ArticlePloS one2017
Predicting functional decline and survival in amyotrophic lateral sclerosis.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Creatine kinase and prognosis in amyotrophic lateral sclerosis: a literature review and multi-centre cohort analysis.Journal of neurology · 2022Pooled it
- Multidisciplinary care in Amyotrophic Lateral Sclerosis: a systematic review and meta-analysis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021Pooled it
- Development and validation of predictive models for 6-month gastrostomy timing in amyotrophic lateral sclerosis.BMJ neurology open · 2026Article
- Predicting Amyotrophic Lateral Sclerosis Mortality With Machine Learning in Diverse Patient Databases.Muscle & nerve · 2025Article
- AI in Neurology: Everything, Everywhere, all at Once PART 2: Speech, Sentience, Scruples, and Service.Annals of neurology · 2025Review
- Predictive Analysis of Amyotrophic Lateral Sclerosis Progression and Mortality in a Clinic Cohort From Singapore.Muscle & nerve · 2025Article
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis.PloS one · 2025Article
- Prognostic factors affecting ALS progression through disease tollgates.Journal of neurology · 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
- Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study.BMC medical informatics and decision making · 2024Article
- Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.Nature communications · 2024Article
- Creatine Kinase and Respiratory Decline in Amyotrophic Lateral Sclerosis.Brain sciences · 2024Article
- Dynamic effects of prognostic factors and individual survival prediction for amyotrophic lateral sclerosis disease.Annals of clinical and translational neurology · 2023Article
- Review
- Review
- Urine specific gravity to identify and predict hydration need in ALS.Amyotrophic lateral sclerosis & frontotemporal degeneration · 2022Article
- Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression.Journal of neurology · 2022Article
- Identifying and predicting amyotrophic lateral sclerosis clinical subgroups: a population-based machine-learning study.The Lancet. Digital health · 2022Article
- Home-Based Music Therapy to Support Bulbar and Respiratory Functions of Persons with Early and Mid-Stage Amyotrophic Lateral Sclerosis-Protocol and Results from a Feasibility Study.Brain sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBetter predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database.
methodsIn this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors.
resultsA model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class.
conclusionsUsing combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.
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