Machine learning approaches to enhance diagnosis and staging of patients with MASLD using routinely available clinical information

PLoS One. 2024 Feb 29;19(2):e0299487. doi: 10.1371/journal.pone.0299487. eCollection 2024.

Abstract

Aims: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints.

Methods: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable.

Results: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance.

Conclusions: This study developed a series of ML models of accuracy ranging from 71.9-99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means.

MeSH terms

  • Humans
  • Machine Learning
  • Metabolic Diseases*
  • Non-alcoholic Fatty Liver Disease* / diagnosis
  • Patients
  • Supervised Machine Learning

Grants and funding

This work was supported by Newcastle University and Red Hat UK. This work has been supported by the LITMUS project, which has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 777377. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. QMA is an NIHR Senior Investigator and is supported by the Newcastle NIHR Biomedical Research Centre. This communication reflects the view of the authors and neither IMI nor the European Union and EFPIA are liable for any use that may be made of the information contained herein.