{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T18:34:25Z","timestamp":1763490865939,"version":"3.45.0"},"reference-count":40,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013373","name":"Alberta Machine Intelligence Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013373","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007631","name":"Canadian Institute for Advanced Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007631","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"\n Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of the motor neurons that causes progressive paralysis in patients. Current treatment options aim to prolong survival and improve quality of life. However, due to the heterogeneity of the disease, it is often difficult to determine the optimal time for potential therapies or medical interventions. In this study, we propose a novel method to predict the time until a patient with ALS experiences significant functional impairment (ALSFRS-R \u2264 2) for each of five common functions: speaking, swallowing, handwriting, walking, and breathing. We formulate this task as a multi-event survival problem and validate our approach in the PRO-ACT dataset (\n \n \n \n \n \n N<\/mml:mi>\n =<\/mml:mo>\n 3<\/mml:mn>\n ,<\/mml:mtext>\n 220<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ) by training five covariate-based survival models to estimate the probability of each event over the 500 days following the baseline visit. We then predict five event-specific individual survival distributions (ISDs) for a patient, each providing an interpretable estimate of when that event is likely to occur. The results show that covariate-based models are superior to the Kaplan-Meier estimator at predicting time-to-event outcomes in the PRO-ACT dataset. Additionally, our method enables practitioners to make individual counterfactual predictions\u2014where certain covariates can be changed\u2014to estimate their effect on the predicted outcome. In this regard, we find that Riluzole has little or no impact on predicted functional decline. However, for patients with bulbar-onset ALS, our model predicts significantly shorter time-to-event estimates for loss of speech and swallowing function compared to patients with limb-onset ALS (log-rank\n p<\/jats:italic>\n < 0.001, Bonferroni-adjusted\n \n \n \n \n \n \u03b1<\/mml:mi>\n =<\/mml:mo>\n 0.01<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:alternatives>\n <\/jats:inline-formula>\n ). The proposed method can be applied to current clinical examination data to assess the risk of functional decline and thus allow more personalized treatment planning.\n <\/jats:p>","DOI":"10.1371\/journal.pone.0336476","type":"journal-article","created":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T18:32:05Z","timestamp":1763490725000},"page":"e0336476","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2520-3774","authenticated-orcid":true,"given":"Christian Marius","family":"Lillelund","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4762-8746","authenticated-orcid":true,"given":"Sanjay","family":"Kalra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russell","family":"Greiner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Pooled Resource Open-Access ALS Clinical Trials Consortium (PRO-ACT)","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,11,18]]},"reference":[{"issue":"3","key":"pone.0336476.ref001","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1080\/21646821.2015.1075181","article-title":"Amyotrophic Lateral Sclerosis (ALS) and related motor neuron diseases: an overview","volume":"55","author":"J Morris","year":"2015","journal-title":"Neurodiagn J."},{"issue":"5","key":"pone.0336476.ref002","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1136\/jnnp.2009.188151","article-title":"Motor neuron disease: the bare essentials","volume":"9","author":"K Talbot","year":"2009","journal-title":"Pract Neurol."},{"key":"pone.0336476.ref003","first-page":"718","article-title":"Towards a machine learning empowered prognostic model for predicting disease progression for amyotrophic lateral sclerosis","volume":"2023","author":"H Turabieh","year":"2024","journal-title":"AMIA Annu Symp Proc."},{"issue":"1","key":"pone.0336476.ref004","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1186\/s12883-021-02187-8","article-title":"Prediction of survival in amyotrophic lateral sclerosis: a nationwide, Danish cohort study","volume":"21","author":"A-L Kj\u00e6 ldgaard","year":"2021","journal-title":"BMC Neurol."},{"issue":"10360","key":"pone.0336476.ref005","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1016\/S0140-6736(22)01272-7","article-title":"Amyotrophic lateral sclerosis","volume":"400","author":"EL Feldman","year":"2022","journal-title":"Lancet."},{"key":"pone.0336476.ref006","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0022-510X(99)00210-5","article-title":"The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III)","volume":"169","author":"JM Cedarbaum","year":"1999","journal-title":"J Neurol Sci."},{"issue":"1","key":"pone.0336476.ref007","doi-asserted-by":"crossref","first-page":"20713","DOI":"10.1038\/s41598-023-47935-7","article-title":"Accurate personalized survival prediction for amyotrophic lateral sclerosis patients","volume":"13","author":"L-H Kuan","year":"2023","journal-title":"Sci Rep."},{"key":"pone.0336476.ref008","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.nicl.2016.10.008","article-title":"Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis","volume":"13","author":"HK van der Burgh","year":"2016","journal-title":"Neuroimage Clin."},{"issue":"1","key":"pone.0336476.ref009","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1038\/s41746-022-00588-8","article-title":"A machine-learning based objective measure for ALS disease severity","volume":"5","author":"FG Vieira","year":"2022","journal-title":"NPJ Digit Med."},{"issue":"1","key":"pone.0336476.ref010","doi-asserted-by":"crossref","first-page":"5717","DOI":"10.1038\/s41467-024-49954-y","article-title":"Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns","volume":"15","author":"D M Amaral","year":"2024","journal-title":"Nat Commun."},{"key":"pone.0336476.ref011","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/21678421.2023.2285443","article-title":"Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning","volume":"25","author":"MA Din Abdul Jabbar","year":"2024","journal-title":"Amyotroph Lateral Scler Frontotemporal Degener."},{"issue":"4","key":"pone.0336476.ref012","article-title":"Predicting functional decline and survival in amyotrophic lateral sclerosis","volume":"12","author":"M-L Ong","year":"2017","journal-title":"PLoS One."},{"issue":"1","key":"pone.0336476.ref013","first-page":"3289","article-title":"Effective ways to build and evaluate individual survival distributions","volume":"21","author":"H Haider","year":"2020","journal-title":"J Mach Learn Res."},{"issue":"19","key":"pone.0336476.ref014","doi-asserted-by":"crossref","first-page":"1719","DOI":"10.1212\/WNL.0000000000000951","article-title":"The PRO-ACT database: design, initial analyses, and predictive features","volume":"83","author":"N Atassi","year":"2014","journal-title":"Neurology."},{"key":"pone.0336476.ref015","doi-asserted-by":"crossref","unstructured":"James G, Witten D, Hastie T, Tibshirani R. 1An introduction to statistical learning: with applications in R. 2nd ed. New York: Springer; 2021.","DOI":"10.32614\/CRAN.package.ISLR2"},{"key":"pone.0336476.ref016","unstructured":"Shen X, Elmer J, Chen GH. Neurological prognostication of post-cardiac-arrest coma patients using EEG data: a dynamic survival analysis framework with competing risks. In: Proceedings of the 8th Machine Learning for Healthcare Conference. 2023. p. 667\u201390."},{"issue":"3","key":"pone.0336476.ref017","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0193523","article-title":"A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis","volume":"13","author":"A Andres","year":"2018","journal-title":"PLoS One."},{"key":"pone.0336476.ref018","article-title":"Prognosis of fall risk in home care clients: a noninvasive approach using survival analysis","author":"CM Lillelund","year":"2024","journal-title":"J Public Health (Berl)."},{"issue":"6","key":"pone.0336476.ref019","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1002\/ana.27196","article-title":"Regional cerebral atrophy contributes to personalized survival prediction in amyotrophic lateral sclerosis: a multicentre, machine learning, deformation-based morphometry study","volume":"97","author":"I Lajoie","year":"2025","journal-title":"Ann Neurol."},{"key":"pone.0336476.ref020","doi-asserted-by":"crossref","unstructured":"Sechidis K, Tsoumakas G, Vlahavas I. On the stratification of multi-label data. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer; 2011. p. 145\u201358. https:\/\/doi.org\/10.1007\/978-3-642-23808-6_10","DOI":"10.1007\/978-3-642-23808-6_10"},{"issue":"2","key":"pone.0336476.ref021","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"DR Cox","year":"1972","journal-title":"Journal of the Royal Statistical Society Series B: Statistical Methodology."},{"issue":"3","key":"pone.0336476.ref022","doi-asserted-by":"crossref","DOI":"10.1214\/08-AOAS169","article-title":"Random survival forests","volume":"2","author":"H Ishwaran","year":"2008","journal-title":"Ann Appl Stat."},{"issue":"1","key":"pone.0336476.ref023","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1186\/s12874-018-0482-1","article-title":"DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network","volume":"18","author":"JL Katzman","year":"2018","journal-title":"BMC Med Res Methodol."},{"key":"pone.0336476.ref024","unstructured":"Yu CN, Greiner R, Lin HC, Baracos V. Learning patient-specific cancer survival distributions as a sequence of dependent regressors. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, Granada, Spain, 2011. p. 1845\u201353."},{"key":"pone.0336476.ref025","unstructured":"Lillelund CM, Gharari Foomani AH, Sun W, Qi S, Greiner R. MENSA: a multi-event network for survival analysis with trajectory-based likelihood estimation. arXiv preprint 2023. https:\/\/arxiv.org\/abs\/2409.06525"},{"issue":"18","key":"pone.0336476.ref026","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"FE Harrell","year":"1982","journal-title":"JAMA."},{"issue":"10","key":"pone.0336476.ref027","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1002\/sim.4154","article-title":"On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data","volume":"30","author":"H Uno","year":"2011","journal-title":"Stat Med."},{"key":"pone.0336476.ref028","doi-asserted-by":"crossref","first-page":"2529","DOI":"10.1002\/(SICI)1097-0258(19990915\/30)18:17\/18<2529::AID-SIM274>3.0.CO;2-5","article-title":"Assessment and comparison of prognostic classification schemes for survival data","volume":"18","author":"E Graf","year":"1999","journal-title":"Statist Med."},{"issue":"1","key":"pone.0336476.ref029","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1609\/aaaiss.v2i1.27713","article-title":"SurvivalEVAL: a comprehensive open-source python package for evaluating individual survival distributions","volume":"2","author":"S Qi","year":"2024","journal-title":"AAAI-SS."},{"issue":"282","key":"pone.0336476.ref030","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1080\/01621459.1958.10501452","article-title":"Nonparametric estimation from incomplete observations","volume":"53","author":"EL Kaplan","year":"1958","journal-title":"Journal of the American Statistical Association."},{"key":"pone.0336476.ref031","first-page":"28244","article-title":"An effective meaningful way to evaluate survival models","volume":"202","author":"S-A Qi","year":"2023","journal-title":"Proc Mach Learn Res."},{"issue":"7","key":"pone.0336476.ref032","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1038\/s42256-020-0197-y","article-title":"Causal inference and counterfactual prediction in machine learning for actionable healthcare","volume":"2","author":"M Prosperi","year":"2020","journal-title":"Nat Mach Intell."},{"issue":"3","key":"pone.0336476.ref033","first-page":"163","article-title":"Evaluation of survival data and two new rank order statistics arising in its consideration","volume":"50","author":"N Mantel","year":"1966","journal-title":"Cancer Chemother Rep."},{"issue":"2","key":"pone.0336476.ref034","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/med.21528","article-title":"Riluzole and edaravone: a tale of two amyotrophic lateral sclerosis drugs","volume":"39","author":"MK Jaiswal","year":"2019","journal-title":"Med Res Rev."},{"issue":"11","key":"pone.0336476.ref035","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1038\/nrneurol.2014.184","article-title":"The phenotypic variability of amyotrophic lateral sclerosis","volume":"10","author":"B Swinnen","year":"2014","journal-title":"Nat Rev Neurol."},{"issue":"15","key":"pone.0336476.ref036","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1212\/WNL.0b013e3182a823e0","article-title":"Analysis of start-up, retention, and adherence in ALS clinical trials","volume":"81","author":"N Atassi","year":"2013","journal-title":"Neurology."},{"key":"pone.0336476.ref037","doi-asserted-by":"crossref","unstructured":"Emura T, Chen Y-H. Analysis of survival data with dependent censoring. Singapore: Springer; 2018. https:\/\/doi.org\/10.1007\/978-981-10-7164-5","DOI":"10.1007\/978-981-10-7164-5"},{"key":"pone.0336476.ref038","unstructured":"Foomani AHG, Cooper M, Greiner R, Krishnan RG. Copula-based deep survival models for dependent censoring. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence. 2023. p. 669\u201380."},{"issue":"2","key":"pone.0336476.ref039","doi-asserted-by":"crossref","DOI":"10.1093\/braincomms\/fcaa061","article-title":"Cerebral atrophy in amyotrophic lateral sclerosis parallels the pathological distribution of TDP43","volume":"2","author":"M Dadar","year":"2020","journal-title":"Brain Commun."},{"key":"pone.0336476.ref040","doi-asserted-by":"crossref","first-page":"103162","DOI":"10.1016\/j.nicl.2022.103162","article-title":"Cortical and subcortical grey matter atrophy in amyotrophic lateral sclerosis correlates with measures of disease accumulation independent of disease aggressiveness","volume":"36","author":"N Dieckmann","year":"2022","journal-title":"Neuroimage Clin."}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0336476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T18:32:16Z","timestamp":1763490736000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0336476"}},"subtitle":[],"editor":[{"given":"Guanghui","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,11,18]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0336476","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,18]]}}}