The lung allocation score and other available models lack predictive accuracy for post-lung transplant survival

Brahmbhatt, Jay M.; Wai, Travis Hee; Goss, Christopher H.; Lease, Erika D.; Merlo, Christian A.; Kapnadak, Siddhartha G.; Ramos, Kathleen J.

doi:10.1016/j.healun.2022.05.008

Cited by 15 publications

(10 citation statements)

References 31 publications

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“…A well-validated prognostic model could integrate various prognostic factors and produce an individualized survival prediction. For patients who have undergone LTx, several reports have presented results of studying prognostic models to predict OS or survival at specific time points . However, these models showed a limited performance, which could not realize an accurate prediction.…”

Section: Discussionmentioning

confidence: 99%

“…5 Reports about prognostic models for LTx recipients are rare, and models have delivered unsatisfactory performance in predicting the survival outcome of patients. [6][7][8][9] Therefore, a personalized and wellperforming prognostic tool is necessary for patients after LTx, although one is not yet available.…”

Section: Introductionmentioning

confidence: 99%

“…A prediction model developed by traditional regression analysis or a machine learning approach could integrate a mass of prognostic parameters and provide individual survival prediction . Reports about prognostic models for LTx recipients are rare, and models have delivered unsatisfactory performance in predicting the survival outcome of patients . Therefore, a personalized and well-performing prognostic tool is necessary for patients after LTx, although one is not yet available.…”

Section: Introductionmentioning

confidence: 99%

“… 5 Reports about prognostic models for LTx recipients are rare, and models have delivered unsatisfactory performance in predicting the survival outcome of patients. 6 , 7 , 8 , 9 Therefore, a personalized and well-performing prognostic tool is necessary for patients after LTx, although one is not yet available. Random survival forests (RSF), as part of machine learning algorithms, are designed to be used specifically for survival outcome prediction and have shown promising performance in our previous study.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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ImportanceAlthough numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.ObjectiveTo develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.Design, Setting, and ParticipantsThis retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020. The LTx recipients were randomly assigned to training and test sets in accordance with a ratio of 7:3. Feature selection was performed using variable importance with bootstrapping resampling. The prognostic model was fitted using the RSF algorithm, and a Cox regression model was set as a benchmark. The integrated area under the curve (iAUC) and integrated Brier score (iBS) were applied to assess model performance in the test set. Data were analyzed from January 2017 to December 2019.Main Outcomes And MeasuresOverall survival in patients after LTx.ResultsA total of 504 patients were eligible for this study, consisting of 353 patients in the training set (mean [SD] age, 55.03 [12.78] years; 235 [66.6%] male patients) and 151 patients in the test set (mean [SD] age, 56.79 [10.95] years; 99 [65.6%] male patients). According to the variable importance of each factor, 16 were selected for the final RSF model, and postoperative extracorporeal membrane oxygenation time was identified as the most valuable factor. The RSF model had excellent performance with an iAUC of 0.879 (95% CI, 0.832-0.921) and an iBS of 0.130 (95% CI, 0.106-0.154). The Cox regression model fitted by the same modeling factors to the RSF model was significantly inferior to the RSF model with an iAUC of 0.658 (95% CI, 0.572-0.747; P &lt; .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P &lt; .001). According to the RSF model predictions, the patients after LTx were stratified into 2 prognostic groups displaying significant difference, with mean overall survival of 52.91 months (95% CI, 48.51-57.32) and 14.83 months (95% CI, 9.44-20.22; log-rank P &lt; .001), respectively.Conclusions and relevanceIn this prognostic study, the findings first demonstrated that RSF could provide more accurate overall survival prediction and remarkable prognostic stratification than the Cox regression model for patients after LTx.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning–Based Prognostic Model for Patients After Lung Transplantation

et al. 2023

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“…( 2 ). The lung allocation score has been shown to be unable to predict death after lung transplantation ( 64 ).…”

Section: Discussionmentioning

confidence: 99%

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

Pison

Tissot²,

Bernasconi³

et al. 2023

Front. Med.

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BackgroundChronic lung allograft dysfunction (CLAD) is the leading cause of poor long-term survival after lung transplantation (LT). Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) aimed to predict CLAD.MethodsTo predict CLAD, we investigated the clinicome of patients with LT; the exposome through assessment of airway microbiota in bronchoalveolar lavage cells and air pollution studies; the immunome with works on activation of dendritic cells, the role of T cells to promote the secretion of matrix metalloproteinase-9, and subpopulations of T and B cells; genome polymorphisms; blood transcriptome; plasma proteome studies and assessment of MSK1 expression.ResultsClinicome: the best multivariate logistic regression analysis model for early-onset CLAD in 422 LT eligible patients generated a ROC curve with an area under the curve of 0.77. Exposome: chronic exposure to air pollutants appears deleterious on lung function levels in LT recipients (LTRs), might be modified by macrolides, and increases mortality. Our findings established a link between the lung microbial ecosystem, human lung function, and clinical stability post-transplant. Immunome: a decreased expression of CLEC1A in human lung transplants is predictive of the development of chronic rejection and associated with a higher level of interleukin 17A; Immune cells support airway remodeling through the production of plasma MMP-9 levels, a potential predictive biomarker of CLAD. Blood CD9-expressing B cells appear to favor the maintenance of long-term stable graft function and are a potential new predictive biomarker of BOS-free survival. An early increase of blood CD4 + CD57 + ILT2+ T cells after LT may be associated with CLAD onset. Genome: Donor Club cell secretory protein G38A polymorphism is associated with a decreased risk of severe primary graft dysfunction after LT. Transcriptome: blood POU class 2 associating factor 1, T-cell leukemia/lymphoma domain, and B cell lymphocytes, were validated as predictive biomarkers of CLAD phenotypes more than 6 months before diagnosis. Proteome: blood A2MG is an independent predictor of CLAD, and MSK1 kinase overexpression is either a marker or a potential therapeutic target in CLAD.ConclusionSystems prediction of Chronic Lung Allograft Dysfunction generated multiple fingerprints that enabled the development of predictors of CLAD. These results open the way to the integration of these fingerprints into a predictive handprint.

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