The newly identified set of variables - when processed by computer software (find out more about medical software in the catalogue of MEDICA 2018) - will yield more precise predictions of a patient's response, said Wei Wu, a faculty member in Carnegie Mellon University's Computational Biology Department. "Systemic corticosteroids are the most effective therapy we have for asthma, but not all patients respond in the same way," Wu said. "Unfortunately, when clinicians do not see a big improvement after initial treatment, they might give patients even higher doses. If a patient is one of those who cannot be helped by corticosteroids, the higher dose just means worse side effects."
The study, led by Wu and Dr. Sally E. Wenzel, director of the University of Pittsburgh Asthma Institute at UPMC, was recently published online by the American Journal of Respiratory and Critical Care Medicine.
"I see so many patients in my clinic who have been ravaged by the side effects of corticosteroids," said Wenzel, also chair of the Department of Environmental and Occupational Health at Pitt's Graduate School of Public Health. Weight gain, extreme emotions, inability to sleep, glaucoma and thinning of the skin are among the possible side effects of corticosteroid pills and injections, so physicians would like to prescribe them only to patients they know will benefit from them, she added.
She emphasized that the study addresses corticosteroid pills and injections, not the widely used corticosteroid inhalers (Inhaler and aerosol appliances at MEDICA 2018), although there is likely to be some overlap in patient response to the medications in either form.
To better understand how different subgroups of patients respond to systemic corticosteroid therapy, the researchers used a machine learning algorithm to sift through 100 variables for each of 346 adult patients in the federally funded Severe Asthma Research Program (SARP).
The algorithm, developed by Wu and Seojin Bang, a Ph.D. student in CMU's Computational Biology Department, recognizes patterns in massive volumes of complex clinical data (solutions for Archiving and documentation of medical data at MEDICA 2018). It clustered patients into four subgroups, including two for severe asthmatics - one that responded to systemic corticosteroids and one that didn't.
Of the original 100 variables, they identified 12 - including age of onset, weight, race and scores on a quality-of-life questionnaire - that could correctly categorize patients with high confidence if processed by a computer app. To test this process, they used the 12 variables (or their equivalents) to categorize a group of 182 SARP participants not included in the original analysis. The variables proved effective in successfully categorizing these additional patients.
"We believe we've made progress toward making precision medicine a reality," Wei said. "Five years ago, we were only able to categorize patients clinically. Now, using incredibly complex data, we are able to predict how these subgroups will respond to a critical drug treatment."
MEDICA-tradefair.com; Source: Carnegie Mellon University
