ArticleTransplantation direct2019
Predictive Capacity of Risk Models in Liver Transplantation.
Article in Transplantation direct, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Myosteatosis as a prognostic biomarker for post-transplant survival and cardiometabolic outcomes: A retrospective study.Canadian liver journal · 2026Article
- Preoperative Portal Vein Thrombosis Grading Predicts Post-Transplant Thrombosis and Survival.Therapeutics and clinical risk management · 2026Article
- Effect of Surgeon Service Volume on Survival of Liver Transplant Patients: A Nationwide Cohort Study.Journal of clinical medicine · 2026Article
- Association of Functional, Academic, Motor, and Cognitive Deficits in Graft Failure in Pediatric Liver Transplantation.Clinical transplantation · 2025Article
- Improving lung cancer diagnosis and survival prediction with deep learning and CT imaging.PloS one · 2025Article
- Influence of donor age on liver transplantation outcomes: A multivariate analysis and comparative study.World journal of gastrointestinal surgery · 2024Article
- Donor characteristics and their impact on kidney transplantation outcomes: Results from two nationwide instrumental variable analyses based on outcomes of donor kidney pairs accepted for transplantation.EClinicalMedicine · 2022Article
- BAR Score Performance in Predicting Survival after Living Donor Liver Transplantation: A Single-Center Retrospective Study.Canadian journal of gastroenterology & hepatology · 2022Article
- Short- and Long-Term Outcomes of Different Reperfusion Sequences in Liver Transplantation.Annals of transplantation · 2021Article
- An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction.Frontiers in oncology · 2021Article
- Treatment of Anti-HLA Donor-Specific Antibodies Results in Increased Infectious Complications and Impairs Survival after Liver Transplantation.Journal of clinical medicine · 2020Article
- Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques.BMC medical research methodology · 2020Article
- Liver transplantation in elderly patients: what do we know at the beginning of 2020?Surgery today · 2020Review
- Transplanting Marginal Organs in the Era of Modern Machine Perfusion and Advanced Organ Monitoring.Frontiers in immunology · 2020Review
- Outcomes of liver transplant recipients with high MELD scores: an experience from a Canadian centre.Canadian journal of surgery. Journal canadien de chirurgieArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral risk models to predict outcome after liver transplantation (LT) have been developed in the last decade. This study compares the predictive performance of 7 risk models.
methodsData on 62 294 deceased donor LTs performed in recipients ≥18 years old between January 2005 and December 2015 in the United Network for Organ Sharing region were used for this study. The balance of risk, donor risk index (DRI), Eurotransplant-DRI, donor-to-recipient model (DRM), simplified recipient risk index, Survival Outcomes Following Liver Transplantation (SOFT), and donor Model for End-stage Liver Disease scores were calculated, and calibration and discrimination were evaluated for patient, overall graft, and death-censored graft survival. Calibration was evaluated by outcome of high-risk transplantations (>80th percentile of the respective risk score) and discrimination by concordance index (c-index).
resultsPatient survival at 3 months was best predicted by the SOFT (c-index: 0.68) and Balance of Risk score (c-index: 0.64), while the DRM and SOFT score had the highest predictive capacity at 60 months (c-index: 0.59). Overall, graft survival was best predicted by the SOFT score at 3-month follow-up (c-index: 0.65) and by the SOFT and DRM at 60-month follow-up (c-index: 0.58). Death-censored graft survival at 60-month follow-up is best predicted by the DRI (c-index: 0.59) and Eurotransplant-DRI (c-index: 0.58). For patient and overall graft survival, high-risk transplantations were best defined by the DRM. For death-censored graft survival, this was best defined by the DRI.
conclusionsThis study shows that models dominated by recipient factors have the best performance for short-term patient survival. Models that also include sufficient donor factors have better performance for long-term graft survival. Death-censored graft survival is best predicted by models that predominantly included donor factors.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.