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\begin{document}

\title{A machine learning approach to Model for End Stage Liver Disease score
in cardiac surgery}



\author[1]{Edgar Aranda-Michel}%
\author[2]{Ibrahim Sultan}%
\author[3]{Arman Kilic}%
\author[4]{Valentino Bianco}%
\author[2]{James Brown}%
\author[4]{Derek Serna-Gallegos}%
\affil[1]{University of Pittsburgh Medical Center Health System}%
\affil[2]{University of Pittsburgh}%
\affil[3]{Medical University of South Carolina}%
\affil[4]{University of Pittsburgh Medical Center}%


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  \date{May 18, 2024}


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\begin{abstract}
Objective The Model for End- Stage Liver Disease (MELD) score is a
composite number of physiologic parameters and likely has non-linear
effects on operative outcomes. . We use machine learning to evaluate the
relationship between MELD score and outcomes of cardiac surgery. Methods
All STS indexed elective cardiac surgical procedures at our institution
between 2011 and 2018 were included. MELD score was retrospectively
calculated. Logistic regression models and an imbalanced random forest
classifier was created on operative mortality using 30 preoperative
characteristics. Cox regression models and random forest survival models
were created for long-term survival. Variable importance analysis (VIMP)
was conducted to rank variables by predictive power. Linear and machine
learned models were compared with their receiver operating
characteristic (ROC) and Brier score respectively. Results The patient
population included 3,872 individuals. Operative mortality was 1.7\% and
5-year survival was 82.1\%. MELD score was the 4th largest positive
predictor on VIMP analysis for both operative long-term survival and the
strongest negative predictor for operative mortality. The logistic model
ROC area was 0.762, compared to the random forest classifier ROC of
0.674. The Brier score of the random forest survival model was larger
(worse) than the cox regression starting at 2 years and continuing
throughout the study period. Conclusions MELD score and other continuous
variables had high degrees of non-linearity to mortality. This is
demonstrated by the fact that MELD score was not significant in the cox
multivariable regression but was strongly important in the random forest
survival model.%
\end{abstract}%



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