Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Rationale: Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increased mortality. Early identification of these high-risk patients and accurate prediction of the onset of these important events is important to determine treatment strategies. Although various factors that affect disease behavior among patients with ILD hinder the accurate prediction of these events, the use of longitudinal information may enable better prediction.

Objectives: To develop a deep-learning (DL) model to predict composite outcomes defined as the first occurrence of AE-ILD and mortality using longitudinal data.

Methods: Longitudinal clinical and environmental data were retrospectively collected from consecutive patients with ILD at two specialty centers between January 2008 and December 2015. A DL model was developed to predict composite outcomes using longitudinal data from 80% of patients from the first center, which was then validated using data from the remaining 20% patients and second center. The developed model was compared with the univariate Cox proportional hazard (CPH) model using the ILD gender-age-physiology (ILD-GAP) score and multivariate CPH model at the time of ILD diagnosis.

Measurements and main results: AE-ILD was reported in 218 patients among the 1,175 patients enrolled, whereas 380 died without developing AE-ILD. The truncated concordance index (C-index) values of univariate/multivariate CPH models for composite outcomes within 12, 24, and 36 months after prediction were 0.789/0.843, 0.788/0.853, and 0.787/0.853 in internal validation, and 0.650/0.718, 0.652/0.756, and 0.640/0.756 in external validation, respectively. At 12 months after ILD diagnosis, the DL model outperformed the univariate CPH model and multivariate CPH model for composite outcomes within 12 months, with C-index values of 0.842, 0.840, and 0.839 in internal validation, and 0.803, 0.744, and 0.746 in external validation, respectively. Neutrophils, C-reactive protein, ILD-GAP score, and exposure to suspended particulate matter were strongly associated with the composite outcomes.

Conclusions: The DL model can accurately predict the incidence of AE-ILD or mortality using longitudinal data.