Beschreibung:
<jats:title>Abstract</jats:title>
<jats:sec id="j_labmed-2022-0162_abs_001">
<jats:title>Objectives</jats:title>
<jats:p>The shortage of grafts for liver transplantation requires risk stratification and adequate allocation rules. 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.</jats:p>
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<jats:sec id="j_labmed-2022-0162_abs_002">
<jats:title>Methods</jats:title>
<jats:p>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. 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.</jats:p>
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<jats:sec id="j_labmed-2022-0162_abs_003">
<jats:title>Results</jats:title>
<jats:p>Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. 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. 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.</jats:p>
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<jats:sec id="j_labmed-2022-0162_abs_004">
<jats:title>Conclusions</jats:title>
<jats:p>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.</jats:p>
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