Grant Number:	1R01CA299956-01 Interpret this number
Primary Investigator:	Temel, Jennifer
Organization:	Massachusetts General Hospital
Project Title:	Randomized Trial of a Targeted Palliative Care Intervention for Patients with Metastatic Breast Cancer
Fiscal Year:	2025

Project Summary/Abstract End-of-life care (EOL) for patients with metastatic breast cancer (MBC) is markedly inadequate, as evidenced by this population’s low rates of hospice service use and increased likelihood of receiving chemotherapy and dying in the hospital at the end of life compared to those with other types of solid tumors. Studies show that integrating palliative and oncology care for patients with serious cancers improves patient-clinician communica- tion about EOL care preferences and subsequent delivery of EOL care. However, for those with MBC, many of whom have long disease trajectories, integrating outpatient palliative care from the time of diagnosis is neither scalable nor clinically indicated. Furthermore, prospective studies evaluating interventions to improve EOL out- comes specifically for patients with MBC are lacking. To address this evidence gap, we developed and success- fully pilot-tested a five-session palliative care intervention called TARGET-PC, specifically for patients with MBC who had clinical indicators of poor prognosis per manual chart review. TARGET-PC significantly improved EOL care communication and delivery in this population, though identifying the subgroup of patients with MBC at risk of death within 12 months remains a challenge. For the next step in this program of research, we now propose to utilize the Epic End-of-Life Care Index (EOLCI), a validated and scalable regression-based prediction model, to prospectively identify this vulnerable cohort and demonstrate the efficacy of TARGET-PC in a large-scale multi-site trial for patients with poor-prognosis MBC and their family caregivers. The aims of the study are to improve the documentation of patients’ EOL care preferences, delivery of EOL care, and participant-reported outcome measures. Using the Epic EOLCI, we will identify and enroll 400 patients with MBC at risk of death within 12 months and their family caregivers across three academic cancer centers (i.e., Massachusetts General Hospital Cancer Center, Penn Abramson Cancer Center, and Duke Cancer Center). Participants will be ran- domly assigned to receive either the five-session TARGET-PC intervention or to an enhanced usual care control group that involves an electronic health record prompt sent to oncology clinicians encouraging them to discuss and document their patients’ EOL care preferences. Natural language processing methods will be used to query the electronic health record to collect data regarding discussion and documentation of patients’ EOL care pref- erences and hospice utilization. Participants will also complete self-report measures of quality of life, mood symptoms, coping strategies, and communication about EOL care preferences at baseline prior to randomization and then again at weeks 12, 24, 36, and 48. The proposed intervention trial not only aligns with national priorities to improve the quality of EOL care for patients with advanced cancer but also leverages advances in innovative technology (i.e., Epic EOLCI and natural language processing) to overcome prior limitations in identifying at-risk study cohorts and potential biases in assessing key EOL care outcomes via the electronic health record. This study will lay the groundwork for delivering scalable, timely, and personalized palliative care services for patients with long disease trajectories.





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