ArticleIndian journal of gastroenterology : official journal of the Indian Society of Gastroenterology2026
Evaluation of spleen stiffness and liver stiffness as non-invasive predictors of esophageal varices in patients with cirrhosis of the liver.
Article in Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAlthough upper gastrointestinal endoscopy (UGIE) is the diagnostic gold standard for esophageal varices (EV), its invasive nature and limited accessibility present considerable challenges. This study aimed at evaluating liver stiffness measurement (LSM) and spleen stiffness measurement (SSM) via transient elastography (TE) as the non-invasive indicators for the presence and severity of EVs in cirrhotic patients.
methodsThis prospective, cross-sectional, observational study enrolled patients with compensated cirrhosis undergoing UGIE for the presence and grading of EVs. LSM and SSM were performed using FibroScan®. Non-invasive markers, including APRI, FIB-4, liver stiffness-spleen size-to platelet ratio score (LSPS) and spleen stiffness-spleen size-to platelet ratio score (SSPS), were also computed. Diagnostic efficacy was assessed through area under receiver-operating characteristic curve (AUROC) analysis, sensitivity and specificity.
resultsEVs were identified in 52.3% of the 132 enrolled patients (55% small, 45% large). Significant differences in laboratory parameters and non-invasive markers were observed between patients with and without EVs and between those with small vs. large EVs. SSM demonstrated superior diagnostic performance for predicting any EVs compared to LSM (AUROC = 0.955 vs. 0.847). The combined assessment of LS and SS, LSPS and particularly SSPS exhibited excellent predictive accuracy, with SSPS (AUROC = 0.997) proving the most robust predictor for any EVs. All elastography parameters had excellent diagnostic performance for the prediction of large EVs with SSM and combined assessment of LS and SS having the highest accuracy (AUROC = 1.000 for both).
conclusionBoth LSM and SSM are significantly associated with the presence and severity of EVs, with SSM exhibiting superior performance. The integration of these parameters, especially SSPS, further augments predictive accuracy.
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