Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.

Journal: The American surgeon
Published Date: Jul 3, 2025

Abstract

BackgroundTraumatic rib fractures can lead to respiratory complications necessitating unplanned intubation, but predictors have been inadequately delineated. We used interpretable machine learning to predict unplanned intubations in rib fracture patients while identifying predictors.MethodsTQIP 2017-2022 was queried for adult patients admitted to the hospital following a rib fracture injury. An XGBoost model was developed to predict unplanned intubation using variables that can be known on admission. A 70/10/20 train/validation/test split was used. SHapley Additive exPlanations (SHAP) were used for interpretation. SHAP allows individualized interpretation of predictors for each patient.ResultsThe cohort had 905 615 patients; 2.3% had unplanned intubations. Model metrics at the F1 maximizing threshold (0.78) included AUROC = 0.83, F1 score = 0.17, accuracy = 0.94, precision = 0.12, recall = 0.29, specificity = 0.95, and Brier score = 0.17. The most influential variables, as determined by mean absolute SHAP values, were admission location (0.62), Injury Severity Score (0.40), age (0.37), absence of comorbidities (0.18), pulse rate (0.14), pneumothorax (0.13), oxygen saturation (0.15), chronic obstructive pulmonary disease (0.11), respiratory rate (0.10), and sex (0.10). ICU admission was the location most influential in predicting an unplanned intubation. SHAP dependency plots determined the directional relationship between variables' values and SHAP values.DiscussionPatients above the F1 maximizing threshold had a 7.4-fold increase in unplanned intubations compared to those below. Nearly 30% of all unplanned intubations were captured at this threshold. Our model's identification of these high-risk patients and influential factors not previously considered in the literature could guide closer monitoring and early interventions.

Authors

  • Shamir C Harry
    University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Melissa A Kendall
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Emily A Grimsley
    Department of Surgery, University of South Florida Morsani College of Medicine, 2 Tampa General Circle, Rm 7015, Tampa, FL, 33606, USA.
  • Rachel L Wolansky
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Johnathan V Torikashvili
    University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • David Boughanem
    University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Yifan Liang
    University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Rajavi Parikh
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Joseph Sujka
    Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Paul C Kuo
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: paul.kuo@luhs.org.
  • Tyler Zander
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.

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