Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment

Anirudh Gangadhar¹, Bima J Hasjim², Xun Zhao³, Yingji Sun⁴, Joseph Chon⁵, Aman Sidhu⁵, Elmar Jaeckel⁶, Nazia Selzner⁶, Mark S Cattral⁷, Blayne A Sayed⁵, Michael Brudno⁸, Chris McIntosh⁹, Mamatha Bhat¹⁰

Affiliations

¹ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
² Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Department of Surgery, University of California - Irvine, Orange, California, USA.
³ McGill University Health Center, Montreal, Quebec, Canada.
⁴ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
⁵ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada.
⁶ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
⁷ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada.
⁸ Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.
⁹ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada; Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Peter Munk Cardiac Center and Ted Rogers Centre for Heart Research, University Health Network, Toronto, ON, Canada.
¹⁰ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada. Electronic address: Mamatha.Bhat@uhn.ca.

PMID: 40446930
DOI: 10.1016/j.jhep.2025.04.040

Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment

Anirudh Gangadhar et al. J Hepatol. 2025.

. 2025 May 28:S0168-8278(25)02253-6.

doi: 10.1016/j.jhep.2025.04.040. Online ahead of print.

Authors

Affiliations

¹ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
² Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Department of Surgery, University of California - Irvine, Orange, California, USA.
³ McGill University Health Center, Montreal, Quebec, Canada.
⁴ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada.
⁵ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada.
⁶ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
⁷ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Department of Surgery, University of Toronto, Toronto, ON, Canada.
⁸ Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada.
⁹ Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada; Joint Department of Medical Imaging, University Health Network, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Peter Munk Cardiac Center and Ted Rogers Centre for Heart Research, University Health Network, Toronto, ON, Canada.
¹⁰ Transplant AI Initiative, Ajmera Transplant Centre, University Health Network, University of Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada; Vector Institute, Toronto, ON, Canada. Electronic address: Mamatha.Bhat@uhn.ca.

PMID: 40446930
DOI: 10.1016/j.jhep.2025.04.040

Abstract

Background & aims: Addressing many clinical questions, such as estimating survival differences between living donor (LDLT) and deceased donor liver transplantation (DDLT), relies on observational studies, as randomized-controlled trials (RCTs) are often unfeasible. Thus, we developed decision path similarity matching (DPSM) - a novel machine learning (ML)-based algorithm that simulates RCT-like conditions to mitigate confounding in observational data.

Methods: We conducted a retrospective study of adult (≥18-years-old) LT candidates between 2002-2023 using the Scientific Registry of Transplant Recipients database. A random forest classifier was trained to predict transplant type from clinicodemographic characteristics. After hyperparameter tuning, decision paths were extracted for individual patients and tree-averaged Hamming distances (d_h) were computed for every LDLT-DDLT decision path pair. One-to-one matching was performed by minimizing the total d_h across all patient pairs. Random survival forest models were then trained on the matched cohorts to predict post-transplant survival.

Results: Of 72,581 LT recipients, 93.8% underwent DDLT and 6.2% underwent LDLT. After matching LDLT with DDLT recipients, DPSM successfully reduced confounding associations as shown by a decrease in AUROC_post-match from 0.82 to 0.51. Random survival forest models outperformed traditional Cox regression in both groups (C-index_ldlt 0.67 vs. 0.57; C-index_ddlt 0.74 vs. 0.65). The predicted 10-year mean survival gain for LDLT over DDLT was 10.3% (SD = 5.7%). In particular, the survival benefit from LDLT was greatest for primary sclerosing cholangitis (12.4% ± 5.3%) and HCV (12.1% ± 4.7%) compared to other etiologies.

Conclusions: DPSM offers a novel ML-based method for simulating RCT-like conditions in observational data, enabling personalized survival prediction while minimizing confounding. This approach equips clinicians with a new tool to more confidently evaluate treatment effects.

Impact and implications: Living donor liver transplantation (LDLT) has emerged as an effective strategy to expand the donor pool, though data from randomized-controlled trials (RCTs) are lacking due to ethical and practical barriers. We developed a novel machine learning-based algorithm termed decision path similarity matching (DPSM), which more effectively reduces bias in observational data by creating cohorts that better approximate those in RCTs. Using DPSM, LDLT was associated with a predicted 10-year mean survival gain of 10.3% (SD = 5.7%) over deceased donor liver transplantation. LDLT was also shown to be particularly beneficial for certain etiologies, i.e. HCV and PSC. DPSM provides clinicians with a powerful tool that transforms real-world observational data into an RCT-like framework, making it an invaluable method in situations where true randomization is not feasible.

Keywords: deceased donor liver transplantation; living donor liver transplantation; matching; random forest; randomized controlled trial.

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Conflict of interest statement

Conflict of interest The authors of this study declare that they do not have any conflict of interest. Please refer to the accompanying ICMJE disclosure forms for further details.

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Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment

Affiliations

Personalized survival benefit estimation from living donor liver transplantation with a novel machine learning method for confounding adjustment

Authors

Affiliations

Abstract

Conflict of interest statement

LinkOut - more resources

Full Text Sources