Machine Learning Framework for Longitudinal Functional Decline and Time-to-Event Prediction

Harnessing longitudinal data for time to event analysis can provide valuable insights into disease progression and help plan clinical interventions for individual patients, with the goal of improving clinical outcomes and quality of life. However, real-world clinical data is characterised by missingness, inconsistencies and heterogeneity, especially when datasets are aggregated from different sources. Here, we propose a robust methodological framework to tackle the above challenges and apply it to time to gastrostomy prediction for amyotrophic lateral sclerosis (ALS) patients. Data from 8,586 ALS patients were extracted from three independent cohorts. We determined classes for time-dependent measures of patient decline using joint latent class growth analysis–discrete time survival analysis (LCGA-DTSA). For new patients, individual trajectories of functional decline were mapped using Fréchet distances. Survival and machine learning approaches (Cox Proportinal Hazards, Cox XGBoost, and XGboost Pseudo-Observation Regression) using baseline and longitudinal features were evaluated for predicting time-to-gastrostomy. The best-performing time-to-gastrostomy model was integrated with a time-to-death survival model to provide an overall confidence label. We found that the joint LCGA-DTSA enables clear patient stratification by functional decline. The prediction models indicated that rapid decline classes for ALSFRS-R (ALS Functional Rating Scale Revised) bulbar subscore and swallow function are the most important factors determining time-to-gastrostomy insertion. Further, we determined that XGBoost MAEPO model applied on longitudinal features extracted via LCGA-DTSA algorithms outperform every other model in absolute error terms, whilst still providing strong concordance index. Predictions are accompanied with a percentage confidence describing the likelihood of gastrostomy insertion happening given predicted survival time. Longitudinal trajectories of functional decline can contain crucial information for time-to-event prediction. For declining conditions, such as ALS, appropriate integration of time-to-intervention models with overall survival models could also help inform clinical care and shared decision-making.