Comparison of machine learning algorithms to SAPS II in predicting in-hospital mortality of fractures of the pelvis and acetabulum: analyzes based on MIMIC-III database

Abstract

Fractures of pelvis and acetabulum are at high risk of death worldwide. However, the capability of mortality prediction by the conventional system Simple Acute Physiologic Score (SAPS) II remains limited. Here, we hypothesized that the use of machine learning (ML) algorithms could provide better performance of prediction than SAPS II for traumatic patients in the intensive care unit (ICU). Customized ML models were built including support vector machine, decision tree, logistic regression and random forest based on MIMIC-III clinical database. 307 patients were enrolled with an ICD-9 diagnosis of pelvic, acetabular or combined pelvic and acetabular fractures. Feature expansion method was used for experimental models by extending SAPS II features to the first 72 hours after ICU admission, compared with the traditional first-24-hours ones that were used to build respective control models. A comparison of each model’s performance was made through the area under the receiver-operating characteristic curve (AUROC). All the ML models outperformed SAPS II system (AUROC = 0.73), among which the experimental random forest model had the supreme performance (AUROC of 0.90). The results suggested that ML models could aid in better performance of mortality prediction for pelvic and acetabular injuries and potentially support decision-making for orthopedics and ICU practitioners.

Keywords

• mortality prediction
• pelvic trauma
• acetabular fractures
• saps ii
• mimic
• machine learning (ml)