Abstract
Background: Although medical costs need to be controlled, there are no easily applicable cost prediction models of transfer to palliative care (PC) for terminal cancer patients. Construct a cost-saving prediction model based on terminal cancer patients’ data at hospital admission. Study design: Retrospective cohort study. Setting: A Japanese general hospital. Patients: A total of 139 stage IV cancer patients transferred to PC, who died during hospitalization from April 2014 to March 2019. Main outcome measure: Patients were divided into higher (59) and lower (80) total medical costs per day after transfer to PC. We compared demographics, cancer type, medical history, and laboratory results between the groups. Stepwise logistic regression analysis was used for model development and area under the curve (AUC) calculation. Results: A cost-saving prediction model (AUC = 0.78, 95% CI: 0.70, 0.85) with a total score of 13 points was constructed as follows: 2 points each for age ≤ 74 years, creatinine ≥ 0.68 mg/dL, and lactate dehydrogenase ≤ 188 IU/L; 3 points for hemoglobin ≤ 8.8 g/dL; and 4 points for potassium ≤ 3.3 mEq/L. Conclusion: Our model contains five predictors easily available in clinical settings and exhibited good predictive ability.