![]() The research project on digital laboratory medicine (AMPEL) develops a clinical decision support system based on laboratory diagnostics that should support clinical practitioners in interpreting laboratory results and taking the necessary medical interventions. To reduce the risk of overlooking or incorrectly calculating and interpreting the results, clinical decision support systems may be used and could improve patient safety. Despite their published prediction improvement, the MELD-Plus scores are not yet used for transplant allocation.Īs shown by the MELD 3.0 and MELD-Plus risk scores, better predictive scores often need more variables and are more complicated. The derived MELD-Plus7 and MELD-Plus9 risk scores add albumin, white blood cell count, total cholesterol, age, and length of stay to the MELD-Na variables. There have been some attempts to use the data extracted from more than 300,000 electronic medical records from two hospitals in the United States to improve the MELD score. Furthermore, for acute-on-chronic liver failure, for example, the MELD score often underestimates the mortality risk. Patients with identical disease states can have very different MELD scores and thus receive different priority levels on the liver transplantation waiting list. In addition, women are disadvantaged in MELD-Na. Although the MELD score should be an objective allocation score, creatinine and INR are highly dependent on the laboratory methods used. It was subsequently revalidated to predict mortality risk in patients awaiting a liver transplantation. The MELD was initially developed to predict the survival of patients undergoing transjugular intrahepatic portosystemic shunts. Recently MELD 3.0 was introduced adding a factor for female sex, albumin and interactions between bilirubin and sodium and between albumin and creatinine. The MELD score has been extended by the sodium level (MELD-Na score) because this was found to be an important additional risk factor in liver cirrhosis. The MELD score estimates patients’ 3-month mortality risk based on laboratory results, namely, for bilirubin, creatinine, and the international normalized ratio (INR). The allocation of liver transplantation in most countries is based on disease severity determined by the model of end-stage liver disease (MELD). However, the shortage of grafts for transplantation from deceased donors requires risk stratification and precise and fair allocation rules. This end-stage of liver disease is usually irreversible, and the only available therapy is liver transplantation. Liver cirrhosis is the terminal result of the fibrotic remodeling of liver tissue due to chronic damage. We provide a new machine-learning-based model of end-stage liver disease that incorporates synthesis and inflammatory markers and may improve the classical MELD score for 90-day survival prediction. The new model offers improved discrimination and calibration over MELD and MELD with sodium (MELD-Na), MELD 3.0, or the MELD-Plus7 risk score. Beside the classical MELD international normalized ratio (INR) and bilirubin, the lasso regression selected cystatin C over creatinine, as well as IL-6, total protein, and cholinesterase. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. After comparing 13 different machine-learning algorithms in a nested cross-validation setting and selecting the best performing one, we built a new model to predict 90-day mortality in patients with end-stage liver disease. We retrospectively analyzed the clinical and laboratory data of 654 patients who were recruited during the evaluation process for liver transplantation at University Hospital Leipzig. This study aims to improve the model of end-stage liver disease (MELD) score for 90-day mortality prediction with the help of different machine-learning algorithms. The shortage of grafts for liver transplantation requires risk stratification and adequate allocation rules.
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