Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

Autor:	Su GL; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.; Department of Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA., Zhang P; Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA., Belancourt PX; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA., Youles B; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA., Enchakalody B; Department of Surgery, University of Michigan, Ann Arbor, Michigan, USA., Perumalswami P; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.; Department of Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA., Waljee A; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.; Department of Learning Health Sciences, University of Michigan, Ann Arbor, Michigan, USA.; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA., Saini S; Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.; Department of Medicine, Division of Gastroenterology and Hepatology, University of Michigan, Ann Arbor, Michigan, USA.; Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan, USA.
Jazyk:	angličtina
Zdroj:	Hepatology (Baltimore, Md.) [Hepatology] 2024 Oct 01; Vol. 80 (4), pp. 928-936. Date of Electronic Publication: 2023 Dec 29.
DOI:	10.1097/HEP.0000000000000750
Abstrakt:	Background and Aims: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease. Approach and Results: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively. Conclusions: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease. (Copyright © 2023 American Association for the Study of Liver Diseases.)
Databáze:	MEDLINE
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