Predicting Liver Stiffness Approximately 12 Months Ahead Using Serial Transient Elastography
5 affiliations
- 1Department of Hemodialysis Dalian Public Health Clinical Center No. 269, Gui Bai Road, Lu Gang, Ganjingzi District Dalian Liaoning China
- 2Unit 4-Department of Geriatric Medicine The Fourth People’s Hospital of Chengdu No.8 Huli-West First-Alley, Jinniu District Chengdu Sichuan China
- 3Department of Laboratory Medicine Liaoning Electric Power Central Hospital. No. 2, Wanghu Road, Heping District Shenyang Liaoning China
- 4Department of Gastroenterology West China Hospital of Sichuan University No. 37, Guoxue Lane, Wuhou District Chengdu Sichuan China wchscu.cn
- 5Biobank for Biological Samples and Data Dalian Public Health Clinical Center No. 269, Gui Bai Road, Lu Gang, Ganjingzi District Dalian Liaoning China
Abstract
Background: Liver stiffness measurement (LSM) is widely used to assess chronic liver disease, but a single measurement does not capture longitudinal change. We evaluated whether serial LSMs and routine clinical indicators could predict continuous LSM approximately 12 months later.
Methods: This retrospective longitudinal study included patients with at least two historical LSMs and a follow‐up LSM 270–450 days after the index date. The LSM closest to 365 days was the outcome, and each patient contributed the earliest eligible task. Patients were separated chronologically into a train set (index date through 2022), test set (2023), and validation set (2024). Nested expanding temporal cross‐validation selected the model, which was then fixed and compared with longitudinal benchmarks. Patient‐level bootstrapping provided 95% confidence intervals (CIs).
Results: The analysis included 715 patients (train, 434; test, 178; validation, 103). Random forest was selected. In the test set, its mean absolute error (MAE) was 3.622 kPa (95% CI, 2.828–4.769), R 2 was 0.456 (0.240–0.550), and calibration slope was 1.043 (0.612–1.475). In the validation set, MAE was 2.989 kPa (2.149–4.149), R 2 was 0.432 (−0.141–0.575), and calibration slope was 0.951 (0.513–1.598). In the test set, the model reduced MAE by 8.443 kPa (4.833–12.605) versus linear‐trend extrapolation; however, the last observed LSM performed better, with a paired last‐observed‐LSM‐minus‐model MAE difference of −0.658 kPa (−1.168 to −0.187). Historical LSM summaries were the dominant features.
Conclusions: The locked model showed reproducible out‐of‐time performance and near‐ideal calibration point estimates but did not outperform the last observed LSM. Independent multicenter validation is required before clinical translation.
Trial Registration: ClinicalTrials.gov identifier: NCT06123858
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BibTeX
@article{lei2026predicting,
title = {{Predicting Liver Stiffness Approximately 12 Months Ahead Using Serial Transient Elastography}},
author = {Ming Lei and Shoukang Zou and Qing Liu and Yangling Li and Jing Han and Yungang Zhang and Chunwen Pu},
journal = {Canadian Journal of Gastroenterology \& Hepatology},
year = {2026},
month = oct,
doi = {10.1155/cjgh/1261185},
url = {https://doi.org/10.1155/cjgh/1261185},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Predicting Liver Stiffness Approximately 12 Months Ahead Using Serial Transient Elastography
AU - Ming Lei
AU - Shoukang Zou
AU - Qing Liu
AU - Yangling Li
AU - Jing Han
AU - Yungang Zhang
AU - Chunwen Pu
PY - 2026
DA - 2026/10/27
JO - Canadian Journal of Gastroenterology & Hepatology
DO - 10.1155/cjgh/1261185
UR - https://doi.org/10.1155/cjgh/1261185
AB - Background: Liver stiffness measurement (LSM) is widely used to assess chronic liver disease, but a single measurement does not capture longitudinal change. We evaluated whether serial LSMs and routine clinical indicators could predict continuous LSM approximately 12 months later. Methods: This retrospective longitudinal study included patients with at least two historical LSMs and a follow‐up LSM 270–450 days after the index date. The LSM closest to 365 days was the outcome, and each patient contributed the earliest eligible task. Patients were separated chronologically into a train set (index date through 2022), test set (2023), and validation set (2024). Nested expanding temporal cross‐validation selected the model, which was then fixed and compared with longitudinal benchmarks. Patient‐level bootstrapping provided 95% confidence intervals (CIs). Results: The analysis included 715 patients (train, 434; test, 178; validation, 103). Random forest was selected. In the test set, its mean absolute error (MAE) was 3.622 kPa (95% CI, 2.828–4.769), R 2 was 0.456 (0.240–0.550), and calibration slope was 1.043 (0.612–1.475). In the validation set, MAE was 2.989 kPa (2.149–4.149), R 2 was 0.432 (−0.141–0.575), and calibration slope was 0.951 (0.513–1.598). In the test set, the model reduced MAE by 8.443 kPa (4.833–12.605) versus linear‐trend extrapolation; however, the last observed LSM performed better, with a paired last‐observed‐LSM‐minus‐model MAE difference of −0.658 kPa (−1.168 to −0.187). Historical LSM summaries were the dominant features. Conclusions: The locked model showed reproducible out‐of‐time performance and near‐ideal calibration point estimates but did not outperform the last observed LSM. Independent multicenter validation is required before clinical translation. Trial Registration: ClinicalTrials.gov identifier: NCT06123858
SN - 2291-2789
SN - 2291-2797
ER -