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A CT-based model accurately predicts arterial injury in pelvic disruptions using hematoma volume. This tool aids in identifying patients needing embolization and predicting mortality risk.

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Area of Science:

  • Radiology
  • Trauma Surgery
  • Medical Imaging Analysis

Background:

  • Blunt pelvic ring disruptions can cause severe arterial hemorrhage.
  • Predicting the need for intervention like transarterial embolization (TAE) is critical.
  • Existing methods may lack precision in assessing injury severity.

Purpose of the Study:

  • To develop and validate a computed tomography (CT)-based predictive model for major arterial injury.
  • To incorporate semiautomated pelvic hematoma volume quantification into the model.
  • To estimate the probability of arterial injury and associated mortality.

Main Methods:

  • Developed a multivariable logistic regression model using CT data from patients with blunt pelvic ring disruptions.
  • Quantified pelvic hematoma volume using semiautomated segmentation.
  • Validated the model's performance against trauma radiologists using receiver operating characteristic (ROC) analysis.

Main Results:

  • Key predictors included hematoma volume, contrast extravasation, atherosclerosis, rotational instability, and obturator ring fracture.
  • The CT-based model demonstrated strong predictive performance (AUC, 0.78), comparable to expert readers.
  • A hematoma volume cutoff of 433 mL predicted major arterial injury requiring TAE with 87%-100% positive predictive value.

Conclusions:

  • A CT-derived logistic regression model provides objective probability estimates for major arterial injury.
  • Pelvic hematoma volume correlates with transfusion requirements and can help predict mortality risk.
  • The model aids in clinical decision-making for trauma patients with pelvic disruptions.