Stories about: Mauricio Santillana

Scientists find link between increases in local temperature and antibiotic resistance

Image representing the rise of antibiotic resistance
Illustration by Fawn Gracey

Over-prescribing has long been thought to increase antibiotic resistance in bacteria. But could much bigger environmental pressures be at play?

While studying the role of climate on the distribution of antibiotic resistance across the geography of the U.S., a multidisciplinary team of epidemiologists from Boston Children’s Hospital found that higher local temperatures and population densities correlate with higher antibiotic resistance in common bacterial strains. Their findings were published today in Nature Climate Change.

“The effects of climate are increasingly being recognized in a variety of infectious diseases, but so far as we know this is the first time it has been implicated in the distribution of antibiotic resistance over geographies,” says the study’s lead author, Derek MacFadden, MD, an infectious disease specialist and research fellow at Boston Children’s Hospital. “We also found a signal that the associations between antibiotic resistance and temperature could be increasing over time.”

During their study, the team assembled a large database of U.S. antibiotic resistance in E. coli, K. pneumoniae and S. aureus, pulling from hospital, laboratory and disease surveillance data documented between 2013 and 2015. Altogether, their database comprised more than 1.6 million bacterial specimens from 602 unique records across 223 facilities and 41 states.

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Lessons from the data: Applying machine learning for clinical decision support

machine learning clinical decision support

Mauricio Santillana, PhD, faculty member in the Computational Health Informatics Program at Boston Children’s Hospital, had an idea as he witnessed the volume of continuous real-time data generated in the pediatric intensive care unit (PICU). He realized that tapping the data on patients’ ever-changing vital signs, with the help of machine-learning algorithms, could support clinical decision-making and predict (and help head off) up-coming health issues.

He started a dialogue with the hospital’s Innovation & Digital Health Accelerator, and now collaborates closely with clinicians in the PICU to create machine-learning algorithms that can help them provide the highest level of care.

“It’s fairly recent that clinicians realized people with backgrounds in math and statistics can be very helpful in a clinical context,” says Santillana

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