Artificial neural network-predicted PPG and HVPG with measured PPG in decompensated cirrhosis

The study compared the performance of artificial neural network (ANN)-predicted portal pressure gradient (PPG) and hepatic venous pressure gradient (HVPG) with measured PPG in patients with decompensated cirrhosis. Here's a detailed breakdown of the findings:

Correlation Results in Group A (Real-World Conditions):

• HVPG vs. Measured PPG: HVPG demonstrated negligible correlation with measured PPG (correlation coefficient r = 0.014). This indicates that HVPG is highly unreliable in estimating actual portal pressures in real-world conditions, likely due to the influence of hepatic venous collaterals.
• ANN-Predicted PPG vs. Measured PPG: ANN-predicted PPG showed moderate correlation with measured PPG (r = 0.437, P < 0.001), suggesting that the ANN model provides a more accurate and consistent estimate of portal pressure compared to HVPG.

Agreement Analysis:

• Bland–Altman plots revealed that ANN-predicted PPG had narrower limits of agreement with measured PPG (−8.45 to 8.51 mmHg) compared to HVPG (−22.17 to 10.03 mmHg). This signifies that the ANN model is more precise and less prone to large deviations from actual portal pressure values.

Group B Findings (Optimized Conditions):

• In Group B, which excluded patients with a high coefficient of variation (>30%) to approximate optimized conditions:
• HVPG vs. Measured PPG: HVPG showed moderate correlation (r = 0.457).
• ANN-Predicted PPG vs. Measured PPG: ANN-predicted PPG demonstrated a slightly better correlation (r = 0.476).
• This indicates that under optimized conditions, both methods perform comparably in estimating portal pressures.

Etiology Subgroup Findings:

• Hepatitis B–Related Cirrhosis: The ANN model significantly outperformed HVPG in hepatitis B–related cirrhosis, with a correlation coefficient of r = 0.716 for ANN-predicted PPG vs. r = 0.472 for HVPG.
• Alcohol-Related Cirrhosis: HVPG performed better than ANN-predicted PPG in alcohol-related cirrhosis.
• Autoimmune Cirrhosis: Both methods showed weak performance in autoimmune cirrhosis, indicating the need for further refinement in these specific cases.

Child–Pugh Subgroup Results:

• Class A and B Patients (Mild to Moderate Cirrhosis):
• Both HVPG and ANN-predicted PPG correlated moderately with measured PPG, showing comparable accuracy in less severe stages of cirrhosis.
• Class C Patients (Severe Cirrhosis):
• ANN-predicted PPG maintained correlation with measured PPG even in severe cirrhosis cases.
• HVPG completely failed to correlate with measured PPG in class C patients, highlighting its unreliability in advanced disease stages.

Clinical Relevance:

• ANN-predicted PPG offers a stable and reliable estimate of portal pressure in patients with venous collaterals or advanced disease, where HVPG often yields inaccurate results.
• In real-world settings (Group A), HVPG significantly underestimated PPG, leading to potential clinical misinterpretations of portal pressure severity. In contrast, ANN-predicted values closely matched measured PPG, reducing diagnostic errors.

Advantages of ANN Method:

• Noninvasive: Unlike HVPG, which requires invasive procedures, the ANN model uses clinical and imaging data to predict PPG.
• Reproducible: The ANN approach is less affected by procedural variability and anatomical complexities.
• Improved Accuracy: Especially in complex clinical conditions like severe cirrhosis, the ANN model demonstrated superior agreement and precision compared to HVPG.

Limitations:

• The study was retrospective, which limits the statistical strength and generalizability of the findings.
• Subgroup sizes were limited, particularly for alcohol-related and autoimmune cirrhosis, which may impact the reliability of results in these etiologies.
• The study focused only on patients with decompensated cirrhosis, excluding compensated cases.

Conclusion:

While HVPG remains the clinical gold standard, the ANN-predicted PPG demonstrated superior stability, agreement, and practicality under complex clinical conditions, making it a promising noninvasive complement for evaluating portal hypertension in cirrhosis patients. Future research should focus on validating the ANN model in prospective, multicenter studies across diverse etiologies and disease stages, including compensated cirrhosis.