Development and validation of primary graft dysfunction predictive algorithm for lung transplant candidates

Abstract

Background: Primary graft dysfunction (PGD) is the leading cause of early morbidity and mortality after lung transplantation. Accurate prediction of PGD risk could inform donor approaches and perioperative care planning. We sought to develop a clinically useful, generalizable PGD prediction model to aid in transplant decision-making.

Methods: We derived a predictive model in a prospective cohort study of subjects from 2012 to 2018, followed by a single-center external validation. We used regularized (lasso) logistic regression to evaluate the predictive ability of clinically available PGD predictors and developed a user interface for clinical application. Using decision curve analysis, we quantified the net benefit of the model across a range of PGD risk thresholds and assessed model calibration and discrimination.

Results: The PGD predictive model included distance from donor hospital to recipient transplant center, recipient age, predicted total lung capacity, lung allocation score (LAS), body mass index, pulmonary artery mean pressure, sex, and indication for transplant; donor age, sex, mechanism of death, and donor smoking status; and interaction terms for LAS and donor distance. The interface allows for real-time assessment of PGD risk for any donor/recipient combination. The model offers decision-making net benefit in the PGD risk range of 10% to 75% in the derivation centers and 2% to 10% in the validation cohort, a range incorporating the incidence in that cohort.

Conclusion: We developed a clinically useful PGD predictive algorithm across a range of PGD risk thresholds to support transplant decision-making, posttransplant care, and enrich samples for PGD treatment trials.

Keywords: donor; lung transplantation; prediction; primary graft dysfunction; recipient.

Figure 1.

Predictive performance of the model for all transplant centers. Net benefit compares the number of correct predictions (true positive predictions) with the number of cases of positive predictions among those without PGD, across all possible risk thresholds. Over a wide range of risk thresholds, the net benefit of using the prediction model (blue dotted line) is higher than alternative decision rules of treating all patients as high risk (solid black curve) or at low risk (red dot-dash line). The area between the curves represents the improved risk determination from use of the prediction model. The model demonstrates net benefit in the PGD risk threshold ranges of 10-75%.

Figure 2.

Calibration curve for prediction model for primary graft dysfunction (PGD) for all transplant centers. Ideally the calibration curve (smooth curve) should coincide with the 45 degree (dotted) line that equates expected (E) and observed (O) risk, CITL (calibration in the large) should be 0.0 and the curve slope should be 1.0. The model performance is only slightly deflected downward, suggesting a small degree upward deflection of predicted risk, a conservative result for clinical applications. The AUC =0.76 (c-statistic), indicating a reasonable level of model discrimination, the ability of the model to distinguish higher from lower PGD risk. Reasonable discrimination is also reflected in the distribution of predictions by PGD status along the x-axis, where the predictions for PGD=0 are lower than those for the patients with PGD=1.

Figure 3.

Decision curve for the validation sample (n=321). The model demonstrates net benefit in the PGD incidence range 2-10%, consistent with the observed PGD incidence at this center (7%).

Figure 4.

Calibration curve for the validation center sample. Ideally the calibration curve (smooth curve) should coincide with the 45 degree (dotted) line that equates expected (E) and observed (O) risk, CITL (calibration in the large) should be 0.0 and the curve slope should be 1.0. The model demonstrated reasonable calibration (E/O=1.6) and discrimination (c=0.66).