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• W3041760740 abstract "Background 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 January 2013 to August 2019 were retrospectively collected. EPOH was defined in post-CEA patients as hypertension involving a systolic blood pressure above 160 mm Hg and requiring the administration of any intravenous vasodilator medications in the first 24 hr after a return to the vascular ward. Gradient boosted regression trees 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 was used as the main metric. Four-fold stratified cross-validation was performed on the data set, and the average performance of the 4 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. Patients with EPOH exhibited a higher incidence of postoperative cerebral hyperperfusion syndrome (7.5% vs. 0, P  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." @default.
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• W3041760740 date "2021-02-01" @default.
• W3041760740 modified "2023-09-23" @default.
• W3041760740 title "A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid Endarterectomy" @default.
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• W3041760740 doi "https://doi.org/10.1016/j.avsg.2020.07.001" @default.
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