The goal of this observational study is to enhance the ability to forecast kidney failure in liver transplant patients in the ICU under multidrug treatment by developing a computer platform that integrates mathematical models of drug interactions, proteomics, and clinical data. The main outcomes it aims to develop are:

1. Design the multidrug web computing platform with available information on drug pair interactions (DDIs).
2. Integrate the proteomic and clinical data of liver transplant patients into the IT platform.
3. Implement the multidrug web platform to predict the clinical evolution of liver transplant patients.

The aim of this project is to enhance the ability to forecast kidney failure for liver transplant patients in the ICU by developing a computer platform that integrates mathematical models of drug interactions, proteomics, and clinical data. This integrated platform enables the prediction of the development of AKI in patients who have undergone OLT. Through the integration of mathematical models, the platform captures complex interactions between multiple drugs and proteomics data from patients, contributing to an improved predictive capacity for AKI outcomes.

By leveraging proteomic insights, personalized therapeutic interventions can be devised, promoting safer and more effective treatments. This research seeks to empower medical professionals with comprehensive information to navigate the complex landscape of drug interactions and personalized treatment strategies, ultimately contributing to improved patient outcomes and healthcare practices in the realm of liver transplantation.

Inclusion Criteria:

• Liver transplant patients undergoing surgery at UC Christus Hospital

Exclusion Criteria:

• Patients on renal replacement therapy
• Patients for whom no clinical or pharmacological registry is available

Liver transplant patients admitted to intensive care after surgery