Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction
Abstract Background To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients. Methods This retrospective cohort study analysed 173 allogeneic LT recipients at the Aff...
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BMC
2025-08-01
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| Series: | European Journal of Medical Research |
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| Online Access: | https://doi.org/10.1186/s40001-025-03021-4 |
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| author | Kui Tu Dan Luo Xuanyu Gu Jichang Jiang Zhihong Zheng Lijin Zhao |
| author_facet | Kui Tu Dan Luo Xuanyu Gu Jichang Jiang Zhihong Zheng Lijin Zhao |
| author_sort | Kui Tu |
| collection | DOAJ |
| description | Abstract Background To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients. Methods This retrospective cohort study analysed 173 allogeneic LT recipients at the Affiliated Hospital of Zunyi Medical University between August 2019 and December 2023. Clinical and biochemical variables were systematically collected, including recipient profiles [age, gender, prior abdominal surgery Performance Status (PS) scores], biochemical markers (serum creatinine, sodium, albumin, total bilirubin, neutrophil/lymphocyte counts), and prognostic scores [Model for End-Stage Liver Disease (MELD), MELD–sodium (MELD–Na), Child–Turcotte–Pugh (CTP), neutrophil-to-lymphocyte ratio (NLR), and albumin–bilirubin (ALBI)]. Intraoperative metrics, such as blood loss volume and anhepatic phase duration, were also recorded. Univariate and multivariate Cox regression identified mortality predictors. LASSO-regularised Cox regression facilitated variable selection and nomogram construction. Internal validation used decision curve analysis (quantifying clinical net benefit) and time-dependent receiver operating characteristic (ROC) curve analysis [12/18/24-month area under the curve (AUC)]. Kaplan–Meier survival analysis stratified patients into tertiles. Results Univariate analysis identified MELD score > 25, blood loss > 5 L, PS score, neutrophil count, total bilirubin level, and MELD–Na score as significant predictors (p < 0.05). Multivariate Cox regression confirmed massive haemorrhage (> 5 L) as an independent mortality predictor (p < 0.001). LASSO-selected predictors (prior abdominal surgery, blood loss > 5 L, and ALBI score) formed a prognostic nomogram demonstrating strong discrimination (1-year AUC: 0.824; 2-year AUC: 0.788). Tertile-based stratification revealed significant intergroup differences in survival (p < 0.001). Conclusions Massive intraoperative haemorrhage independently predicted post-LT mortality. The validated nomogram integrating surgical history, haemorrhage severity, and ALBI score enables clinically actionable risk stratification, potentially informing perioperative resource allocation and personalised management protocols. |
| format | Article |
| id | doaj-art-6febf9cbabcb454586d60249b38ce296 |
| institution | Kabale University |
| issn | 2047-783X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | European Journal of Medical Research |
| spelling | doaj-art-6febf9cbabcb454586d60249b38ce2962025-08-24T11:14:53ZengBMCEuropean Journal of Medical Research2047-783X2025-08-013011910.1186/s40001-025-03021-4Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram constructionKui Tu0Dan Luo1Xuanyu Gu2Jichang Jiang3Zhihong Zheng4Lijin Zhao5Affiliated Hospital of Zunyi Medical UniversityThe Second Affiliated Hospital of Zunyi Medical UniversityAffiliated Hospital of Zunyi Medical UniversityAffiliated Hospital of Zunyi Medical UniversityAffiliated Hospital of Zunyi Medical UniversityAffiliated Hospital of Zunyi Medical UniversityAbstract Background To identify risk factors for post-transplant mortality and develop a machine learning-integrated prognostic tool to optimise clinical decision-making in liver transplantation (LT) recipients. Methods This retrospective cohort study analysed 173 allogeneic LT recipients at the Affiliated Hospital of Zunyi Medical University between August 2019 and December 2023. Clinical and biochemical variables were systematically collected, including recipient profiles [age, gender, prior abdominal surgery Performance Status (PS) scores], biochemical markers (serum creatinine, sodium, albumin, total bilirubin, neutrophil/lymphocyte counts), and prognostic scores [Model for End-Stage Liver Disease (MELD), MELD–sodium (MELD–Na), Child–Turcotte–Pugh (CTP), neutrophil-to-lymphocyte ratio (NLR), and albumin–bilirubin (ALBI)]. Intraoperative metrics, such as blood loss volume and anhepatic phase duration, were also recorded. Univariate and multivariate Cox regression identified mortality predictors. LASSO-regularised Cox regression facilitated variable selection and nomogram construction. Internal validation used decision curve analysis (quantifying clinical net benefit) and time-dependent receiver operating characteristic (ROC) curve analysis [12/18/24-month area under the curve (AUC)]. Kaplan–Meier survival analysis stratified patients into tertiles. Results Univariate analysis identified MELD score > 25, blood loss > 5 L, PS score, neutrophil count, total bilirubin level, and MELD–Na score as significant predictors (p < 0.05). Multivariate Cox regression confirmed massive haemorrhage (> 5 L) as an independent mortality predictor (p < 0.001). LASSO-selected predictors (prior abdominal surgery, blood loss > 5 L, and ALBI score) formed a prognostic nomogram demonstrating strong discrimination (1-year AUC: 0.824; 2-year AUC: 0.788). Tertile-based stratification revealed significant intergroup differences in survival (p < 0.001). Conclusions Massive intraoperative haemorrhage independently predicted post-LT mortality. The validated nomogram integrating surgical history, haemorrhage severity, and ALBI score enables clinically actionable risk stratification, potentially informing perioperative resource allocation and personalised management protocols.https://doi.org/10.1186/s40001-025-03021-4Liver transplantationRisk factorsMachine learningTransplantation prognosisSurvival prediction |
| spellingShingle | Kui Tu Dan Luo Xuanyu Gu Jichang Jiang Zhihong Zheng Lijin Zhao Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction European Journal of Medical Research Liver transplantation Risk factors Machine learning Transplantation prognosis Survival prediction |
| title | Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| title_full | Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| title_fullStr | Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| title_full_unstemmed | Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| title_short | Predicting post-liver transplantation mortality: a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| title_sort | predicting post liver transplantation mortality a retrospective cohort study on risk factor identification and prognostic nomogram construction |
| topic | Liver transplantation Risk factors Machine learning Transplantation prognosis Survival prediction |
| url | https://doi.org/10.1186/s40001-025-03021-4 |
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