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. 2024 May 8;14(10):981.
doi: 10.3390/diagnostics14100981.

Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score

Nermin Salkić  1 , Predrag Jovanović  1   2 , Mislav Barišić Jaman  3 , Nedim Selimović  2 , Frane Paštrović  3 , Ivica Grgurević  3   4
Affiliations

Machine Learning for Short-Term Mortality in Acute Decompensation of Liver Cirrhosis: Better than MELD Score

Nermin Salkić et al. Diagnostics (Basel). .

Abstract

Prediction of short-term mortality in patients with acute decompensation of liver cirrhosis could be improved. We aimed to develop and validate two machine learning (ML) models for predicting 28-day and 90-day mortality in patients hospitalized with acute decompensated liver cirrhosis. We trained two artificial neural network (ANN)-based ML models using a training sample of 165 out of 290 (56.9%) patients, and then tested their predictive performance against Model of End-stage Liver Disease-Sodium (MELD-Na) and MELD 3.0 scores using a different validation sample of 125 out of 290 (43.1%) patients. The area under the ROC curve (AUC) for predicting 28-day mortality for the ML model was 0.811 (95%CI: 0.714- 0.907; p < 0.001), while the AUC for the MELD-Na score was 0.577 (95%CI: 0.435-0.720; p = 0.226) and for MELD 3.0 was 0.600 (95%CI: 0.462-0.739; p = 0.117). The area under the ROC curve (AUC) for predicting 90-day mortality for the ML model was 0.839 (95%CI: 0.776- 0.884; p < 0.001), while the AUC for the MELD-Na score was 0.682 (95%CI: 0.575-0.790; p = 0.002) and for MELD 3.0 was 0.703 (95%CI: 0.590-0.816; p < 0.001). Our study demonstrates that ML-based models for predicting short-term mortality in patients with acute decompensation of liver cirrhosis perform significantly better than MELD-Na and MELD 3.0 scores in a validation cohort.

Keywords: MELD 3.0 score; MELD-Na score; liver cirrhosis; liver transplantation; machine learning; mortality prediction; transplant selection.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Flow diagram illustrating the selection of patient records.
Figure 2
Figure 2
(A). Normalized importance of each input variable in ML model for prediction of 28-day mortality in decompensated liver cirrhosis. (B). Normalized importance of each input variable in ML model for prediction of 90-day mortality in decompensated liver cirrhosis.
Figure 3
Figure 3
(A). Areas under the ROC curve (AUC) for ML model, MELD-Na, and MELD 3.0 scores for prediction of 28-day mortality in decompensated liver cirrhosis. (B). Areas under the ROC curve (AUC) for the ML model and MELD-Na scores for prediction of 90-day mortality in decompensated liver cirrhosis.
See this image and copyright information in PMC

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