Partitioning the progressive course of ALS disease into stages facilitates clinical description, communication, prognostication, therapeutic decision-making, allocation of resources, and therapeutic targeting in clinical trials. Two approaches (King’s and MITOS) are recently proposed.

Patients from the PRO-ACT dataset were staged using ALSFRS-R responses. Risks of progression through stages and death were estimated, as were effects of prognostic variables on these risks.

A total of 29,947 time points in 3,199 patients from the PRO-ACT dataset were assigned stages. Although the three systems were moderately correlated, MITOS stages were heavily skewed towards advanced disease, whereas King’s and FT9 stages were more balanced.  Non-sequential progression was observed with King’s system. Markov models adequately described transitions from stage to stage in the first year of observation, but underestimated risks beyond that point. Regardless of staging method, initial rate of ALSFRS-R decline had a powerful effect on rate of progression through sequential stages, whereas age predominantly influenced stage-specific mortality.

King's and FT9 are more sensitive to observed progression of disease in clinical trials than MITOS. FT9 can partition the course similar to King's, and may have advantages of sequential progression and easy applicability to retrospective data. Markov transition intensity estimates may be of value for counseling, health economic studies and research design. In particular, this framework permits estimation of multidimensional effects of variables (including treatment) on outcome.