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Published on: May 3, 2018
Adding 100 Hz Spleen Stiffness Measurement to the Baveno VII Model Improves Decompensation Risk Prediction in cACLD.
Emma Vanderschueren1,2, Wim Laleman1,2
1Department of Gastroenterology and Hepatology, University Hospitals Leuven, KU Leuven, Leuven, Belgium.
Spleen stiffness measurement (SSM) and liver stiffness measurement (LSM) help predict decompensation in advanced liver disease. Combining these with clinical factors in models like NICER improves risk prediction for patients with compensated advanced chronic liver disease (cACLD).
Area of Science:
- Hepatology
- Medical Imaging
- Diagnostic Tools
Background:
- Compensated advanced chronic liver disease (cACLD) carries a risk of hepatic decompensation.
- Spleen stiffness measurement (SSM) using a 100 Hz probe shows promise in diagnosing portal hypertension.
- The predictive value of 100 Hz-SSM, alone or with liver stiffness measurement (LSM), for decompensation in cACLD needs further evaluation.
Purpose of the Study:
- To assess the efficacy of 100 Hz-SSM in predicting hepatic decompensation in patients with cACLD.
- To determine if combining 100 Hz-SSM with LSM improves decompensation risk prediction.
- To evaluate the performance of novel risk models incorporating SSM and LSM.
Main Methods:
- Retrospective analysis of 371 cACLD patients (LSM ≥ 10 kPa) undergoing elastography.
- Collection of baseline elastography, laboratory, demographic, and clinical data.
- Recording of liver-related events over a median follow-up of 35 months to assess hepatic decompensation.
Main Results:
- Both LSM (C-index=0.79) and 100 Hz-SSM (C-index=0.70) predicted hepatic decompensation.
- The NICER model (Non-Invasive CSPH Estimated Risk), incorporating SSM, BMI, and platelets, showed improved prediction (C-index=0.84).
- The Baveno VII model combined with SSM stratified patients into distinct risk groups for decompensation (p<0.001).
Conclusions:
- LSM and 100 Hz-SSM are valuable for assessing decompensation risk in cACLD.
- Incorporating these measurements into risk models like NICER or Baveno VII enhances predictive accuracy.
- These non-invasive tools aid in managing patients with multi-etiological cACLD.
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