An Easy-to-Use Prognostic Model for Survival Estimation for Patients with Symptomatic Long Bone Metastases

Background: A survival estimation for patients with symptomatic long bone metastases (LBM) is crucial to prevent overtreatment and undertreatment. This study analyzed prognostic factors for overall survival and developed a simple, easy-to-use prognostic model. Methods: A multicenter retrospective study of 1,520 patients treated for symptomatic LBM between 2000 and 2013 at the radiation therapy and/or orthopaedic departments was performed. Primary tumors were categorized into 3 clinical profiles (favorable, moderate, or unfavorable) according to an existing classification system. Associations between prognostic variables and overall survival were investigated using the Kaplan-Meier method and multivariate Cox regression models. The discriminatory ability of the developed model was assessed with the Harrell C-statistic. The observed and expected survival for each survival category were compared on the basis of an external cohort. Results: Median overall survival was 7.4 months (95% confidence interval [CI], 6.7 to 8.1 months). On the basis of the independent prognostic factors, namely the clinical profile, Karnofsky Performance Score, and presence of visceral and/or brain metastases, 12 prognostic categories were created. The Harrell C-statistic was 0.70. A flowchart was developed to easily stratify patients. Using cutoff points for clinical decision-making, the 12 categories were narrowed down to 4 categories with clinical consequences. Median survival was 21.9 months (95% CI, 18.7 to 25.1 months), 10.5 months (95% CI, 7.9 to 13.1 months), 4.6 months (95% CI, 3.9 to 5.3 months), and 2.2 months (95% CI, 1.8 to 2.6 months) for the 4 categories. Conclusions: This study presents a model to easily stratify patients with symptomatic LBM according to their expected survival. The simplicity and clarity of the model facilitate and encourage its use in the routine care of patients with LBM, to provide the most appropriate treatment for each individual patient.