ArticleAnnals of clinical and translational neurology2016
Predicting disease progression in amyotrophic lateral sclerosis.
Article in Annals of clinical and translational neurology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
- A phase II open label clinical study of the safety, tolerability and efficacy of ILB® for Amyotrophic Lateral Sclerosis.PloS one · 2022Trial
- Long-term survival of participants in the CENTAUR trial of sodium phenylbutyrate-taurursodiol in amyotrophic lateral sclerosis.Muscle & nerve · 2021Trial
- Umbilical Cord Mesenchymal Stem Cells in Amyotrophic Lateral Sclerosis: an Original Study.Stem cell reviews and reports · 2020Trial
- Trial of Sodium Phenylbutyrate-Taurursodiol for Amyotrophic Lateral Sclerosis.The New England journal of medicine · 2020Trial
- Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2026Article
- Prevalence and impact of comorbidities in amyotrophic lateral sclerosis.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Prognostic factors affecting ALS progression through disease tollgates.Journal of neurology · 2024Article
- Predictive modeling of ALS progression: an XGBoost approach using clinical features.BioData mining · 2024Article
- Prognostic communication in amyotrophic lateral sclerosis: findings from a Nationwide Italian survey.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
- Review
- Dysregulated FOXO1 activity drives skeletal muscle intrinsic dysfunction in amyotrophic lateral sclerosis.Acta neuropathologica · 2024Article
- Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics.Biomedicines · 2023Article
- Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study.Diagnostics (Basel, Switzerland) · 2023Article
- Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review.Life (Basel, Switzerland) · 2023Article
- Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis.BMC medical informatics and decision making · 2023Article
- Status of ALS Treatment, Insights into Therapeutic Challenges and Dilemmas.Journal of personalized medicine · 2022Review
- Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.Journal of neurology · 2022Article
- Considerations for Amyotrophic Lateral Sclerosis (ALS) Clinical Trial Design.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- A machine-learning based objective measure for ALS disease severity.NPJ digital medicine · 2022Article
- Neurofilament light and heterogeneity of disease progression in amyotrophic lateral sclerosis: development and validation of a prediction model to improve interventional trials.Translational neurodegeneration · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIt is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic.
methodsBased on the PRO-ACT ALS database, we developed random forest (RF), pre-slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability.
resultsWe found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre-slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population.
interpretationWe conclude that the RF Model delivers superior predictions of ALS disease progression.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.