Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.

Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model. n=940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n=528 MASLD patients. In-sample model performance achieved AUROC curve 0.74-0.90 (95% CI: 0.72-0.94), sensitivity 64%-82%, specificity 75%-92% and Positive Predictive Value (PPV) 94%-98%. Out-of-sample model validation had AUROC 0.70-0.86 (95% CI: 0.67-0.90), sensitivity 69%-70%, specificity 96%-97% and PPV 75%-77%. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days. A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions.

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Copyright © 2024. Published by Elsevier España, S.L.U.

Déclaration de conflit d'intérêts

Conflicts of interest ID and BS are employees of Bering Limited. ID is a shareholder at Bering Limited. The funder (Innovate UK) provided support in the form of salaries for authors ID and BS but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Views expressed are those of the authors and not necessarily those of Innovate UK or Bering. IAR serves as a consultant or has received speakers’ fees from Novo Nordisk, Boehringer Ingelheim, Bayer, Roche, and Norgine. TJK serves as a consultant for or has received speakers’ fees from Resolution Therapeutics, Clinnovate Health, Perspectum, Servier Laboratories, Kynos Therapeutics, Concept Life Sciences, HistoIndex, Fibrofind, and Incyte Corporation. JAF serves as a consultant or advisory board member for Resolution Therapeutics, Kynos Therapeutics, Ipsen, River 2 Renal Corp., Stimuliver, Global Clinical Trial Partners and Guidepoint and has received research grant funding from GlaxoSmithKline, Intercept Pharmaceuticals and Genentech.