ALSFRS-R score and its ratio: A useful predictor for ALS-progression

Kollewe, Katja; Mauss, Ulrike; Krampfl, K.; Petri, Susanne; Dengler, Reinhard; Mohammadi, Bahram

doi:10.1016/j.jns.2008.07.016

Cited by 224 publications

(173 citation statements)

References 36 publications

(101 reference statements)

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“…Overall, 15 different features were identified by more than one solver. Several were features reported in the literature 17,18,[24][25][26][27][28][29][30][31][32][33] , including age, site of disease onset, gender, the slope of disease progression so far, past ALSFRS slope, and past FVC slope, thus serving as validation of both the features and the algorithms. Unfortunately for many of the patients in PRO-ACT, FVC information was not available or other key features required to calculate FVC were missing.…”

Section: Discussionmentioning

confidence: 99%

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

et al. 2014

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Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in its clinical presentation. This makes diagnosis and effective treatment difficult, so better tools for estimating disease progression are needed. Here, we report results from the DREAM-Phil Bowen ALS Prediction Prize4Life challenge. In this crowdsourcing competition, competitors developed algorithms for the prediction of disease progression of 1,822 ALS patients from standardized, anonymized phase 2/3 clinical trials. The two best algorithms outperformed a method designed by the challenge organizers as well as predictions by ALS clinicians. We estimate that using both winning algorithms in future trial designs could reduce the required number of patients by at least 20%. The DREAM-Phil Bowen ALS Prediction Prize4Life challenge also identified several potential nonstandard predictors of disease progression including uric acid, creatinine and surprisingly, blood pressure, shedding light on ALS pathobiology. This analysis reveals the potential of a crowdsourcing competition that uses clinical trial data for accelerating ALS research and development

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Section: Discussionmentioning

confidence: 99%

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

et al. 2014

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“…These potential candidates were further screened with baseline laboratory studies to confirm that they did not have any condition that would exclude them from the study. Baseline clinical assessments included hand-held dynamometry (HHD), forced vital capacity (FVC), ALS functional rating scale-revised (ALSFRS-R) [13], and MRI scans of brain and entire spinal cord. HHD megascores were calculated from the individual measurements for each muscle [14], providing a normalized assessment of upper and lower extremity strength.…”

Section: Presurgical Assessmentmentioning

confidence: 99%

Lumbar Intraspinal Injection of Neural Stem Cells in Patients with Amyotrophic Lateral Sclerosis: Results of a Phase I Trial in 12 Patients

et al. 2012

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“…Understanding the development of ALSFRS over time is relevant for understanding disease progression. Reports vary regarding the linearity of ALSFRS-R measure over time [34,43,54,55], and these reports likely depend on number of patients, distribution of disease progression, and frequency of measuring ALSFRS-R.…”

Section: Understanding Als Symptom Profilementioning

confidence: 99%

Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

et al. 2015

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Advancing research and clinical care, and conducting successful and cost-effective clinical trials requires characterizing a given patient population. To gather a sufficiently large cohort of patients in rare diseases such as amyotrophic lateral sclerosis (ALS), we developed the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) platform. The PRO-ACT database currently consists of >8600 ALS patient records from 17 completed clinical trials, and more trials are being incorporated. The database was launched in an open-access mode in December 2012; since then, >400 researchers from >40 countries have requested the data. This review gives an overview on the research enabled by this resource, through several examples of research already carried out with the goal of improving patient care and understanding the disease. These examples include predicting ALS progression, the simulation of future ALS clinical trials, the verification of previously proposed predictive features, the discovery of novel predictors of ALS progression and survival, the newly identified stratification of patients based on their disease progression profiles, and the development of tools for better clinical trial recruitment and monitoring. Results from these approaches clearly demonstrate the value of large datasets for developing a better understanding of ALS natural history, prognostic factors, patient stratification, and more. The increasing use by the community suggests that further analyses of the PRO-ACT database will continue to reveal more information about this disease that has for so long defied our understanding.

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ALSFRS-R score and its ratio: A useful predictor for ALS-progression

Cited by 224 publications

References 36 publications

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Crowdsourced analysis of clinical trial data to predict amyotrophic lateral sclerosis progression

Lumbar Intraspinal Injection of Neural Stem Cells in Patients with Amyotrophic Lateral Sclerosis: Results of a Phase I Trial in 12 Patients

Being PRO-ACTive: What can a Clinical Trial Database Reveal About ALS?

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