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


  • Mikayla Kricfalusi California University of Science and Medicine School of Medicine, 1501 Violet St, Colton, CA, USA
  • Richard C. Rice Loma Linda University Health, Department of Orthopedic Surgery, Loma Linda, CA, USA
  • Andrew Cabrera Loma Linda University School of Medicine, 11021 Campus Street, Loma Linda, CA, USA
  • Prathyusha Dasari University of California Riverside School of Medicine, 92521 Botanic Gardens Dr, Riverside, CA, USA
  • David Chung Loma Linda University Health, Department of Orthopedic Surgery, Loma Linda, CA, USA
  • Trevor Case California University of Science and Medicine School of Medicine, 1501 Violet St, Colton, CA, USA
  • David E. Ruckle Loma Linda University Health, Department of Orthopedic Surgery, Loma Linda, CA, USA
  • Joseph N. Liu USC Epstein Family Center for Sports Medicine, Keck Medicine of USC, 1520 San Pablo St. Los Angeles, CA, USA
  • Wayne K. Cheng Loma Linda University Health, Department of Orthopedic Surgery, Loma Linda, CA, USA
  • Olumide Danisa Loma Linda University Health, Department of Orthopedic Surgery, Loma Linda, CA, USA



ML, Odontoid fracture, Anterior odontoid screw fixation, Hematocrit


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.


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Original Research Articles