Predicting Portal Pressure Gradient in Patients with Decompensated Cirrhosis: A Non-invasive Deep Learning Model.

Background: A high portal pressure gradient (PPG) is associated with an increased risk of failure to control esophagogastric variceal hemorrhage and refractory ascites in patients with decompensated cirrhosis. However, direct measurement of PPG is invasive, limiting its routine use in clinical practice. Consequently, there is an urgent need for non-invasive techniques to assess PPG.

Aim: To develop and validate a deep learning model that predicts PPG values for patients with decompensated cirrhosis and identifies those with high-risk portal hypertension (HRPH), who may benefit from early transjugular intrahepatic portosystemic shunt (TIPS) intervention.

Methods: Data of 520 decompensated cirrhosis patients who underwent TIPS between June 2014 and December 2022 were retrospectively analyzed. Laboratory and imaging parameters were used to develop an artificial neural network model for predicting PPG, with feature selection via recursive feature elimination for comparison experiments. The best performing model was tested by external validation.

Results: After excluding 92 patients, 428 were included in the final analysis. A series of comparison experiments demonstrated that a three-parameter (3P) model, which includes the international normalized ratio, portal vein diameter, and white blood cell count, achieved the highest accuracy of 87.5%. In two distinct external datasets, the model attained accuracy rates of 85.40% and 90.80%, respectively. It also showed notable ability to distinguish HRPH with an AUROC of 0.842 in external validation.

Conclusion: The developed 3P model could predict PPG values for decompensated cirrhosis patients and could effectively distinguish HRPH.