Predicting Individualized Lung Disease Progression in Treatment-Naive Patients With Lymphangioleiomyomatosis.

Background: Lung function decline varies significantly in patients with lymphangioleiomyomatosis (LAM), impeding individualized clinical decision-making.

Research Question: Can we aid individualized decision-making in LAM by developing a dynamic prediction model that can estimate the probability of clinically relevant FEV decline in patients with LAM before treatment initiation?

Study Design And Methods: Patients observed in the US National Heart, Lung, and Blood Institute (NHLBI) Lymphangioleiomyomatosis Registry were included. Using routinely available variables such as age at diagnosis, menopausal status, and baseline lung function (FEV and diffusing capacity of the lungs for carbon monoxide [Dlco]), we used novel stochastic modeling and evaluated predictive probabilities for clinically relevant drops in FEV. We formed predictive probabilities of transplant-free survival by jointly modeling longitudinal FEV and lung transplantation or death events. External validation used the UK Lymphangioleiomyomatosis Natural History cohort.

Results: Analysis of the NHLBI Lymphangioleiomyomatosis Registry and UK Lymphangioleiomyomatosis Natural History cohorts consisted of 216 and 185 individuals, respectively. We derived a joint model that accurately estimated the risk of future lung function decline and 5-year probabilities of transplant-free survival in patients with LAM not taking sirolimus (area under the receiver operating characteristic curve [AUC], approximately 0.80). The prediction model provided estimates of forecasted FEV, rate of FEV decline, and probabilities for risk of prolonged drops in FEV for untreated patients with LAM with a high degree of accuracy (AUC > 0.80) for the derivation cohort as well as the validation cohort. Our tool is freely accessible at: https://anushkapalipana.shinyapps.io/testapp_v2/.

Interpretation: Longitudinal modeling of routine clinical data can allow individualized LAM prognostication and assist in decision-making regarding the timing of treatment initiation.