, 359 attributes, 144 value sets, and 155 relationships, ranked for priority of implementation based on clinical significance, likelihood of availability in EHRs, or ability to modify routine clinical processes to permit aggregation. The initial version of OORO includes many disease-site independent concepts common for all cancer patients and a smaller set specific for prostate cancer. The OORO development methodology is currently being applied/adapted to include additional disease site-specific concepts beginning with head and neck cancers.

CONCLUSION

The first of its kind in radiation oncology, the OORO is a professional society-based, multi-stakeholder, consensus driven informatics standard. The iterative and collaborative approach to ontology development and refinement aims to ensure that OORO serves as a « living » guidance document, facilitating incremental expansion of data elements over time, as disease site-specific standards are set and RT concepts evolve. Supporting construction of comprehensive "real-world" datasets and application of advanced analytic techniques, including artificial intelligence (AI), OORO holds the potential to revolutionize patient management and improve outcomes.