Patient Stratification Using Clinical and Patient Profiles: Targeting Personalized Prognostic Prediction in ALS

Amyotrophic Lateral Sclerosis (ALS) is a severe neurodegenerative disease with highly heterogeneous disease presentation and progression patterns. This hampers effective treatments targeting all patients and finding a cure is still a challenge. In this scenario, patient stratification is believed to be a key tool to deal with the heterogeneous nature of the disease, promoting the discovery of more homogeneous groups of patients, that can then be used to improve patient prognosis and care. In this work, we propose to use clustering to stratify patient observations in accordance with clinically defined subsets of features (Clinical Profiles). The groups obtained by clustering patients using the Clinical Profiles are called Patient Profiles. Each patient profile is then used to learn specialized prognostic models to predict the need for Non-Invasive Ventilation (NIV) within a time window of 90 days. Each patient profile specific prognostic model is then used in ensemble learning. We used three clinical profiles (prognostic, respiratory and functional) based on complementary clinically relevant views of disease presentation and progression. These clinical profiles yielded two, four, and two patient profiles, respectively. The specialized prognostic models learned from these clinical and patient profiles show overall improvements when compared to the baseline models, where patients are not stratified. These promising results highlight the need for patient stratification for prognostic prediction in ALS. Furthermore, this innovative approach for prognostic prediction, where clinical profiles and patient profiles are integrated to enhance patient stratification, can be used to improve predictions for other disease outcomes in ALS or applied to other diseases.