Using Machine Learning to Predict Outcomes of Patients with Blunt Traumatic Aortic Injuries

Author:

Lu Eileen1ORCID,Dubose Joseph2,Venkatesan Mythreye3,Wang Zhiping Paul3,Starnes Benjamin W.4,Saqib Naveed U.5,Miller Charles C.5,Azizzadeh Ali1,Chou Elizabeth L.1

Affiliation:

1. Division of Vascular Surgery, Cedars-Sinai Medical Center, Los Angeles, CA

2. Department of Surgery, University of Texas at Austin Dell Medical School, Austin, TX

3. Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA

4. Division of Vascular Surgery, Department of Surgery, University of Washington, Seattle, WA

5. Department of Cardiothoracic and Vascular Surgery, University of Texas Health Science Center, Houston, TX

Abstract

ABSTRACT Introduction The optimal management of blunt thoracic aortic injury (BTAI) remains controversial, with experienced centers offering therapy ranging from medical management to TEVAR. We investigated the utility of a machine learning (ML) algorithm to develop a prognostic model of risk factors on mortality in patients with BTAI. Methods The Aortic Trauma Foundation registry was utilized to examine demographics, injury characteristics, management and outcomes of patients with BTAI. A STREAMLINE (A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison) model as well as logistic regression (LR) analysis with imputation using chained equations was developed and compared. Results From a total of 1018 patients in the registry, 702 patients were included in the final analysis. Of the 258 (37%) patients who were medically managed, 44 (17%) died during admission, 14 (5.4%) of which were aortic related deaths. 444 (63%) patients underwent TEVAR and 343 of which underwent TEVAR within 24 hours of admission. Amongst TEVAR patients, 39 (8.8%) patients died and 7 (1.6%) had aortic related deaths. (Table 1) Comparison of the STREAMLINE and LR model showed no significant difference in ROC curves and high AUCs of 0.869 (95% CI, 0.813 – 0.925) and 0.840 (95% CI, 0.779 – 0.900) respectively in predicting in-hospital mortality. Unexpectedly, however, the variables prioritized in each model differed between models (Figure 1A-B). The top three variables identified from the LR model were similar to that from existing literature. The STREAMLINE model, however, prioritized location of the injury along the lesser curve, age and aortic injury grade (Figure 1A). Conclusions Machine learning provides insight on prioritization of variables not typically identified in standard multivariable logistic regression. Further investigation and validation in other aortic injury cohorts are needed to delineate the utility of ML models. Level of Evidence Level III Study Type Original research (prognostic/epidemiological)

Publisher

Ovid Technologies (Wolters Kluwer Health)

Reference47 articles.

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2. Functional and survival outcomes in traumatic blunt thoracic aortic injuries: an analysis of the National Trauma Databank;J Vasc Surg,2009

3. Timing and causes of death after injuries;Proc (Bayl Univ Med Cent),2013

4. Blunt thoracic aortic injuries: an autopsy study;J Trauma,2011

5. Prospective study of blunt aortic injury: multicenter trial of the American Association for the Surgery of Trauma;J Trauma,1997

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