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<p>Liver decompensation represents a critical milestone in compensated advanced chronic liver disease (cACLD). Liver stiffness measurement (LSM) has emerged as a valuable non-invasive marker. This study aimed to develop an LSM-based liver decompensation risk prediction. We retrospectively recruited 1064 cACLD patients (LSM ≥ 10&#xa0;kPa), divided into derivation (<i>n</i> = 745) and validation (<i>n</i> = 319) groups. Fine-Gray competing risk regression identified independent risk factors. Optimal cut-off values for risk stratification were determined using X-tile software. Model performance was evaluated using C-index and calibration curves. The main etiology was hepatitis B virus infection (69.8%). During follow-up, 328 patients (30.8%) developed liver decompensation with median decompensation time of 33 (16–50) months. Six independent predictors were identified: age, LSM, spleen diameter, hemoglobin, platelet, and international normalized ratio. The model demonstrated good discrimination [C-index: 0.779 (0.714–0.845)], calibration and overall performance (Brier Score 0.139). LSM contributed significantly (likelihood ratio test = 47.99, <i>P</i> &lt; 0.001) with hazard ratio increasing substantially when LSM &gt; 20&#xa0;kPa. Patients were stratified using optimal cut-offs into low-risk (≤ 147.7 points), medium-risk (147.7–206.6 points), and high-risk (≥ 206.6 points) groups, with decompensation rates of 13.4%, 58.0%, and 86.7%, respectively, and median time to decompensation of 37 (18–55), 33 (15–50), and 28 (17–48) months, respectively. Cumulative decompensation incidences differed significantly among risk groups (Gray’s test, <i>P</i> &lt; 0.001). A user-friendly web-based LSM-Based Liver Decompensation Risk Score assessment tool was developed. Despite single-center retrospective design, hepatitis B focus, and lacking external validation, the LSM-based model effectively identified high-risk patients, providing valuable clinical decision support.</p>

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Liver stiffness measurement-based risk score for predicting liver decompensation risk: a single-center retrospective Chinese study

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  • 发表日期：

    2025年11月18日

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  • Yanqiu Li,
  • Zihang Qiao,
  • Jinze Li,
  • Yongqi Li,
  • Ying Feng,
  • Xianbo Wang

摘要

Liver decompensation represents a critical milestone in compensated advanced chronic liver disease (cACLD). Liver stiffness measurement (LSM) has emerged as a valuable non-invasive marker. This study aimed to develop an LSM-based liver decompensation risk prediction. We retrospectively recruited 1064 cACLD patients (LSM ≥ 10 kPa), divided into derivation (n = 745) and validation (n = 319) groups. Fine-Gray competing risk regression identified independent risk factors. Optimal cut-off values for risk stratification were determined using X-tile software. Model performance was evaluated using C-index and calibration curves. The main etiology was hepatitis B virus infection (69.8%). During follow-up, 328 patients (30.8%) developed liver decompensation with median decompensation time of 33 (16–50) months. Six independent predictors were identified: age, LSM, spleen diameter, hemoglobin, platelet, and international normalized ratio. The model demonstrated good discrimination [C-index: 0.779 (0.714–0.845)], calibration and overall performance (Brier Score 0.139). LSM contributed significantly (likelihood ratio test = 47.99, P < 0.001) with hazard ratio increasing substantially when LSM > 20 kPa. Patients were stratified using optimal cut-offs into low-risk (≤ 147.7 points), medium-risk (147.7–206.6 points), and high-risk (≥ 206.6 points) groups, with decompensation rates of 13.4%, 58.0%, and 86.7%, respectively, and median time to decompensation of 37 (18–55), 33 (15–50), and 28 (17–48) months, respectively. Cumulative decompensation incidences differed significantly among risk groups (Gray’s test, P < 0.001). A user-friendly web-based LSM-Based Liver Decompensation Risk Score assessment tool was developed. Despite single-center retrospective design, hepatitis B focus, and lacking external validation, the LSM-based model effectively identified high-risk patients, providing valuable clinical decision support.