{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T21:11:09Z","timestamp":1792098669852,"version":"4.4.0"},"reference-count":18,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,12,29]],"date-time":"2020-12-29T00:00:00Z","timestamp":1609200000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000406","name":"Motor Neurone Disease Association","doi-asserted-by":"publisher","award":["MR\/P000983\/1"],"award-info":[{"award-number":["MR\/P000983\/1"]}],"id":[{"id":"10.13039\/501100000406","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000406","name":"Motor Neurone Disease Association","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000406","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Muscle and Nerve"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Prognostic uncertainty in amyotrophic lateral sclerosis (ALS) confounds clinical management planning, patient counseling, and trial stratification. Fasciculations are an early clinical hallmark of disease and can be quantified noninvasively. Using an innovative analytical method, we correlated novel fasciculation parameters with a predictive survival model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using high\u2010density surface electromyography, we collected biceps recordings from ALS patients on their first research visit. By accessing an online survival prediction tool, we provided eight clinical and genetic parameters to estimate individual patient survival. Fasciculation analysis was performed using an automated algorithm (Surface Potential Quantification Engine), with a Cox proportional hazards model to calculate hazard ratios.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The median predicted survival for 31 patients was 41 (interquartile range, 31.5\u201057) months. Univariate hazard ratios were 1.09 (95% confidence interval [CI], 1.03\u20101.16) for the rate of change of fasciculation frequency (RoCoFF) and 1.10 (95% CI, 1.01\u20101.19) for the amplitude dispersion rate. Only the RoCoFF remained significant (<jats:italic>P<\/jats:italic> =\u2009.04) in a multivariate model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Noninvasive measurement of fasciculations at a single time\u2010point could enhance prognostic models in ALS, where higher RoCoFF values indicate shorter survival.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mus.27139","type":"journal-article","created":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T09:05:48Z","timestamp":1607504748000},"page":"392-396","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Fasciculation analysis reveals a novel parameter that correlates with predicted survival in amyotrophic lateral sclerosis"],"prefix":"10.1002","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4248-9030","authenticated-orcid":false,"given":"Kate","family":"Wannop","sequence":"first","affiliation":[{"name":"Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London UK Dementia Research Institute  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5972-0748","authenticated-orcid":false,"given":"James","family":"Bashford","sequence":"additional","affiliation":[{"name":"Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London UK Dementia Research Institute  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidan","family":"Wickham","sequence":"additional","affiliation":[{"name":"Department of Bioengineering Imperial College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raquel","family":"Iniesta","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Health Informatics, King's College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Drakakis","sequence":"additional","affiliation":[{"name":"Department of Bioengineering Imperial College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martyn","family":"Boutelle","sequence":"additional","affiliation":[{"name":"Department of Bioengineering Imperial College London  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kerry","family":"Mills","sequence":"additional","affiliation":[{"name":"Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London UK Dementia Research Institute  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chris","family":"Shaw","sequence":"additional","affiliation":[{"name":"Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London UK Dementia Research Institute  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,12,29]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrneurol.2013.203"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp.2010.235952"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3109\/17482960802566824"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(18)30089-9"},{"key":"e_1_2_10_6_1","unstructured":"ENCALS survival prediction model. 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