Journal of Neurology

, Volume 266, Issue 3, pp 755–765 | Cite as

Tollgate-based progression pathways of ALS patients

  • Özden O. Dalgıç
  • F. Safa Erenay
  • Kalyan S. Pasupathy
  • Osman Y. Özaltın
  • Brian A. Crum
  • Mustafa Y. SirEmail author
Original Communication



To capture ALS progression in arm, leg, speech, swallowing, and breathing segments using a disease-specific staging system, namely tollgate-based ALS staging system (TASS), where tollgates refer to a set of critical clinical events including having slight weakness in arms, needing a wheelchair, needing a feeding tube, etc.


We compiled a longitudinal dataset from medical records including free-text clinical notes of 514 ALS patients from Mayo Clinic, Rochester-MN. We derived tollgate-based progression pathways of patients up to a 1-year period starting from the first clinic visit. We conducted Kaplan–Meier analyses to estimate the probability of passing each tollgate over time for each functional segment.


At their first clinic visit, 93%, 77%, and 60% of patients displayed some level of limb, bulbar, and breathing weakness, respectively. The proportion of patients at milder tollgate levels (tollgate level < 2) was smaller for arm and leg segments (38% and 46%, respectively) compared to others (> 65%). Patients showed non-uniform TASS pathways, i.e., the likelihood of passing a tollgate differed based on the affected segments at the initial visit. For instance, stratified by impaired segments at the initial visit, patients with limb and breathing impairment were more likely (62%) to use bi-level positive airway pressure device in a year compared to those with bulbar and breathing impairment (26%).


Using TASS, clinicians can inform ALS patients about their individualized likelihood of having critical disabilities and assistive-device needs (e.g., being dependent on wheelchair/ventilation, needing walker/wheelchair or communication devices), and help them better prepare for future.


ALS progression Tollgate-based staging system Phenotypes Kaplan–Meier analysis 



This research is partially supported by the Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grants 113788 and 113790). This work is also funded in part by the Mayo Clinic Department of Neurology and Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery.

Compliance with ethical standards

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical standard

This study was reviewed by the Mayo Clinic Institutional Review Board and deemed as an exempt study.

Supplementary material

415_2019_9199_MOESM1_ESM.docx (1.3 mb)
Supplementary material 1 (DOCX 1289 KB)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Harvard Medical School, BostonBostonUSA
  2. 2.Institute for Technology AssessmentMassachusetts General HospitalBostonUSA
  3. 3.Department of Managements SciencesUniversity of WaterlooWaterlooCanada
  4. 4.Department of Health Sciences ResearchMayo ClinicRochesterUSA
  5. 5.Mayo Kern Center for the Science of Health Care DeliveryMayo ClinicRochesterUSA
  6. 6.Edward P. Fitts Department of Industrial and Systems EngineeringNorth Carolina State UniversityRaleighUSA
  7. 7.Department of NeurologyMayo ClinicRochesterUSA
  8. 8.Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care DeliveryRochesterUSA

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