Background: In patients with hepatitis B-related cirrhosis, it is important to predict those at high-risk of oesophagogastric variceal haemorrhage (OVH) to decide upon prophylactic treatment. Our published model developed with right liver lobe volume and diameters of portal vein system did not incorporate maximum variceal size as a factor. This study thus aimed to develop an improved model based on right liver lobe volume, diameters of maximum oesophagogastric varices (OV) and portal vein system obtained at magnetic resonance imaging (MRI) to predict OVH.
Methods: Two hundred and thirty consecutive individuals with hepatitis B-related cirrhosis undergoing abdominal enhanced MRI were randomly grouped into training (n=160) and validation sets (n=70). OVH was confirmed in 51 and 23 participants in the training and validation sets during 2-year follow-up period, respectively. Spleen, total liver, right lobe, caudate lobe, left lateral lobe, and left medial lobe volumes, together with diameters of maximum OV and portal venous system were measured on MRI. In the training set, univariate analyses and binary logistic regression analyses were conducted to determine independent predictors. The performance of the model for predicting OVH constructed based on independent predictors from the training set was evaluated with receiver operating characteristic (ROC) analysis and validated in the validation set.
Results: The model for predicting OVH was established based on right liver lobe volume and diameters of the maximum OV, left gastric vein, and portal vein [odds ratio (OR) =0.991, 2.462, 1.434, and 1.582, respectively; all P values <0.05]. The logistic regression model equation [-0.009 × right liver lobe volume + 0.901 × maximum OV diameter (MOVD) + 0.361 × left gastric vein diameter (LGVD) + 0.459 × portal vein diameter (PVD) - 7.842] with a cutoff value of -0.656 for predicting OVH obtained excellent performance with an area under ROC curve (AUC) of 0.924 [95% confidence interval (CI): 0.878-0.971]. The Delong test showed negative statistical difference in the model performance between the training and validation sets, with a P value >0.99.
Conclusions: The model could help well screen those patients at high risk of OVH for timely intervention and avoiding the fatal complications.
Keywords: Gastrointestinal haemorrhage; liver cirrhosis; magnetic resonance imaging (MRI); portal vein.
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