A New Prognostic Model For Predicting 30-Day Mortality In Acute Oncology Patients

Introduction Acute oncology services (AOS) provide rapid review and expedited pathways for referral to specialist care for cancer patients. Blood tests may support AOS in providing estimates of prognosis. We aimed to develop and validate a prognostic model of 30-day mortality based on routine blood markers to inform an AOS decision to actively treat or palliate patients. Methods and Materials Using clinical data from 752 AOS referrals, multivariable logistic regression analysis was conducted to develop a 30-day mortality prognostic model. Internal validation and then internal-external cross-validation were used to examine overfitting and generalizability of the model's predictive performance. Results Urea, alkaline phosphatase, albumin and neutrophils were the strongest predictors of outcome. The model separated patients into distinct prognostic groups from the cross-validation (C Statistic: 0.70; 95% CI: 0.64-0.76). Admission year was included as a predictor in the model to improve the model calibration. Conclusion The developed prediction model was able to classify patients into distinct prognostic risk groups, which is clinically useful for delivering an evidence-based AOS. Collation of data from other AOS centers would allow for the development of a more generalizable prognostic model.