Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis
Background. Liver transplantation is essential for many people with primary sclerosing cholangitis (PSC). People with PSC are less likely to receive a deceased donor liver transplant compared with other causes of chronic liver disease. This disparity may stem from the inaccuracy of the model for end...
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| Language: | English |
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Wolters Kluwer
2025-04-01
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| Series: | Transplantation Direct |
| Online Access: | http://journals.lww.com/transplantationdirect/fulltext/10.1097/TXD.0000000000001774 |
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| author | Xun Zhao, MD Maryam Naghibzadeh, MD Yingji Sun, MSc Arya Rahmani, BSc Leslie Lilly, MD Nazia Selzner, MD, PhD Cynthia Tsien, MD, MPH Elmar Jaeckel, MD Mary Pressley Vyas Rahul Krishnan, PhD Gideon Hirschfield, MD, PhD, MB Mamatha Bhat, MD, PhD |
| author_facet | Xun Zhao, MD Maryam Naghibzadeh, MD Yingji Sun, MSc Arya Rahmani, BSc Leslie Lilly, MD Nazia Selzner, MD, PhD Cynthia Tsien, MD, MPH Elmar Jaeckel, MD Mary Pressley Vyas Rahul Krishnan, PhD Gideon Hirschfield, MD, PhD, MB Mamatha Bhat, MD, PhD |
| author_sort | Xun Zhao, MD |
| collection | DOAJ |
| description | Background. Liver transplantation is essential for many people with primary sclerosing cholangitis (PSC). People with PSC are less likely to receive a deceased donor liver transplant compared with other causes of chronic liver disease. This disparity may stem from the inaccuracy of the model for end-stage liver disease (MELD) in predicting waitlist mortality or dropout for PSC. The broad applicability of MELD across many causes comes at the expense of accuracy in prediction for certain causes that involve unique comorbidities. We aimed to develop a model that could more accurately predict dynamic changes in waitlist outcomes among patients with PSC while including complex clinical variables.
Methods. We developed 3 machine learning architectures using data from 4666 patients with PSC in the Scientific Registry of Transplant Recipients (SRTR) and tested our models on our institutional data set of 144 patients at the University Health Network (UHN). We evaluated their time-dependent concordance index (C-index) for mortality prediction and compared it against MELD-sodium and MELD 3.0.
Results. Random survival forest (RSF), a decision tree–based survival model, outperformed MELD-sodium and MELD 3.0 in both the SRTR and the UHN test data set using the same bloodwork variables and readily available demographic data. It achieved a C-index of 0.868 (SD 0.020) and 0.771 (SD 0.085) on the SRTR and UHN test data, respectively. Training a separate RSF model using the UHN data with PSC-specific achieved a C-index of 0.91. In addition to high MELD score, increased white blood cells, time on the waiting list, platelet count, presence of Autoimmune hepatitis-PSC overlap, aspartate aminotransferase, female sex, age, history of stricture dilation, and extremes of body weight were the top-ranked features predictive of the outcomes.
Conclusions. Our RSF model offers more accurate waitlist outcome prediction in PSC. The significant performance improvement with the inclusion of PSC-specific variables highlights the importance of disease-specific variables for predicting trajectories of clinically distinct presentations. |
| format | Article |
| id | doaj-art-62df13c145474c16adaf9dafa4dc206f |
| institution | OA Journals |
| issn | 2373-8731 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wolters Kluwer |
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| series | Transplantation Direct |
| spelling | doaj-art-62df13c145474c16adaf9dafa4dc206f2025-08-20T02:30:06ZengWolters KluwerTransplantation Direct2373-87312025-04-01114e177410.1097/TXD.0000000000001774202504000-00009Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing CholangitisXun Zhao, MD0Maryam Naghibzadeh, MD1Yingji Sun, MSc2Arya Rahmani, BSc3Leslie Lilly, MD4Nazia Selzner, MD, PhD5Cynthia Tsien, MD, MPH6Elmar Jaeckel, MD7Mary Pressley Vyas8Rahul Krishnan, PhD9Gideon Hirschfield, MD, PhD, MB10Mamatha Bhat, MD, PhD111 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.3 PSC Partners Seeking a Cure Canada, Toronto, ON, Canada.4 Department of Computer Science, University of Toronto, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.1 Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.Background. Liver transplantation is essential for many people with primary sclerosing cholangitis (PSC). People with PSC are less likely to receive a deceased donor liver transplant compared with other causes of chronic liver disease. This disparity may stem from the inaccuracy of the model for end-stage liver disease (MELD) in predicting waitlist mortality or dropout for PSC. The broad applicability of MELD across many causes comes at the expense of accuracy in prediction for certain causes that involve unique comorbidities. We aimed to develop a model that could more accurately predict dynamic changes in waitlist outcomes among patients with PSC while including complex clinical variables. Methods. We developed 3 machine learning architectures using data from 4666 patients with PSC in the Scientific Registry of Transplant Recipients (SRTR) and tested our models on our institutional data set of 144 patients at the University Health Network (UHN). We evaluated their time-dependent concordance index (C-index) for mortality prediction and compared it against MELD-sodium and MELD 3.0. Results. Random survival forest (RSF), a decision tree–based survival model, outperformed MELD-sodium and MELD 3.0 in both the SRTR and the UHN test data set using the same bloodwork variables and readily available demographic data. It achieved a C-index of 0.868 (SD 0.020) and 0.771 (SD 0.085) on the SRTR and UHN test data, respectively. Training a separate RSF model using the UHN data with PSC-specific achieved a C-index of 0.91. In addition to high MELD score, increased white blood cells, time on the waiting list, platelet count, presence of Autoimmune hepatitis-PSC overlap, aspartate aminotransferase, female sex, age, history of stricture dilation, and extremes of body weight were the top-ranked features predictive of the outcomes. Conclusions. Our RSF model offers more accurate waitlist outcome prediction in PSC. The significant performance improvement with the inclusion of PSC-specific variables highlights the importance of disease-specific variables for predicting trajectories of clinically distinct presentations.http://journals.lww.com/transplantationdirect/fulltext/10.1097/TXD.0000000000001774 |
| spellingShingle | Xun Zhao, MD Maryam Naghibzadeh, MD Yingji Sun, MSc Arya Rahmani, BSc Leslie Lilly, MD Nazia Selzner, MD, PhD Cynthia Tsien, MD, MPH Elmar Jaeckel, MD Mary Pressley Vyas Rahul Krishnan, PhD Gideon Hirschfield, MD, PhD, MB Mamatha Bhat, MD, PhD Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis Transplantation Direct |
| title | Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis |
| title_full | Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis |
| title_fullStr | Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis |
| title_full_unstemmed | Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis |
| title_short | Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis |
| title_sort | machine learning prediction model of waitlist outcomes in patients with primary sclerosing cholangitis |
| url | http://journals.lww.com/transplantationdirect/fulltext/10.1097/TXD.0000000000001774 |
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