ArticleThe Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation2022
The lung allocation score and other available models lack predictive accuracy for post-lung transplant survival.
Article in The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Inconsistent values and algorithmic fairness: a review of organ allocation priority systems in the United States.BMC medical ethics · 2024Pooled it
- XGBoost Outperforms Traditional Transplant Regulatory Models Based on Regression.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026Article
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Early identification of high-risk individuals for mortality after lung transplantation: A retrospective cohort study with topological feature engineering.PLOS digital health · 2026Article
- Transforming lung transplantation with artificial intelligence: a narrative review from organ allocation to post-transplant management.Journal of thoracic disease · 2026Review
- Variability in Lung Transplant Clinical Practices in Canada: Results of a National Survey.Transplantation direct · 2026Article
- Article
- Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.Journal of Korean medical science · 2025Article
- Article
- Machine Learning for 1-Year Mortality Prediction in Lung Transplant Recipients: ISHLT Registry.Transplant international : official journal of the European Society for Organ Transplantation · 2025Article
- Application of artificial intelligence and machine learning in lung transplantation: a comprehensive review.Frontiers in digital health · 2025Review
- Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Use of Predictive Models to Determine Transplant Eligibility.Current transplantation reports · 2024Article
- Pushing the Survival Bar Higher: Two Decades of Innovation in Lung Transplantation.Journal of clinical medicine · 2024Review
- Cell-Free DNA Maps Tissue Injury and Correlates with Disease Severity in Lung Transplant Candidates.American journal of respiratory and critical care medicine · 2024Article
- The transformative potential of artificial intelligence in solid organ transplantation.Frontiers in transplantation · 2024Review
- Venopulmonary extracorporeal membrane oxygenation for right ventricular support as a bridge to lung transplantation: A narrative review.SAGE open medicine · 2024Review
- Lung Transplantation in a New Era in the Field of Cystic Fibrosis.Life (Basel, Switzerland) · 2023Review
- Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.JAMA network open · 2023Article
- Systems prediction of chronic lung allograft dysfunction: Results and perspectives from the Cohort of Lung Transplantation and Systems prediction of Chronic Lung Allograft Dysfunction cohorts.Frontiers in medicine · 2023Review
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Authors and funding
7 authors.
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Abstract
backgroundImproved predictive models are needed in lung transplantation in the setting of a proposed allocation system that incorporates longer-term post-transplant survival in the United States. Allocation systems require accurate mortality predictions to justly allocate organs.
methodsUtilizing the United Network for Organ Sharing database (2005-2017), we fit models to predict 1-year mortality based on the Lung Allocation Score (LAS), the Chan, et al, 2019 model, a novel "clinician" model (a priori clinician selection of pre-transplant covariates), and two machine learning models (Least Absolute Shrinkage and Selection Operator; LASSO and Random Forests) for predicting 1-year and 3-year post-transplant mortality. We compared predictive accuracy among models. We evaluated the calibration of models by comparing average predicted probability vs observed outcome per decile. We repeated analyses fit for 3-year mortality, disease category, including donor covariates, and LAS era.
resultsThe area under the cure for all models was low, ranging from 0.55 to 0.62. All exhibited reasonable negative predictive values (0.87-0.90), but the positive predictive value for was poor (all <0.25). Evaluating LAS calibration found 1-year post-transplant estimates consistently overestimated risk of mortality, with greater differences in higher deciles. LASSO, Random Forests, and clinician models showed no improvement when evaluated by disease category or with the addition of donor covariates and performed worse for 3-year outcomes.
conclusionsThe LAS overestimated patients' risk of post-transplant death, thus underestimating transplant benefit in the sickest candidates. Novel models based on pre-transplant recipient covariates failed to improve prediction. There should be wariness in post-transplant survival predictions from available models.
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