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. 2023 Feb;43(2):442-451.
doi: 10.1111/liv.15361. Epub 2022 Oct 11.

APASL-ACLF Research Consortium-Artificial Intelligence (AARC-AI) model precisely predicts outcomes in acute-on-chronic liver failure patients

Nipun Verma  1 , Ashok Choudhury  2 , Virendra Singh  1 , Ajay Duseja  1 , Manum Al-Mahtab  3 , Harshad Devarbhavi  4 , Chundamannil E Eapen  5 , Ashish Goel  5 , Qin Ning  6 , Zhongping Duan  7 , Saeed Hamid  8 , Wasim Jafri  8 , Amna Shubhan Butt  8 , Akash Shukla  9 , Soek-Siam Tan  10 , Dong Joon Kim  11 , Jinhua Hu  12 , Ajit Sood  13 , Omesh Goel  13 , Vandana Midha  13 , Hashmik Ghaznian  14 , Manoj Kumar Sahu  15 , Guan Huei Lee  16 , Sombat Treeprasertsuk  17 , Samir Shah  18 , Laurentius A Lesmana  19 , Rinaldi C Lesmana  19 , V G Mohan Prasad  20 , Shiv K Sarin  2 ; APASL ACLF Research Consortium (AARC) for APASL ACLF Working Party  2
Affiliations

APASL-ACLF Research Consortium-Artificial Intelligence (AARC-AI) model precisely predicts outcomes in acute-on-chronic liver failure patients

Nipun Verma et al. Liver Int. 2023 Feb.

Abstract

Background and aims: We hypothesized that artificial intelligence (AI) models are more precise than standard models for predicting outcomes in acute-on-chronic liver failure (ACLF).

Methods: We recruited ACLF patients between 2009 and 2020 from APASL-ACLF Research Consortium (AARC). Their clinical data, investigations and organ involvement were serially noted for 90-days and utilized for AI modelling. Data were split randomly into train and validation sets. Multiple AI models, MELD and AARC-Model, were created/optimized on train set. Outcome prediction abilities were evaluated on validation sets through area under the curve (AUC), accuracy, sensitivity, specificity and class precision.

Results: Among 2481 ACLF patients, 1501 in train set and 980 in validation set, the extreme gradient boost-cross-validated model (XGB-CV) demonstrated the highest AUC in train (0.999), validation (0.907) and overall sets (0.976) for predicting 30-day outcomes. The AUC and accuracy of the XGB-CV model (%Δ) were 7.0% and 6.9% higher than the standard day-7 AARC model (p < .001) and 12.8% and 10.6% higher than the day 7 MELD for 30-day predictions in validation set (p < .001). The XGB model had the highest AUC for 7- and 90-day predictions as well (p < .001). Day-7 creatinine, international normalized ratio (INR), circulatory failure, leucocyte count and day-4 sepsis were top features determining the 30-day outcomes. A simple decision tree incorporating creatinine, INR and circulatory failure was able to classify patients into high (~90%), intermediate (~60%) and low risk (~20%) of mortality. A web-based AARC-AI model was developed and validated twice with optimal performance for 30-day predictions.

Conclusions: The performance of the AARC-AI model exceeds the standard models for outcome predictions in ACLF. An AI-based decision tree can reliably undertake severity-based stratification of patients for timely interventions.

Keywords: cirrhosis; data science; machine learning; mortality; prognosis.

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References

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