Post–Liver Transplant Outcomes: A Comparative Study of 6 Predictive Models
Background. We compared the performance of the Liver Transplant Risk Score (LTRS) with the survival outcomes following liver transplantation (SOFT), pretransplant SOFT (P-SOFT), Balance of Risk Score (BAR), donor-age and model for end-stage liver disease (D-MELD), and Organ Procurement and Transplan...
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wolters Kluwer
2024-12-01
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| Series: | Transplantation Direct |
| Online Access: | http://journals.lww.com/transplantationdirect/fulltext/10.1097/TXD.0000000000001724 |
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| Summary: | Background. We compared the performance of the Liver Transplant Risk Score (LTRS) with the survival outcomes following liver transplantation (SOFT), pretransplant SOFT (P-SOFT), Balance of Risk Score (BAR), donor-age and model for end-stage liver disease (D-MELD), and Organ Procurement and Transplantation Network Risk Prediction Score (ORPS) for the prediction of 90-d mortality, 1-y mortality, and 5-y survival after first-time liver transplantation (LT).
Methods. A retrospective analysis of the Scientific Registry of Transplant Recipients was conducted using data collected between 2002 and 2021.
Results. A total of 82 696 adult LT recipients with a median age of 56 y were included. The area under the curve for 90-d mortality were 0.61, 0.66, 0.65, 0.61, 0.58, and 0.56 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: P < 0.05). The area under the curve for 1-y mortality were 0.60, 0.63, 0.62, 0.59, 0.60, 0.57, and 0.59 for the LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS, respectively (all pairwise comparisons: P < 0.05). The c-statistics for 5-y survival were not statistically significant among the models. For 90-d mortality, 1-y mortality, and 5-y survival, the correlation coefficients between the LTRS and P-SOFT (the 2 models requiring only preoperative parameters) were 0.90. 0.91, and 0.81, respectively (P < 0.01).
Conclusions. None of the predictive models demonstrated sufficient precision to reliably identify LT recipients who died within 90 d and 1 y after LT. However, all models exhibited strong capabilities in perioperative risk stratification. Notably, the P-SOFT and LTRS models, the 2 models that can be calculated using only preoperative data, proved to be valuable tools for identifying candidates at a significant risk of poor outcomes. |
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| ISSN: | 2373-8731 |