A Novel Inflammation-Based Risk Score Predicts Mortality in Acute Type A Aortic Dissection Surgery: The Additive Anti-inflammatory Action for Aortopathy and Arteriopathy Score

Abstract

Objective: To develop an inflammation-based risk stratification tool for operative mortality in patients with acute type A aortic dissection.

Methods: Between January 1, 2016 and December 31, 2021, 3124 patients from Beijing Anzhen Hospital were included for derivation, 571 patients from the same hospital were included for internal validation, and 1319 patients from other 12 hospitals were included for external validation. The primary outcome was operative mortality according to the Society of Thoracic Surgeons criteria. Least absolute shrinkage and selection operator regression were used to identify clinical risk factors. A model was developed using different machine learning algorithms. The performance of the model was determined using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves, and Brier score for calibration. The final model (5A score) was tested with respect to the existing clinical scores.

Results: Extreme gradient boosting was selected for model training (5A score) using 12 variables for prediction-the ratio of platelet to leukocyte count, creatinine level, age, hemoglobin level, prior cardiac surgery, extent of dissection extension, cerebral perfusion, aortic regurgitation, sex, pericardial effusion, shock, and coronary perfusion-which yields the highest AUC (0.873 [95% confidence interval (CI) 0.845-0.901]). The AUC of 5A score was 0.875 (95% CI 0.814-0.936), 0.845 (95% CI 0.811-0.878), and 0.852 (95% CI 0.821-0.883) in the internal, external, and total cohort, respectively, which outperformed the best existing risk score (German Registry for Acute Type A Aortic Dissection score AUC 0.709 [95% CI 0.669-0.749]).

Conclusion: The 5A score is a novel, internally and externally validated inflammation-based tool for risk stratification of patients before surgical repair, potentially advancing individualized treatment.

Trial registration: clinicaltrials.gov Identifier: NCT04918108.

Keywords: 5A, Additive Anti-inflammatory Action for Aortopathy & Arteriopathy; ATAAD, acute type A aortic dissection; AUC, area under the receiver operating characteristics curve; AVR, aortic valve regurgitation; CT, computed tomography; GERAADA, German Registry for Acute Type A Aortic Dissection; ICU, intensive care unit; KNN, K-nearest neighbor; LASSO, least absolute shrinkage and selection operator; NB, naïve Bayes; RF, random forest; STI, systemic thrombo-inflammatory; SVM, support vector machine; WBC, white blood cell.

© 2022 The Authors.

Figures

Figure 1

Machine learning workflow of model construction and validation. AUC, the area under the receiver operating characteristic curve; Cr, creatinine; Hgb, hemoglobin; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; SII, systemic inflammatory-immune; STI, systemic thrombo-inflammatory; WBC, white blood cell. ∗The 12 Chinese university cardiovascular centers are listed in the Supplemental Materials.

Figure 2

Characterization and performances of the LASSO model and the machine learning model. A, Coefficient profile plots of the LASSO model. B, Penalty plot for the LASSO model. C, Dose-response relationship between risk score and mortality. D, Relative importance of 12 variables predictive from machine learning (ML) inflammatory mode (5A risk score) in the derivation cohort. E, Prediction distributions of patients with acute type A aortic dissection according to the risk of mortality in ML inflammatory mode (5A risk score). F, The standard rate and odds ratio of operative mortality among the fourth quartile of ML inflammatory mode (5A risk score). AUC, the area under the receiver operating characteristic curve; LASSO, least absolute shrinkage and selection operator; OR, odds ratio; STI, systemic thrombo-inflammatory.

Figure 3

Comparison of the prediction performances of the LASSO-based model, ML-based clinical model, and ML-based inflammatory model in the derivation cohort. A, The AUC of the LASSO-based model. B, The AUC of the ML base model. C, The AUC of the ML inflammatory model. D, Calibration curves of the LASSO-based model. E, Calibration curves of the ML base model. F, Calibration curves of the ML inflammatory model. G, Decision curves of the LASSO-based model. H, Decision curves of the ML base model. I, Decision curves of the ML inflammatory model. AUC, area under the receiver operating characteristic curve; LASSO, least absolute shrinkage and selection operator; ML, machine learning.

Figure 4

The prediction performances of the inflammatory model in the internal, external and total cohorts. A, The AUC of the inflammatory model in the internal cohort. B, The AUC of the inflammatory model in the external cohort. C, The AUC of the inflammatory model in the total cohort; D, Calibration curve of inflammatory model in the internal cohort; E, Calibration curve of inflammatory model in the external cohort; F, Calibration curve of inflammatory model in the total cohort; G, Decision curve of inflammatory model in the internal cohort; H, Decision curve of inflammatory model in the external cohort; I, Decision curve of inflammatory model in the total cohort. AUC = the area under the receiver operating characteristic curve.