Prediction Model for Intensive Care Unit Mortality in Patients with Spine Fracture: Can Machine Learning be Used to Improve Current Standards?

backgroundThe APACHE-IV is the gold standard for ICU mortality prediction across a multitude of patient conditions. We aim to create a machine learning model to predict ICU mortality for patients with spine fracture with similar predictive capabilities as the APACHE-IV, but with fewer inputs. STUDY

designThe MIMIC-IV and the eICU databases were queried for patients admitted to the ICU with a spine fracture. Demographics and physiological variables of each patient's first day of ICU admission were collected. Variables with > 10% missing values were excluded, while others were imputed using K-nearest-neighbors (k = 5). Feature selection using MIMIC-IV was performed by training an initial Extreme Gradient Boosting (XGBoost) model. Eight features were selected using gain: systolic blood pressure, fiO

resultsA total of 926 patients with spine fracture in MIMIC-IV were included, with a mortality rate of 6.6%. A total of 571 patients with spinal fracture in the eICU database were included, with a mortality rate of 6.7%. Our trained XGBoost model achieved an external test area under the receiver operating curve (AUROC) of 0.84 (95% CI: 0.77-0.90), area under the precision-recall curve (AUPRC) of 0.22 (95% CI: 0.10-0.36), and Brier score of 0.041, compared with the APACHE-IV model of 0.85 (95% CI: 0.78-0.91), 0.23 (95% CI: 0.11-0.38), and 0.044, respectively.

conclusionsWe developed an eight-variable XGBoost model to predict mortality for patients with spine fracture that performed similarly to the 37-variable APACHE-IV score when applied to a study population from the eICU dataset. Although the APACHE-IV is the most robust method for ICU mortality scoring, novel methods utilizing machine learning guided feature selection should be explored given the potential to simplify and improve on the proprietary APACHE-IV system.