Identification of four novel acute‐on‐chronic liver failure clusters with distinct clinical trajectories and mortality using machine learning methods
ML for the first time could identify clusters with distinct phenotypes, trajectories and outcomes in ACLF. Stratification into clusters can address heterogeneity, guide prognosis, recruitment in trials, resource allocation and liver transplant discussions in ACLF.
Authors
- Saurabh Mishra (ORCID: https://orcid.org/0000-0002-8833-8887)
- Patrick S. Kamath (ORCID: https://orcid.org/0000-0002-7888-1165)
- Akash Roy (ORCID: https://orcid.org/0000-0001-5126-1655)
- Madhumita Premkumar (ORCID: https://orcid.org/0000-0003-2961-4148)
- Virendra Singh (ORCID: https://orcid.org/0000-0002-9113-5167)
- Arka De (ORCID: https://orcid.org/0000-0002-8567-1676)
- Nipun Verma (ORCID: https://orcid.org/0000-0003-4328-0914)
- Radha K. Dhiman (ORCID: https://orcid.org/0000-0002-4107-8512)
- Arun Valsan (ORCID: https://orcid.org/0000-0002-8410-9665)
- Sahaj Rathi (ORCID: https://orcid.org/0000-0001-6529-9080)
- Sunil Taneja (ORCID: https://orcid.org/0000-0003-3901-6969)
- Pratibha Garg (ORCID: https://orcid.org/0009-0002-3490-5855)
- Parminder Kaur (ORCID: https://orcid.org/0009-0007-3548-2301)
- Ajay K. Duseja
Institutions
- Mayo Clinic (US)
- WinnMed (US)
- Post Graduate Institute of Medical Education and Research (IN)
Publication Details
- Journal
- Alimentary Pharmacology & Therapeutics
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1111/apt.18274
- Citations
- 8
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 3.09