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Mortality prediction of nonalcoholic patients presenting with upper gastrointestinal bleeding using data mining
Abd Elrazek M A Abd Elrazek1, Hamdy M Mahfouz, Amro M Metwally
1aDepartment of Gastroenterology & Hepatology, Al Azhar Faculty of Medicine, Al Azhar Assiut University Hospital, Al Azhar University, Assiut, Egypt bDepartment of Virology, Division of Liver Diseases, Ichan School of Medicine at Mount Sinai, New York, New York, USA.
European Journal of Gastroenterology & Hepatology
|October 4, 2013
Summary
In nonalcoholic patients with upper gastrointestinal bleeding, chronic hepatitis C virus infection and NSAID-associated splenomegaly predict mortality. Data mining accurately identified these significant mortality factors.
Area of Science:
- Gastroenterology
- Hepatology
- Internal Medicine
Background:
- Upper gastrointestinal (GI) bleeding is a common medical emergency.
- Predicting mortality, length of stay, and cost in upper GI bleeding patients requires further investigation.
- Alcoholism can complicate upper GI bleeding management and outcomes.
Purpose of the Study:
- To identify significant predictors of mortality in nonalcoholic patients with upper GI bleeding.
- To evaluate the effectiveness of data mining programs in predicting mortality.
- To analyze the impact of specific conditions on mortality in this patient group.
Main Methods:
- Retrospective review of 152 nonalcoholic patients with upper GI bleeding.
- Analysis of patient demographics, causes of bleeding, and outcomes.
- Application of a descriptive data mining model to identify mortality predictors.
Main Results:
- Overall mortality rate was 19.07% (29 out of 152 patients).
- A data mining program achieved 92.08% accuracy in predicting mortality.
- Chronic hepatitis C virus infection and NSAID-associated splenomegaly were identified as significant mortality predictors.
Conclusions:
- Chronic hepatitis C virus infection is a significant predictor of mortality in nonalcoholic upper GI bleeding.
- NSAID-associated splenomegaly due to portal hypertension is a key mortality predictor.
- Data mining models can effectively identify critical factors influencing mortality in upper GI bleeding.
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