Predictive models for trans-stenotic pressure gradient in the transverse sinus based on multimodal correlates.

BACKGROUND

The invasive nature of the methods currently available for screening based on the trans-stenotic pressure gradient (TPG) limits their widespread application. We aimed to develop machine learning models for predicting TPG in the transverse sinus.

METHODS

The study was approved by the Ethics Committee of Beijing Friendship Hospital Affiliated to Capital Medical University. We retrospectively analyzed the data of 83 patients confirmed to have pressure-measured transverse sinus stenosis from January 2017 to June 2022 at Beijing Friendship Hospital. The dataset included clinical, laboratory, computed tomography angiography, and four-dimensional flow magnetic resonance data with 29 feature indicators. Six machine learning methods-support vector machine, k-nearest neighbors, decision tree, random forest (RF), gradient boosting decision tree, and extreme gradient boosting-were employed to construct classification models for 4- and 8-mmHg TPG thresholds via fivefold cross-validation. Models were evaluated according to accuracy, sensitivity, specificity, F1 score, the Matthews correlation coefficient (MCC), and area under the curve (AUC).

RESULTS

For the 4-mmHg threshold, RF showed the highest performance, with an accuracy of 0.83, a specificity of 0.62, a sensitivity of 0.95, an F1 score of 0.92, an MCC of 0.60, and an AUC of 0.92. RF also excelled for the 8-mmHg threshold, with accuracy, specificity, sensitivity, F1 score, MCC, and AUC values of 0.84, 0.86, 0.81, 0.78, 0.70, and 0.91, respectively. SHapley Additive exPlanations analysis indicated that peak velocity and average velocity differences positively correlated with TPG, whereas the residual area ratio was negatively correlated.

CONCLUSIONS

The models demonstrated strong discriminative ability and are useful for noninvasive TPG prediction.