A 2-week prognostic prediction model for terminal cancer patients in a palliative care unit at a Japanese general hospital

Abstract

OBJECTIVE: We aimed to develop a prognostic prediction model for 2-week survival among patients with terminal cancer in a palliative care unit (PCU). METHODS: A prospective cohort study was conducted on terminal cancer patients in the PCU for 11 months at a general hospital in Tokyo, Japan. We collected data regarding demographics, treatment history, performance status, symptoms, and laboratory results. Patients who survived more than 2 weeks were labeled 'long survivors' and those who died within 2 weeks were grouped as 'short survivors'. Stepwise logistic regression model was constructed for the model development and bootstrapping was used for the internal model validation. RESULTS: In 158 subjects whose data were available for the analysis, 109 (69%) subjects were categorized as long survivors and 49 (31%) subjects as short survivors. A prognostic prediction model with a total score of 8 points was constructed as follows: 2 points each for anorexia, dyspnea, and edema; 1 point each for blood urea nitrogen >25 mg/dl and platelets <260,000/mm(3). Area under the receiver operating characteristic (ROC) curve of this model was 83.2% (95% CI: 75.3-91.0%). Bootstrapped validation beta coefficients of the predictors were similar to the original cohort beta coefficients. CONCLUSION: Our prognostic prediction model for estimating 14-day survival for patients with terminal cancer on the PCU ward included five clinical predictors that are readily available in the clinical setting and showed a relatively high accuracy. External validation is needed to confirm the model's generalizability.