ArticleAmyotrophic lateral sclerosis & frontotemporal degeneration2026
Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.
Article in Amyotrophic lateral sclerosis & frontotemporal degeneration, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveDevelop a machine learning-based model for survival prediction in ALS, including advanced-stage patients (≤50% predicted normal vital capacity [VC
methodsTraining data from the PRO-ACT Database (
resultsBaseline characteristics with the highest RI for driving survival predictions included: VC% slope (20.2%); age (12.4%); VC% (9.9%); VC(L) (7.5%); ALSFRS-R (6.6%); and ALSFRS-R slope (5.1%). Model performance upon external validation was satisfactory for both discrimination (C-index, 0.709 [95% CI, 0.671-0.746]) and calibration (calibration-in-the-large, 0.083 [95% CI, -0.073-0.232]; calibration slope, 0.992 [95% CI, 0.789-1.198]). At 8-months from baseline, the model successfully stratified patients by survival prognosis, with low-, average-, and high-risk population tertiles having observed median survival probabilities of 85, 69, and 43%, respectively.
conclusionsThis model accurately predicts survival prognosis in ALS, including patients with severely impaired respiratory function. This new understanding of patient-specific factors that drive survival prognostication will be invaluable for reducing patient heterogeneity in clinical trials evaluating novel therapeutic modalities in early- and advanced-stage ALS.
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