An explainable machine learning model for type B aortic dissection identification: development and internal evaluation.
Machine learning model using clinical and laboratory data identified type B aortic dissection
An explainable machine learning model for type B aortic dissection identification: development and internal evaluation.
Type B aortic dissection (Type B aortic dissection) is a life-threatening cardiovascular emergency that requires timely recognition.
This study aimed to develop and internally evaluate an explainable machine learning model for identifying existing Type B aortic dissection using routinely available clinical history and admission laboratory data.
This single-center retrospective case-control study included 1,640 participants, comprising 854 patients with CTA-confirmed Type B aortic dissection and 786 hospitalized controls who underwent whole-aorta CTA and were confirmed not to have aortic dissection.
higher scores mean better detection; 1.0 is a perfect model
Least absolute shrinkage and selection operator regression identified five predictors: hypertension, white blood cell count, lymphocyte percentage, basophil percentage, and monocyte count.
Several models showed comparable discrimination in the held-out internal test set.
SHAP analysis indicated that lymphocyte percentage, hypertension, monocyte count, white blood cell count, and basophil percentage were the major contributors to the predictions of the final model, with lymphocyte percentage showing the highest mean absolute SHAP value.
This study developed and internally evaluated an explainable machine-learning model for identifying existing Type B aortic dissection in a single-center CTA-confirmed retrospective case-control cohort.
Given the lack of external validation, this study should be regarded as exploratory. External validation in clinically relevant acute symptomatic populations is required before clinical implementation.