Predictive Parameters for Hospital Admission Among Liver Transplant Recipients Presenting to the Emergency Department: A 5-Year Study

Abstract

Aim: Liver transplantation (LT) has significantly improved patient outcomes, leading to increased numbers of LT recipients seeking emergency department (ED) care. However, there is a lack of comprehensive information regarding their outcomes and parameters influencing hospital admission decisions. This study aims to address the gap in knowledge by analyzing critical parameters influencing hospital admission decisions for LT recipients presenting at the ED. Materials and Methods: A retrospective observational case-control study was conducted with 247 consecutive LT patients who visited a tertiary care center’s ED between 2018 and 2023. Demographic information, transplantation details, presenting complaints, laboratory results, and ED outcomes were evaluated. Univariate analysis identified significant predictors for an artificial neural network (ANN) analysis to predict admission decisions. Results: Among 247 LT recipients presenting at the ED, 48.2% were admitted. The most common complaints among admitted patients were abdominal pain and fever. Patients admitted had higher levels of alanine aminotransferase, aspartate aminotransferase (AST), alkaline phosphatase, gamma-glutamyl transferase, and C-reactive protein (CRP) and lower levels of total protein and albumin compared to discharged patients. Ultrasonography findings of perihepatic fluid collection were more common in admitted patients. The ANN analysis identified total protein, conjugated bilirubin, CRP, total bilirubin, and AST as the most influential factors predicting hospital admission decisions. Conclusion: The ANN analysis identified total protein, conjugated bilirubin, CRP, total bilirubin, and AST influencing hospital admission decisions for liver transplant recipients in the ED. Emphasizing the significance of these parameters can guide evidence-based guidelines for improved patient care and resource allocation in emergency settings.

Keywords

• liver transplantation
• hospital admission decisions
• emergency department outcomes
• artificial neural network analysis
• clinical decision-making