Crowdsourcing Project Predicts Progression of ALS

Using data from old clinical trials, two groups of researchers have found a better way to predict how amyotrophic lateral sclerosis (ALS) progresses in different patients. The winning algorithms—designed by non-ALS experts—outperformed the judgments of a group of ALS clinicians given the same data. The advances could make it easier to test whether new drugs can slow the fatal neurodegenerative disease. For the competition, participants were given just a slice of this data set, collected over 3 months, and asked to design an algorithm to predict how patients would fare in the subsequent 9 months, according to a standard functional scale that measures their ability to move and care for themselves. When predictions from the two winning algorithms were combined, they outperformed estimates solicited from a dozen ALS clinicians who pored over the same data, the authors report. They estimate that using these algorithms to predict outcomes could allow a drug sponsor to reduce the size of the trial by at least 20% and save as much as $6 million in a large phase III trial.