@article { , title = {Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study}, abstract = {Background and Aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and Results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53\%), at-risk NASH (NASH with F ≥ 2;35\%), significant (F ≥ 2;47\%), and advanced fibrosis (F ≥ 3;28\%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models. Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis.}, doi = {10.1097/HEP.0000000000000364}, eissn = {1527-3350}, issn = {0270-9139}, issue = {1}, journal = {Hepatology}, note = {In NYP s/s. Published in issue update set statement When published, add set statement: This is a pre-copyedited, author-produced version of an article accepted for publication in [insert journal title]. The published version of record [insert complete citation information here] is available online at: xxxxxxx [insert URL and DOI of the published article from the Journal website].}, pages = {258-271}, publicationstatus = {Published}, publisher = {Wiley}, url = {https://nottingham-repository.worktribe.com/output/17660766}, volume = {78}, year = {2023}, author = {Lee, Jenny and Westphal, Max and Vali, Yasaman and Boursier, Jerome and Ostroff, Rachel and Alexander, Leigh and Chen, Yu and Fournier, Celine and Geier, Andreas and Francque, Sven and Wonders, Kristy and Tiniakos, Dina and Bedossa, Pierre and Allison, Mike and Papatheodoridis, Georgios and Cortez-Pinto, Helena and Pais, Raluca and Dufour, Jean-Francois and Leeming, Diana Julie and Harrison, Stephen and Cobbold, Jeremy and Holleboom, Adriaan G. and Yki-Järvinen, Hannele and Crespo, Javier and Ekstedt, Mattias and Aithal, Guruprasad P. and Bugianesi, Elisabetta and Romero-Gomez, Manuel and Karsdal, Morten and Yunis, Carla and Schattenberg, Jörn M. and Schuppan, Detlef and Ratziu, Vlad and Brass, Clifford and Duffin, Kevin and Zwinderman, Koos and Pavlides, Michael and Anstee, Quentin M. and Bossuyt, Patrick M. and LITMUS investigators, LITMUS investigators} }