Accurate prediction of all-cause mortality in patients with metabolic dysfunction-associated steatotic liver disease using electronic health records.
Ann Hepatol
; : 101528, 2024 Jul 04.
Article
em En
| MEDLINE
| ID: mdl-38971372
ABSTRACT
INTRODUCTION AND OBJECTIVES:
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. PATIENTS ANDMETHODS:
nâ¯=â¯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 nâ¯=â¯528 MASLD patients.RESULTS:
In-sample model performance achieved AUROC curve 0.74-0.90 (95â¯% CI 0.72-0.94), sensitivity 64â¯%-82â¯%, specificity 75â¯%-92â¯% and Positive Predictive Value (PPV) 94â¯%-98â¯%. Out-of-sample model validation had AUROC 0.70-0.86 (95â¯% CI 0.67-0.90), sensitivity 69â¯%-70â¯%, specificity 96â¯%-97â¯% and PPV 75â¯%-77â¯%. 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.CONCLUSIONS:
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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MEDLINE
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En
Ano de publicação:
2024
Tipo de documento:
Article