A Machine Learning Approach for Predicting Early Phase Postoperative Hypertension in Patients Undergoing Carotid
Jinyun Tan1, Qi Wang1, Weihao Shi1
1Department of Vascular Surgery, Huashan Hospital, Fudan University, Shanghai, China.
Insights
A machine learning model predicts early phase postoperative hypertension after carotid endarterectomy, identifying patients at risk for complications like cerebral hyperperfusion syndrome.
Area of Science:
- Vascular Surgery
- Machine Learning in Medicine
- Predictive Analytics
Background:
- Early phase postoperative hypertension (EPOH) requiring intravenous vasodilators after carotid endarterectomy (CEA) is a clinical challenge.
- Predicting EPOH is crucial for managing post-CEA complications.
Purpose of the Study:
- To develop and validate a machine learning model for predicting EPOH after CEA.
- To identify patients at higher risk of developing EPOH and associated complications.
Main Methods:
- Retrospective analysis of perioperative data from 406 CEA procedures.
- Gradient boosted regression trees used to build the predictive model.
- Model performance evaluated using Area Under the Receiver Operating Characteristic Curve (AUC).
Main Results:
- 13.1% of patients developed EPOH.
- EPOH was associated with increased incidence of cerebral hyperperfusion syndrome and cerebral hemorrhage.
- The prediction model achieved an average AUC of 0.77.
Conclusions:
- The first machine learning-based prediction model for EPOH after CEA was developed.
- The model shows promising validation results for identifying high-risk patients.
- This tool can aid vascular surgeons in proactive management and complication reduction.
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 < 0.001), as well as a higher incidence of cerebral hemorrhage (3.8% vs. 0, P < 0.001). The gradient boosted regression trees prediction model achieved an average AUC of 0.77 (95% CI 0.62 to 0.92). When the sensitivity was fixed near 0.90, the model achieved an average specificity of 0.52 (95% CI 0.28 to 0.75).
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.
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