It is indeed interesting to know if an ALS patient condition is stable or deteriorating. Until 2018 there was little known biomarkers for ALS. Thanks to analysis of the ALS database PRO-ACT, scientists learned that some common biomarkers may indicate how a patient situation is evolving.
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Predicting functional decline and survival in amyotrophic lateral sclerosis.pdf


Better biomarkers of ALS could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from ALS sufferers who participated in clinical trials and made them available to researchers in the PRO-ACT database.

PRO-ACT data was used to predict decline in ALS-FRS over the next 9 months with accuracies exceeding chance and clinicians’ predictions and achieving root mean squared errors of ~54% (which is not so good).

Mei-Lyn Ong, Pei Fang Tan and Joanna D. Holbrook [0] attempted to build models to better predict decline in the ALSFRS-R score and to predict survival. They applied learning algorithms to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. They say their error in prediction is better than 20%.

What is interesting is that they published their results in 2017 in a comprehensive manner and it is possible to use them to provide information to guide a treatment from a reduced set of common biomarkers.
// Alkaline phosphatase
// Albumin
// Creatine Kinase
// Weight
// Chloride
// Bicarbonate
// Gamma Glutamyl Transferase
// Pulse
// Bilirubin

[0] Ong M-L, Tan PF, Holbrook JD (2017)
Predicting functional decline and survival in
amyotrophic lateral sclerosis. PLoS ONE 12(4):