Using machine learning to predict outcomes of patients with blunt traumatic aortic injuries
Eileen Lu1, Joseph Dubose, Mythreye Venkatesan
1From the Division of Vascular Surgery (E.L., A.A., E.L.C.), Cedars-Sinai Medical Center, Los Angeles, California; Department of Surgery (J.D.), University of Texas at Austin Dell Medical School, Austin, Texas; Department of Computational Biomedicine (M.V., Z.P.W.), Cedars-Sinai Medical Center, West Hollywood, California; Division of Vascular Surgery, Department of Surgery (B.W.S.), University of Washington, Seattle, Washington; and Department of Cardiothoracic and Vascular Surgery (N.U.S., C.C.M.), University of Texas Health Science Center, Houston, Texas.
Background:
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. Four hundred forty-four (63%) patients underwent TEVAR and 343 of which underwent TEVAR within 24 hours of admission. Among 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% confidence interval, 0.813-0.925) and 0.840 (95% confidence interval, 0.779-0.900) respectively in predicting in-hospital mortality. Unexpectedly, however, the variables prioritized in each model differed between models. The top 3 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.
Conclusion:
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:
Prognostic and Epidemiological; Level III.
