Factors predicting one-year mortality in amyotrophic lateral sclerosis patients - data from a population-based registry

Wolf, Joachim; Safer, Anton; Wöhrle, Johannes C.; Palm, Frederick; Nix, Wilfred A.; Maschke, Matthias; Grau, Armin J.

doi:10.1186/s12883-014-0197-9

Cited by 48 publications

(27 citation statements)

References 19 publications

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“…In fact, we did not find an association between prediagnosis BMI and ALS survival; only BMI at diagnosis and BMI change between age 40 years and diagnosis were associated with ALS survival. Other studies support the hypothesis that BMI change immediately before ALS diagnosis is both a common feature of ALS and a prognostic factor, whereas evidence that BMI at ALS diagnosis is a prognostic factor is inconsistent, possibly due to small sample sizes in some studies (12,(25)(26)(27).…”

Section: Discussionmentioning

confidence: 91%

Body Mass Index and Amyotrophic Lateral Sclerosis: A Study of US Military Veterans

Mariosa¹,

Beard²,

Umbach³

et al. 2017

American Journal of Epidemiology

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Amyotrophic lateral sclerosis (ALS) may be associated with low body mass index (BMI) at the time of diagnosis. However, the role of premorbid BMI in the development of ALS and survival after diagnosis remains unclear. In 2005-2010, we interviewed 467 patients with ALS from the US National Registry of Veterans with ALS and 975 frequency-matched veteran controls. In this sample, we evaluated the association of BMI and BMI change at different ages with ALS risk using unconditional logistic models and with survival after ALS diagnosis using Cox proportional hazards models. After adjustment for confounders, compared with a moderate increase in BMI between ages 25 and 40 years, stable or decreasing BMI was positively associated with ALS risk (odds ratio (OR) = 1.61, 95% confidence interval (CI): 1.20, 2.16). A 1-unit increase in BMI at age 40 years (OR = 0.95, 95% CI: 0.91, 0.98) but not at age 25 years (OR = 0.99, 95% CI: 0.95, 1.03) was inversely associated with ALS. These associations were similar for bulbar and spinal ALS but stronger for those with a delay of less than 1 year between symptom onset and diagnosis. We found no association between prediagnosis BMI and survival. A decreasing BMI from early to middle age and a low BMI in middle age may be positively associated with ALS risk.

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Section: Discussionmentioning

confidence: 91%

Body Mass Index and Amyotrophic Lateral Sclerosis: A Study of US Military Veterans

Mariosa¹,

Beard²,

Umbach³

et al. 2017

American Journal of Epidemiology

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“…There is evidence that patients with upper motor neuron features alone12 or lower motor neuron features alone12 22–24 tend to survive longer, and that those with cognitive impairment,25 neck weakness,26 weight loss at diagnosis6 and certain genotypes27 28 have a higher overall mortality. Our study did not include information on these factors.…”

Section: Discussionmentioning

confidence: 99%

A clinical tool for predicting survival in ALS

Knibb

Keren

Kulka

et al. 2016

J Neurol Neurosurg Psychiatry

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BackgroundAmyotrophic 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.Methods575 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.FindingsYounger 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.InterpretationA 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.

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“…To date, clinical characteristics such as site of onset, respiratory status, ALS Functional Rating Scale (ALSFRS) scores (Cedarbaum et al, 1999) and C9orf72 phenotype status (i.e. a disease-causing repeat expansion mutation in ALS (DeJesus-Hernandez et al, 2011, Renton et al, 2011)) are shown to have some predictive power for prediction of survival (Chiò et al, 2009, Elamin et al, 2015, Scotton et al, 2012, Wolf et al, 2014, Wolf et al, 2015). Prognosis based on these markers, however, often remains too uncertain to be implemented in clinical practice (Elamin et al, 2015) as motor neuron loss might already occur before clinical weakness can be measured (Simon et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis

Burgh

Schmidt

Westeneng

et al. 2017

NeuroImage: Clinical

142

109

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Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

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Factors predicting one-year mortality in amyotrophic lateral sclerosis patients - data from a population-based registry

Cited by 48 publications

References 19 publications

Body Mass Index and Amyotrophic Lateral Sclerosis: A Study of US Military Veterans

Body Mass Index and Amyotrophic Lateral Sclerosis: A Study of US Military Veterans

A clinical tool for predicting survival in ALS

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis

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