Cargando…

A clinical tool for predicting survival in ALS

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use o...

Descripción completa

Detalles Bibliográficos
Autores principales: Knibb, Jonathan A, Keren, Noa, Kulka, Anna, Leigh, P Nigel, Martin, Sarah, Shaw, Christopher E, Tsuda, Miho, Al-Chalabi, Ammar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5136716/
https://www.ncbi.nlm.nih.gov/pubmed/27378085
http://dx.doi.org/10.1136/jnnp-2015-312908
_version_ 1782471767937253376
author Knibb, Jonathan A
Keren, Noa
Kulka, Anna
Leigh, P Nigel
Martin, Sarah
Shaw, Christopher E
Tsuda, Miho
Al-Chalabi, Ammar
author_facet Knibb, Jonathan A
Keren, Noa
Kulka, Anna
Leigh, P Nigel
Martin, Sarah
Shaw, Christopher E
Tsuda, Miho
Al-Chalabi, Ammar
author_sort Knibb, Jonathan A
collection PubMed
description BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. METHODS: 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. FINDINGS: Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. INTERPRETATION: A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors.
format Online
Article
Text
id pubmed-5136716
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher BMJ Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-51367162016-12-08 A clinical tool for predicting survival in ALS Knibb, Jonathan A Keren, Noa Kulka, Anna Leigh, P Nigel Martin, Sarah Shaw, Christopher E Tsuda, Miho Al-Chalabi, Ammar J Neurol Neurosurg Psychiatry Neurodegeneration BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a progressive and usually fatal neurodegenerative disease. Survival from diagnosis varies considerably. Several prognostic factors are known, including site of onset (bulbar or limb), age at symptom onset, delay from onset to diagnosis and the use of riluzole and non-invasive ventilation (NIV). Clinicians and patients would benefit from a practical way of using these factors to provide an individualised prognosis. METHODS: 575 consecutive patients with incident ALS from a population-based registry in South-East England register for ALS (SEALS) were studied. Their survival was modelled as a two-step process: the time from diagnosis to respiratory muscle involvement, followed by the time from respiratory involvement to death. The effects of predictor variables were assessed separately for each time interval. FINDINGS: Younger age at symptom onset, longer delay from onset to diagnosis and riluzole use were associated with slower progression to respiratory involvement, and NIV use was associated with lower mortality after respiratory involvement, each with a clinically significant effect size. Riluzole may have a greater effect in younger patients and those with longer delay to diagnosis. A patient's survival time has a roughly 50% chance of falling between half and twice the predicted median. INTERPRETATION: A simple and clinically applicable graphical method of predicting an individual patient's survival from diagnosis is presented. The model should be validated in an independent cohort, and extended to include other important prognostic factors. BMJ Publishing Group 2016-12 2016-07-04 /pmc/articles/PMC5136716/ /pubmed/27378085 http://dx.doi.org/10.1136/jnnp-2015-312908 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Neurodegeneration
Knibb, Jonathan A
Keren, Noa
Kulka, Anna
Leigh, P Nigel
Martin, Sarah
Shaw, Christopher E
Tsuda, Miho
Al-Chalabi, Ammar
A clinical tool for predicting survival in ALS
title A clinical tool for predicting survival in ALS
title_full A clinical tool for predicting survival in ALS
title_fullStr A clinical tool for predicting survival in ALS
title_full_unstemmed A clinical tool for predicting survival in ALS
title_short A clinical tool for predicting survival in ALS
title_sort clinical tool for predicting survival in als
topic Neurodegeneration
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5136716/
https://www.ncbi.nlm.nih.gov/pubmed/27378085
http://dx.doi.org/10.1136/jnnp-2015-312908
work_keys_str_mv AT knibbjonathana aclinicaltoolforpredictingsurvivalinals
AT kerennoa aclinicaltoolforpredictingsurvivalinals
AT kulkaanna aclinicaltoolforpredictingsurvivalinals
AT leighpnigel aclinicaltoolforpredictingsurvivalinals
AT martinsarah aclinicaltoolforpredictingsurvivalinals
AT shawchristophere aclinicaltoolforpredictingsurvivalinals
AT tsudamiho aclinicaltoolforpredictingsurvivalinals
AT alchalabiammar aclinicaltoolforpredictingsurvivalinals
AT knibbjonathana clinicaltoolforpredictingsurvivalinals
AT kerennoa clinicaltoolforpredictingsurvivalinals
AT kulkaanna clinicaltoolforpredictingsurvivalinals
AT leighpnigel clinicaltoolforpredictingsurvivalinals
AT martinsarah clinicaltoolforpredictingsurvivalinals
AT shawchristophere clinicaltoolforpredictingsurvivalinals
AT tsudamiho clinicaltoolforpredictingsurvivalinals
AT alchalabiammar clinicaltoolforpredictingsurvivalinals