Literature DB >> 29558295

CT Prediction Model for Major Arterial Injury after Blunt Pelvic Ring Disruption.

David Dreizin1, Uttam Bodanapally1, Alexis Boscak1, Nikki Tirada1, Ghada Issa1, Jason W Nascone1, Louis Bivona1, Daniel Mascarenhas1, Robert V O'Toole1, Erika Nixon1, Rong Chen1, Eliot Siegel1.   

Abstract

Purpose To develop and test a computed tomography (CT)-based predictive model for major arterial injury after blunt pelvic ring disruptions that incorporates semiautomated pelvic hematoma volume quantification. Materials and Methods A multivariable logistic regression model was developed in patients with blunt pelvic ring disruptions who underwent arterial phase abdominopelvic CT before angiography from 2008 to 2013. Arterial injury at angiography requiring transarterial embolization (TAE) served as the outcome. Areas under the receiver operating characteristic (ROC) curve (AUCs) for the model and for two trauma radiologists were compared in a validation cohort of 36 patients from 2013 to 2015 by using the Hanley-McNeil method. Hematoma volume cutoffs for predicting the need for TAE and probability cutoffs for the secondary outcome of mortality not resulting from closed head injuries were determined by using ROC analysis. Correlation between hematoma volume and transfusion was assessed by using the Pearson coefficient. Results Independent predictor variables included hematoma volume, intravenous contrast material extravasation, atherosclerosis, rotational instability, and obturator ring fracture. In the validation cohort, the model (AUC, 0.78) had similar performance to reviewers (AUC, 0.69-0.72; P = .40-.80). A hematoma volume cutoff of 433 mL had a positive predictive value of 87%-100% for predicting major arterial injury requiring TAE. Hematoma volumes correlated with units of packed red blood cells transfused (r = 0.34-0.57; P = .0002-.0003). Predicted probabilities of 0.64 or less had a negative predictive value of 100% for excluding mortality not resulting from closed head injuries. Conclusion A logistic regression model incorporating semiautomated hematoma volume segmentation produced objective probability estimates of major arterial injury. Hematoma volumes correlated with 48-hour transfusion requirement, and low predicted probabilities excluded mortality from causes other than closed head injury. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29558295     DOI: 10.1148/radiol.2018170997

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  14 in total

1.  Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

Authors:  David Dreizin; Yuyin Zhou; Yixiao Zhang; Nikki Tirada; Alan L Yuille
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

2.  Commentary on "Multidetector CT in Vascular Injuries Resulting from Pelvic Fractures".

Authors:  David Dreizin
Journal:  Radiographics       Date:  2019 Nov-Dec       Impact factor: 5.333

3.  Dual-Energy CT and Cinematic Rendering to Improve Assessment of Pelvic Fracture Instability.

Authors:  Theresa J Yu; Abdulai Bangura; Uttam Bodanapally; Jason Nascone; Robert O'Toole; Yuanyuan Liang; David Dreizin
Journal:  Radiology       Date:  2022-04-19       Impact factor: 29.146

Review 4.  [Radiological diagnosis of pelvic ring fractures].

Authors:  Thomas Grieser
Journal:  Radiologe       Date:  2020-03       Impact factor: 0.635

Review 5.  Endovascular Management of Pelvic Trauma.

Authors:  Husameddin El Khudari; Ahmed Kamel Abdel Aal
Journal:  Semin Intervent Radiol       Date:  2021-04-15       Impact factor: 1.513

6.  A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.

Authors:  David Dreizin; Yuyin Zhou; Shuhao Fu; Yan Wang; Guang Li; Kathryn Champ; Eliot Siegel; Ze Wang; Tina Chen; Alan L Yuille
Journal:  Radiol Artif Intell       Date:  2020-11-11

7.  Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

Authors:  David Dreizin; Yuyin Zhou; Tina Chen; Guang Li; Alan L Yuille; Ashley McLenithan; Jonathan J Morrison
Journal:  J Trauma Acute Care Surg       Date:  2020-03       Impact factor: 3.697

8.  Do we really need the arterial phase on CT in pelvic trauma patients?

Authors:  Johannes Clemens Godt; Torsten Eken; Anselm Schulz; Kjetil Øye; Thijs Hagen; Johann Baptist Dormagen
Journal:  Emerg Radiol       Date:  2020-07-19

9.  Volumetric Markers of Body Composition May Improve Personalized Prediction of Major Arterial Bleeding After Pelvic Fracture: A Secondary Analysis of the Baltimore CT Prediction Model Cohort.

Authors:  David Dreizin; Remberto Rosales; Guang Li; Hassan Syed; Rong Chen
Journal:  Can Assoc Radiol J       Date:  2020-09-10       Impact factor: 2.248

10.  An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Authors:  David Dreizin; Florian Goldmann; Christina LeBedis; Alexis Boscak; Matthew Dattwyler; Uttam Bodanapally; Guang Li; Stephan Anderson; Andreas Maier; Mathias Unberath
Journal:  J Digit Imaging       Date:  2021-01-21       Impact factor: 4.056

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