Prognostic Factors and Survival Outcomes among Patients with Breast Cancer and Brain Metastases at Diagnosis: A National Cancer Database Analysis

Abstract

<b><i>Introduction:</i></b> The aim of this study was to assess for clinicopathologic and socioeconomic features that predict improved survival for patients with advanced breast cancer with synchronous brain metastases at diagnosis. <b><i>Methods:</i></b> We utilized the National Cancer Database (NCDB) to identify all patients with brain metastases present at diagnosis, with adequate information on receptor status (ER, PR, Her2), clinical T stage of cT1-4, clinical M1, with 3,943 patients available for analysis. The association between brain metastases patterns and patient/disease variables was examined by robust Poisson regression model. Cox proportional hazards model was used to quantify the associations between overall survival (OS) and these variables. <b><i>Results:</i></b> In univariable analysis, OS was significantly associated with the number of sites of metastases (<i>p</i> &#x3c; 0.0001). Patients with 2 or more additional extracranial sites of metastases had significantly worse OS (median 8.8 months, 95% confidence interval [CI] 7.8, 9.9) than patients with brain metastases only (median OS 10.6 months, 95% CI 9.4, 12.9) or brain metastases plus one other extracranial site of metastases (median OS 13.1 months, 95% CI 11.8, 14.4). Risk factors which predicted poor prognosis included triple-negative disease, high comorbidity score, poorly differentiated tumors, invasive lobular histology, multi-organ involvement of metastases, and government or lack of insurance. Factors which improve survival include younger age and Hispanic race. <b><i>Discussion/Conclusion:</i></b> Using a large NCDB, we identified various factors associated with prognosis for patients with brain metastases at the time of breast cancer diagnosis. Insurance status and related socioeconomic challenges provide potential areas for improvement in care for these patients. This information may help stratify patients into prognostic categories at the time of diagnosis to improve treatment plans.