Unraveling the Complexity of Amyotrophic Lateral Sclerosis Survival Prediction

Abstract

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.

Associations of Patient Mood, Modulators of Quality of Life, and Pharmaceuticals with Amyotrophic Lateral Sclerosis Survival Duration

“…This is a secondary analysis of a retrospective cohort study comprising 8028 clinical visit records collected from 1585 patients at the Emory ALS Clinic (Emory University Hospital, Atlanta, GA, United States) into a FileMaker Pro relational database. Data collection, organization, and quality control methods are as previously published in prior work with this data set [5,20,44,45]. All data was de-identified and anonymized.…”

“…The conditional random forests model was simulated four times and the variable importance was averaged to determine the final result. The area under the curve (AUC) of variable importance was used as the performance metric [45].…”

“…Gender is defined as biological gender (male or female). ALS onset type is defined as limb, bulbar, or other/unclassifiable [45]. Table 1 illustrates the patient characteristics of the overall cohort.…”

“…The present study uses the ALSFRS-R total score and the ALSFRS-R respiratory sub-score. Most of the assessed physical health factors are standardly measured in ALS, well-defined, and often used by the field in ALS predictive analysis [45].…”

Survival forests under test: Impact of the proportional hazards assumption on prognostic and predictive forests for amyotrophic lateral sclerosis survival

“…The availability of such instruments would, for example, allow the planning of more powerful clinical trials by means of efficient patient stratification (Chiò et al 2009). Two approaches have been used in the past, namely the search for prognostic models for the overall survival time after diagnosis (Kimura et al 2006;Zoccolella et al 2008;Fujimura-Kiyono et al 2011;Beaulieu-Jones et al 2016;Mandrioli et al 2017;Ong et al 2017;Pfohl et al 2018, among many others) and the prognosis of a functional assessment of patients via the ordinal ALS functional rating scale (ALSFRS; Brooks et al 1996) and ALSFRS-R scores (Cedarbaum et al 1999;Hothorn and Jung 2014;Küffner et al 2015).…”