Imaging Inflammation in Compensated Advanced Chronic liver disease as a new predictor for liver decompensation

Volume 9, Issue 2, April 2024     |     PP. 50-69      |     PDF (2400 K)    |     Pub. Date: May 7, 2024
DOI: 10.54647/cm321260    27 Downloads     2149 Views  

Author(s)

Atul Kapoor, Department of Radiology, Advanced Diagnostics and institute of Imaging. Amritsar. Punjab, India.
Aprajita kapur, Department of Radiology, Advanced Diagnostics and institute of Imaging. Amritsar. Punjab, India.

Abstract
Introduction: Alcohol and Non-alcoholic fatty liver disease (NAFLD) are the commonest causes of advanced chronic liver disease (ACLD) worldwide. Various imaging are commonly used in the diagnosis and follow up of these patients. Child Pugh and Model of end stage liver disease systems are used for survival and three mortality of decompensated ACLD patients. However there is a need for imaging biomarkers for compensated ACLD (cACLD) patients to predict likelihood of decompensation. With increasing stress on the role of systemic inflammation as a causative agent in the progression and decompensation of ACLD patients the new imaging technique of shear wave elastography with dispersion of liver (SWD) and small bowel (SBD) were evaluated alongwith liver stiffness estimation.
Material and Methods: 61 patients with cACLD with NAFLD or history of alcohol ingestion were enrolled after informed consent and were followed for a period of three months for any decompensation i.e. ascites, gastrointestinal bleed or encephalopathy. All patients underwent a fasting ultrasound examination with shear wave elastography and liver stiffness alongwith shear wave dispersion (SWD) and small bowel inflammation (SBD). Follow up examinations were done at 30, 45 and 90 days or any time in between if there was any clinical evidence of decompensation. The patients were divided into four groups I-IV based on SWD and SBD findings as group I(A0B1),group II(A1B0), group , Group III(A1B1), group (A0B0). Statistical analysis was done to assess the significance of differences in the groups and incidence of decompensation recorded in each group. A prediction model was developed using machine learning based on above parameters to predict decompensation in a 90 day period.
Results: Group II and III performed the largest groups with 44 patients. Statistically significant differences were seen in SWD and SBD in all the groups (p<0.001). A good linear regression of 0.52 and 0.64 was seen between SWD and SBD and liver stiffness. The study showed decompensation in 12/61 patients the largest were number being in the groups III and II with decompensation likelihood of 27% and 24% respectively using Kaplan Meyer survival plot.SBD showing a highest hazard ratio of 1:24.4 while SWD alone had hazard ratio of 1:2.4.
Conclusion: The use of SWE markers is a novel way to evaluate patients with cACLD patients and using machine learning model predict 3 month likelihood of decompensation with 75% accuracy.

Keywords
Compensated advanced chronic liver disease, Cirrhosis, shear wave elastography, Shear wave dispersion, decompensated advanced liver disease.

Cite this paper
Atul Kapoor, Aprajita kapur, Imaging Inflammation in Compensated Advanced Chronic liver disease as a new predictor for liver decompensation , SCIREA Journal of Clinical Medicine. Volume 9, Issue 2, April 2024 | PP. 50-69. 10.54647/cm321260

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