• OBJECTIVES
    • To identify characteristics that contribute to surgical complexity in pilon fractures and to develop a machine learning (ML) Pilon Surgical Difficulty Score (PSDS) based on these factors.
  • DESIGN
    • Retrospective cohort study.
  • SETTING
    • Academic Level I trauma center.
  • PATIENT SELECTION CRITERIA
    • Pilon fractures (OTA/AO Type 43) in adult patients treated with open reduction internal fixation.
  • OUTCOMES MEASURES AND COMPARISONS
    • Various patient, injury, and radiological characteristics were assessed. Surgical difficulty was measured using 2 outcomes: (1) operative time and (2) perceived difficulty. Perceived difficulty was determined using the opinion of 16 fellowship-trained orthopaedic traumatologists on a 10-point scale. Significant predictors of difficulty were determined using univariate analyses. ML models were used to develop a PSDS for both operative time and surgical difficulty.
  • RESULTS
    • One hundred operatively fixed pilon fractures were included. Predictors of operative time were age, OTA/AO classification, articular comminution, articular impaction, bone loss, delay to surgery, poor quality reduction, number of approaches, and number of articular fragments. Predictors of perceived difficulty included OTA/AO classification and delay to surgery. Operative time PSDS had a mean absolute error of 64 minutes and a 60-minute buffer accuracy of 59%. Perceived difficulty PSDS had a mean absolute error of 1.7 points and a 2-point buffer accuracy of 63%.
  • CONCLUSION
    • ML was used to generate accurate PSDSs for operative time and difficulty for pilon fractures. Future work should aim to clinically validate these PSDSs, so they may improve patient outcomes.
  • LEVEL OF EVIDENCE
    • Level III Diagnostic.