Predictive Risk Score for Cerebral Malperfusion in Type A Aortic Dissection Utilizing a Machine Learning Model
Presented During:
Thursday, April 25, 2024: 5:38PM - 7:00PM
Sheraton Times Square
Posted Room Name:
Central Park
Abstract No:
P0260
Submission Type:
Abstract Submission
Authors:
Alexander Mills (1), Akiko Tanaka (1), Yuki Ikeno (1), Lucas Ribe (1), Harleen Sandhu (1), Charles Miller (1), Charles Green (1), Danny Ramzy (1), Anthony Estrera (1)
Institutions:
(1) McGovern Medical School at UTHealth, Houston, TX
Submitting Author:
Alexander Mills
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Co-Author(s):
Akiko Tanaka
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Yuki Ikeno
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Lucas Ribe
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Harleen Sandhu
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Charles Miller
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Charles Green
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
*Danny Ramzy
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
*Anthony Estrera
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McGovern Medical School at UTHealth
McGovern Medical School at UTHealth
Presenting Author:
Alexander Mills
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University of Texas Health Science Center at Houston (UTHealth Houston)
University of Texas Health Science Center at Houston (UTHealth Houston)
Abstract:
Objectives:
Cerebral malperfusion due to acute type A aortic dissection can lead to severe neurologic deficits and even operative death. Early detection of risk for this sequela is imperative to optimize clinical decision-making and improve patient outcomes. Machine learning models have been shown to synthesize large, granular databases efficiently and accurately. We hypothesized that a machine learning model would be able to produce a novel predictive risk score for cerebral malperfusion to aid in early risk assessment.
Methods:
We retrospectively reviewed all patients undergoing surgical management for acute type A aortic dissection from 2001 to 2020 at our institution. We focused on readily available admission data that was retrieved upon initial assessment by the surgical team (i.e., prior medical/surgical history, admission laboratory values, admission imaging). Any missing data was imputed with a missRanger imputation model (RStudio). All data was then analyzed in a random forest regression machine learning model to identify key predictor variables for cerebral malperfusion (primary outcome). Risk scores were calculated for these variables using a generalized regression model. Predictive probabilities were then obtained, and the strength of the model was evaluated by the area under the curve, sensitivity/specificity, and negative/positive predictive values.
Results:
We identified 650 patients who underwent surgical repair for acute type A aortic dissection during the study period. Median age was 58.0 years old (interquartile range: 47.0-69.0). 183 (28.1%) were female. There were 119 patients (18.3%) who were diagnosed with cerebral malperfusion preoperatively. Our model determined 8 key predictor variables (score) for cerebral malperfusion: presenting comatose (2), presenting with altered mental status (2), hemiparesis on presentation (4), concomitant celiac malperfusion (1), concomitant renal malperfusion (1), prior history of stroke (1), prior history of transient ischemic attack (1), and dissection extending into either common carotid artery (1). This model had an area under the curve of 0.866 (0.798-0.933, p<0.001). For patients with a score of 1 or less, the sensitivity was 95.0%. With a score of 4 or more, the specificity was 97.0%. Table illustrates the sensitivities, specificities, positive predictive values, and negative predictive values for the different score ranges. Figure demonstrates the receiver operative curve for the model.
Conclusion:
A novel predictive risk score was developed using machine learning models to aid in the early detection of cerebral malperfusion. This model uses readily available information, is easy to calculate, and can aid the surgical team in clinical decision-making.
Cerebral malperfusion due to acute type A aortic dissection can lead to severe neurologic deficits and even operative death. Early detection of risk for this sequela is imperative to optimize clinical decision-making and improve patient outcomes. Machine learning models have been shown to synthesize large, granular databases efficiently and accurately. We hypothesized that a machine learning model would be able to produce a novel predictive risk score for cerebral malperfusion to aid in early risk assessment.
Methods:
We retrospectively reviewed all patients undergoing surgical management for acute type A aortic dissection from 2001 to 2020 at our institution. We focused on readily available admission data that was retrieved upon initial assessment by the surgical team (i.e., prior medical/surgical history, admission laboratory values, admission imaging). Any missing data was imputed with a missRanger imputation model (RStudio). All data was then analyzed in a random forest regression machine learning model to identify key predictor variables for cerebral malperfusion (primary outcome). Risk scores were calculated for these variables using a generalized regression model. Predictive probabilities were then obtained, and the strength of the model was evaluated by the area under the curve, sensitivity/specificity, and negative/positive predictive values.
Results:
We identified 650 patients who underwent surgical repair for acute type A aortic dissection during the study period. Median age was 58.0 years old (interquartile range: 47.0-69.0). 183 (28.1%) were female. There were 119 patients (18.3%) who were diagnosed with cerebral malperfusion preoperatively. Our model determined 8 key predictor variables (score) for cerebral malperfusion: presenting comatose (2), presenting with altered mental status (2), hemiparesis on presentation (4), concomitant celiac malperfusion (1), concomitant renal malperfusion (1), prior history of stroke (1), prior history of transient ischemic attack (1), and dissection extending into either common carotid artery (1). This model had an area under the curve of 0.866 (0.798-0.933, p<0.001). For patients with a score of 1 or less, the sensitivity was 95.0%. With a score of 4 or more, the specificity was 97.0%. Table illustrates the sensitivities, specificities, positive predictive values, and negative predictive values for the different score ranges. Figure demonstrates the receiver operative curve for the model.
Conclusion:
A novel predictive risk score was developed using machine learning models to aid in the early detection of cerebral malperfusion. This model uses readily available information, is easy to calculate, and can aid the surgical team in clinical decision-making.
Aortic Symposium:
Dissection
Image or Table
