Noninvasive model based on liver and spleen stiffness for predicting clinical decompensation in patients with cirrhosis

BACKGROUND The hepatic venous pressure gradient serves as a crucial parameter for assessing portal hypertension and predicting clinical decompensation in individuals with cirrhosis. However, owing to its invasive nature, there has been growing interest in identifying noninvasive alternatives. Transient elastography offers a promising approach for measuring liver stiffness and spleen stiffness, which can help estimate the likelihood of decompensation in patients with chronic liver disease. AIM To investigate the predictive ability of the liver stiffness measurement (LSM) and spleen stiffness measurement (SSM) in conjunction with other noninvasive indicators for clinical decompensation in patients suffering from compensatory cirrhosis and portal hypertension. METHODS This study was a retrospective analysis of the clinical data of 200 patients who were diagnosed with viral cirrhosis and who received computed tomography, transient elastography, ultrasound, and endoscopic examinations at The Second Affiliated Hospital of Xi’an Jiaotong University between March 2020 and November 2022. Patient classification was performed in accordance with the Baveno VI consensus. The area under the curve was used to evaluate and compare the predictive accuracy across different patient groups. The diagnostic effectiveness of several models, including the liver stiffness-spleen diameter-platelet ratio, variceal risk index, aspartate aminotransferase-alanine aminotransferase ratio, Baveno VI criteria, and newly developed models, was assessed. Additionally, decision curve analysis was carried out across a range of threshold probabilities to evaluate the clinical utility of these predictive factors. RESULTS Univariate and multivariate analyses demonstrated that SSM, LSM, and the spleen length diameter (SLD) were linked to clinical decompensation in individuals with viral cirrhosis. On the basis of these findings, a predictive model was developed via logistic regression: Ln P/(1-P) = -4.969 - 0.279 × SSM + 0.348 × LSM + 0.272 × SLD. The model exhibited strong performance, with an area under the curve of 0.944. At a cutoff value of 0.56, the sensitivity, specificity, positive predictive value, and negative predictive value for predicting clinical decompensation were 85.29%, 88.89%, 87.89%, and 86.47%, respectively. The newly developed model demonstrated enhanced accuracy in forecasting clinical decompensation among patients suffering from viral cirrhosis when compared to four previously established models. CONCLUSION Noninvasive models utilizing SSM, LSM, and SLD are effective in predicting clinical decompensation among patients with viral cirrhosis, thereby reducing the need for unnecessary hepatic venous pressure gradient testing.