Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis

Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases

The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.

Accelerating drug development for amyotrophic lateral sclerosis: construction and application of a disease course model using historical placebo group data

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is an irreversible degenerative disease. Placebo-controlled randomized trials are currently the main trial design to assess the clinical efficacy of drugs for ALS treatment. The aim of this study was to establish models to quantitatively describe the course of ALS, explore influencing factors, and provide the necessary information for ALS drug development.

METHODS

We conducted a comprehensive search of PubMed and the Cochrane Library Central Register for placebo-controlled trials that evaluated treatments for ALS. From these trials, we extracted the clinical and demographic characteristics of participants in the placebo group, as well as outcome data, which encompassed overall survival (OS) and Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) scores, at various time points.

RESULTS

In total, 47 studies involving 6118 participants were included. Disease duration and the proportion of patients receiving riluzole were identified as significant factors influencing OS in the placebo group. Specifically, the median OS was 35.5 months for a disease duration of 9 months, whereas it was 20.0 months for a disease duration of 36 months. Furthermore, for every 10% increase in the proportion of patients treated with riluzole (100 mg daily), there was an association with a median OS extension of approximately 0.4 months. The estimated time for the ALSFRS-R score in the placebo group to decrease to 50% of its maximum effect from baseline level was approximately 17.5 months, and the time to reach a plateau was about 40 months.

CONCLUSIONS

The established disease course model of the historical placebo group is valuable in the decision-making process for the clinical development of ALS drugs. It serves not only as an external control to evaluate the efficacy of the tested drug in single-arm trials but also as prior information that aids in accurately estimating the posterior distribution of the disease course in the placebo group during small-sample clinical trials.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.