About this item:

100 Views | 133 Downloads

Author Notes:

Correspondence: Cassie S. Mitchell cassie.mitchell@bme.gatech.edu

SP contributed to study design, model construction, statistical analysis, results interpretation, drafted the initial manuscript, and review of content

RK assisted in data collection, analysis, results interpretation, and review of content.

GC managed data quality control, assisted in results interpretation, and reviewed the content.

CM contributed to project oversight, study conception, study design, data collection protocol, results interpretation, wrote the final manuscript, and review of content.

This study was carried out in accordance with the recommendations and approval of the Institutional Review Board of Georgia Institute of Technology and Emory University.

A waiver for consent was granted for this retrospective study, which utilized de-identified an anonymized data from deceased patients.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Research Funding:

This study was funded by National Institutes of Health grants NS081426, NS069616, and NS098228 to CM.


  • predictive medicine
  • data complexity
  • survival analysis
  • neuromuscular disease
  • health informatics

Unraveling the complexity of amyotrophic lateral sclerosis survival prediction


Journal Title:

Frontiers in Neuroinformatics


Volume 12


Type of Work:

Article | Final Publisher PDF


Objective: The heterogeneity of amyotrophic lateral sclerosis (ALS) survival duration, which varies from <1 year to >10 years, challenges clinical decisions and trials. Utilizing data from 801 deceased ALS patients, we: (1) assess the underlying complex relationships among common clinical ALS metrics; (2) identify which clinical ALS metrics are the “best” survival predictors and how their predictive ability changes as a function of disease progression. Methods: Analyses included examination of relationships within the raw data as well as the construction of interactive survival regression and classification models (generalized linear model and random forests model). Dimensionality reduction and feature clustering enabled decomposition of clinical variable contributions. Thirty-eight metrics were utilized, including Medical Research Council (MRC) muscle scores; respiratory function, including forced vital capacity (FVC) and FVC % predicted, oxygen saturation, negative inspiratory force (NIF); the Revised ALS Functional Rating Scale (ALSFRS-R) and its activities of daily living (ADL) and respiratory sub-scores; body weight; onset type, onset age, gender, and height. Prognostic random forest models confirm the dominance of patient age-related parameters decline in classifying survival at thresholds of 30, 60, 90, and 180 days and 1, 2, 3, 4, and 5 years. Results: Collective prognostic insight derived from the overall investigation includes: multi-dimensionality of ALSFRS-R scores suggests cautious usage for survival forecasting; upper and lower extremities independently degenerate and are autonomous from respiratory decline, with the latter associating with nearer-to-death classifications; height and weight-based metrics are auxiliary predictors for farther-from-death classifications; sex and onset site (limb, bulbar) are not independent survival predictors due to age co-correlation. Conclusion: The dimensionality and fluctuating predictors of ALS survival must be considered when developing predictive models for clinical trial development or in-clinic usage. Additional independent metrics and possible revisions to current metrics, like the ALSFRS-R, are needed to capture the underlying complexity needed for population and personalized forecasting of survival.

Copyright information:

© 2018 Pfohl, Kim, Coan and Mitchell.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
Export to EndNote