Predicting 30-day mortality with routine blood tests in patients undergoing palliative radiation therapy: A comparison of logistic regression and gradient boosting models.

Purpose: This study aimed to estimate the 30-day mortality (30D_M) and compare models for 30D_M prediction in patients undergoing palliative radiation therapy (RT).

Materials And Methods: Data from 3,756 patients who underwent palliative RT between 2018 and 2020 at two institutions were retrospectively reviewed. From one institution, 3,315 patients were randomly assigned to the training (N = 2,652) and internal validation (N = 663) cohorts. The remaining 441 patients from the other institution constituted the external validation cohort. Nineteen features, including seven blood test features, were extracted from medical records. For 30D_M prediction, 4 models were constructed: logistic regression comprising all features (LRM-A) and 7 blood test features (LRM-B) and gradient boosting using all features (GBM-A) and 7 blood test features (GBM-B).

Results: The 30D_M rates were 10.6 %, 11.2 %, and 17.5 % in the training, internal validation, and external validation cohorts, respectively. GBM-B demonstrated a good value for the area under the receiver operating characteristic curve (AUC) (0.830-0.863). Among the four models, GBM-A exhibited the highest AUC values, although GBM-B still generally outperformed LRM-A and LRM-B. The 30D_M rates significantly differed across the four prognostic groups according to the quantile values of predictive probability of GBM-B: 0-0.8 % (1st quantile), 1.2-3.4 % (2nd quantile), 8.7-12.9 % (3rd quantile), and 31.1-36.6 % (4th quantile), respectively.

Conclusions: The 30D_M rates were successfully stratified into distinct prognostic groups by using the GBM-B model. The model could serve as a straightforward and objective tool for predicting mortality in patients undergoing palliative RT.