The Houston Methodist Lung Transplant Risk Model: A Validated Tool for Pretransplant Risk Assessment

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation

Importance

Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Objective

To 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 Participants

This 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 Measures

Overall survival in patients after LTx.

Results

A 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 < .001) and an iBS of 0.205 (95% CI, 0.176-0.233; P < .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 < .001), respectively.

Conclusions and relevance

In 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.

Progress in Combined Liver-lung Transplantation at a Single Center

Combined liver-lung transplantation is an uncommon, although vital, procedure for patients with simultaneous end-stage lung and liver disease. The utility of lung-liver transplant has been questioned because of initial poor survival outcomes, particularly when compared with liver-alone transplant recipients.

Methods

A single-center, retrospective review of the medical records of 19 adult lung-liver transplant recipients was conducted, comparing early recipients (2009-2014) with a recent cohort (2015-2021). Patients were also compared with the center's single lung or liver transplant recipients.

Results

Recent lung-liver recipients were older (P = 0.004), had a higher body mass index (P = 0.03), and were less likely to have ascites (P = 0.02), reflecting changes in the etiologies of lung and liver disease. Liver cold ischemia time was longer in the modern cohort (P = 0.004), and patients had a longer posttransplant length of hospitalization (P = 0.048). Overall survival was not statistically different between the 2 eras studied (P = 0.61), although 1-y survival was higher in the more recent group (90.9% versus 62.5%). Overall survival after lung-liver transplant was equivalent to lung-alone recipients and was significantly lower than liver-alone recipients (5-y survival: 52%, 51%, and 75%, respectively). Lung-liver recipient mortality was primarily driven by deaths within 6 mo of transplant due to infection and sepsis. Graft failure was not significantly different (liver: P = 0.06; lung: P = 0.74).

Conclusions

The severity of illness in lung-liver recipients combined with the infrequency of the procedure supports its continued use. However, particular attention should be paid to patient selection, immunosuppression, and prophylaxis against infection to ensure proper utilization of scarce donor organs.

The Lung Allocation Score and Other Available Models Lack Predictive Accuracy for Post-Lung Transplant Survival

BACKGROUND

Improved 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.

METHODS

Utilizing 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.

RESULTS

The 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.

CONCLUSIONS

The 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.

The Year in Cardiothoracic Transplantation Anesthesia: Selected Highlights from 2019

The highlights in cardiothoracic transplantation focus on the recent research pertaining to heart and lung transplantation, including expansion of the donor pool, the optimization of donors and recipients, the use of mechanical support, the perioperative and long-term outcomes in these patient populations, and the use of transthoracic echocardiography to diagnose rejection.