Individualized predictions for clinical milestone in amyotrophic lateral sclerosis: A multialgorithmic approach

Abstract

Objective The phenotypic heterogeneity and complex disease trajectory complicate the ability to predict specific clinical milestone for individual patients with amyotrophic lateral sclerosis (ALS). Here we developed individualized prediction models to estimate the time to the loss of autonomy in swallowing function. Methods Utilizing the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we built three models of distinct time-to-event prediction algorithms: accelerated failure time (AFT), cox proportional hazard (COX) and random survival forest (RSF) for an individualized risk assessment of the swallowing milestone. The target variable was defined as the time to a decline in the ALSFRS-R swallowing item score to 1 or below, indicating a need for supplementary tube feeding. Results Internal cross-validation revealed the median concordance index (C-index) of 0.851 (IQR, 0.842–0.859) for AFT, 0.850 (0.841–0.859) for COX and 0.846 (0.839–0.854) for RSF, and all models demonstrated good distributional calibration with predicted and observed event probabilities closely matched across different time intervals. For external validation with a registry dataset with characteristics different from PRO-ACT, the discriminative power was replicated with comparable C-indices for all models, whereas the calibration revealed a left-skewed distribution suggesting a bias towards overestimation of event probabilities in real-world data. While all models were effective at stratifying patients, the results of RSF model, unlike AFT and COX, did not match well with the KM curves of the corresponding risk groups, supporting the importance of nuanced understanding of data structure and algorithmic properties. Conclusion Our models are implemented into a web application which could be applied to individualized counselling, management and clinical trial design for gastrostomy intervention. Further studies for model optimization will advance personalized care in patients with ALS.