Publication:

Supervised machine learning algorithms used to predict post-surgical outcomes following anterior surgical fixation of odontoid fractures

Background Odontoid fractures have a high mortality rate, and numerous classification systems have previously predicted surgical outcomes with mixed consensus. We generated a machine learning (ML) construct to predict post-operative adverse events following anterior (ORIF) of odontoid fractures.

Methods 266 patients from the American college of surgeons-national surgical quality improvement program (ACS-NSQIP) with anterior ORIF (CPT 22318) of odontoid fractures from 2008-2018 were analyzed using ML algorithms random forest classifier (RF), gradient boosting classifier (GB), support vector machine classifier (SVM), Gaussian Naive Bayes classifier (GNB), and multi-layer perceptron classifier (MLP), and were compared to logistic regression classifier (LR). Algorithms predicted increased length of stay (LOS), need for transfusion (Transf), non-home discharge (NHD), and any adverse event (AAE). Permutation feature importance (PFI) identified risk factors.

Results ML algorithms outperformed LR. The average AUC for predicting Transf was 0.635 (accuracy=77.4%), extended LOS=0.652 (accuracy 59.6%), NHD 0.788 (accuracy=71.9%) and AAE 0.649 (accuracy 68.1%). GB performed highest for Transf (AUC=0.861), identifying operative time (PFI 0.253, p=0.016). GB and RF performed equally for NHD (AUC=0.819), highlighting preoperative hematocrit (PFI=0.157, p<0.001). GB predicted AAE (AUC=0.720) also identifying preoperative hematocrit (PFI=0.112, p<0.001). RF predicted extended LOS (AUC=0.669) highlighting preoperative hematocrit (PFI=0.049, p<0.001).

Conclusions ML outperformed LR, successfully predicting Transf, extended LOS, NHD, and AAE for anterior ORIF of odontoid fractures. Our construct may complement conventional risk stratification to reduce adverse outcomes and excess cost.