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In Annals of vascular surgery

OBJECTIVES : This study aimed to establish and validate a machine learning-based model for the prediction of early phase postoperative hypertension (EPOH) requiring the administration of intravenous vasodilators after carotid endarterectomy (CEA).

METHODS : Perioperative data from consecutive CEA procedures performed from Jan 2013 to August 2019 were retrospectively collected. EPOH was defined in post-CEA patients as hypertension involving a systolic blood pressure above 160 mmHg and requiring the administration of any intravenous vasodilator medications in the first 24 hours after a return to the vascular ward. Gradient boosted regression trees (GBRT) were used to construct the predictive model, and the featured importance scores were generated by using each feature's contribution to each tree in the model. To evaluate the model performance, the area under the receiver operating characteristic curve (AUC) was used as the main metric. Four-fold stratified cross-validation was performed on the dataset, and the average performance of the four folds was reported as the final model performance.

RESULTS : A total of 406 CEA operations were performed under general anesthesia. Fifty-three patients (13.1%) met the definition of EPOH. There was no significant difference in the percentage of postoperative stroke/death between patients with and without EPOH during the hospital stay. EPOH patients exhibited a higher incidence of postoperative cerebral hyperperfusion syndrome (7.5% vs 0, p<.001), as well as a higher incidence of cerebral hemorrhage (3.8% vs 0, p<.001). The GBRT prediction model achieved an average AUC of .77 (95% CI .62 to .92). When the sensitivity was fixed near .90, the model achieved an average specificity of .52 (95% CI .28 to .75).

CONCLUSIONS : We have built the first-ever machine learning-based prediction model for EPOH after CEA. The validation result from our single-center database was very promising. This novel prediction model has the potential to help vascular surgeons identify high-risk patients and reduce related complications more efficiently.

Tan Jinyun, Wang Qi, Shi Weihao, Liang Kun, Yu Bo, Mao Qingqing


Carotid Endarterectomy, Gradient Boosted Regression Trees, Machine Learning, Postoperative Hypertension, Predictive Modeling