A Scoring Model to Predict In-Hospital Mortality in Patients With Budd-Chiari Syndrome

A model that can predict short-term mortality in patients with the Budd-Chiari syndrome (BCS) with a high degree of accuracy is currently lacking. The primary objective of our study was to develop an easy-to-use in-hospital mortality prediction model in patients with BCS using easily available clinical variables.

Data were extracted from the National Inpatient Sample to identify all adult patients with a listed diagnosis of BCS from 2008 to 2017 using ICD-9 or ICD-10 codes. After identifying independent risk factors of in-hospital mortality, we developed a prediction model using logistic regression analysis. The model was built and validated in a training and a validation data set, respectively. Using the model, we risk stratified patients into low-, intermediate-, and high-risk groups.

Between 2008 and 2017, we identified a total of 5,306 (weighted sample size 26,110) discharge diagnosis of patients with BCS, with an overall in-hospital mortality of 7.14%. The independent risk factors that predicted mortality were age of 50 years or older, ascites, sepsis, acute respiratory failure, acute liver failure, hepatorenal syndrome, and cancers. The mortality prediction model that incorporated these risk factors had an area under the receiver operating characteristic curve of 0.87 (95% CI 0.85-0.95) for the training data and 0.89 (95% CI 0.86-0.92) for the validation data. Patients with low-, intermediate-, and high-risk scores had a predicted in-patient mortality of 4%, 30%, and 66%, respectively.

Using a national administrative database, we developed a reliable in-patient mortality prediction model with an excellent accuracy. The model was able to risk stratify patients into low-, intermediate-, and high-risk groups.