MIT’s Algorithm Reduces Unnecessary Use of Antibiotics for UTIs

MIT's Algorithm Reduces Unnecessary Use of Antibiotics for UTIs
MIT's Algorithm Reduces Unnecessary Use of Antibiotics for UTIs

What You Should Know:

– MIT research team created a machine learning algorithm
that predicts the probability that a patient’s Urinary Tract Infection (UTI)
can be treated by various antibiotics.

– The team showed that the algorithm would allow
clinicians to reduce the use of second-line antibiotics (which have been shown
to put women at risk for potentially dangerous complications) by 67 percent.
For patients where clinicians chose a second-line drug but the algorithm chose
a first-line drug, the first-line drug ended up working more than 90 percent of
the time.

One paradox
about antibiotics is that, broadly speaking, the more we use them, the less
they continue to work. The Darwinian process of bacteria growing resistant to
antibiotics means that, when the drugs don’t work, we can no longer treat
infections, leading to groups like the World Health Organization warning about our
ability to control major public health threats.

Because of its ubiquity, one topic that’s
particularly concerning is urinary tract infections (UTIs), which affect half of all women and add almost $4
billion a year in unnecessary healthcare costs. Doctors often treat
UTIs using antibiotics called fluoroquinolones that are inexpensive and
generally effective. However, they have also been found to put women at risk of
contracting other infections like C. difficile (C.diff) and staph infections, and also associated
with a higher risk of tendon injuries and life-threatening
conditions like aortic tears

Because of this, medical associations have issued
guidelines recommending fluoroquinolones as “second-line treatments” that should only be
used on a patient when other antibiotics are ineffective or have adverse
reactions. All the while, doctors with limited time and resources continue to
prescribe them at high rates. 

A team led by MIT scientists believes that this conundrum
opens up an opportunity for a data-driven tool that could help doctors make
safer, more customized decisions for patients. 

In a new paper the researchers present a recommendation
algorithm that predicts the probability that a patient’s UTI can be treated by
first or second-line antibiotics. With this information, the model then makes a
recommendation for a specific treatment that selects a first-line agent as
frequently as possible, without leading to an excess of treatment

Research Protocols

The team showed that the model would allow clinicians to
reduce the use of second-line antibiotics 67 percent. For patients where
clinicians chose a second-line drug but the algorithm chose a first-line drug,
the first-line drug ended up working more than 90 percent of the time. When
clinicians chose an inappropriate first-line drug, the algorithm chose an
appropriate first-line drug almost half of the time. 

MIT professor David Sontag says that a system like this
could be used when a patient comes into the emergency room or their primary
physician’s office with a suspected UTI. Even when the infection is confirmed,
the specific bacteria is still unknown, making it difficult to choose a
treatment plan. That’s where the algorithm comes in, and makes a suggestion
using electronic health record (EHR) data from more than 10,000 patients from
Brigham & Women’s Hospital and Massachusetts General Hospital (MGH). 

Presented in an article appearing in the Science Translational Medicine journal, the team’s system
features a thresholding algorithm that the team hopes will be intuitive for
clinicians to apply to a wide range of drugs that all face a similar dilemma:
how to balance the need for an effective treatment with the desire to minimize
the use of second-line antibiotics. They also structured their model to be
directly embedded into the EHR, eliminating unnecessary steps and additional

“With this
algorithm we can actually ask the doctor what specific probability of treatment
failure they’re willing to risk in order to reduce the use of second-line drugs
by a certain amount,” says Sanjat Kanjilal, a Harvard Medical School lecturer,
infectious diseases physician and associate medical director of microbiology at
the BWH. Kanjilal and Sontag co-wrote the new paper with researchers at
Carnegie Mellon University and MGH.

As an example of how the threshold algorithm works, UTI
treatments are extremely unlikely to lead to life-threatening side effects, so
a doctor might set the threshold treatment failure at a relatively high number
like 10 percent. In contrast, treatments for certain bloodstream infections
have a much higher risk of death, so in those cases a doctor would likely set
the treatment failure much lower, such as one percent. (Even at such a low
threshold for failure, Kanjilal feels the algorithm could lead to additional
improvements, but that requires further study.)

The project is part of a larger wave of machine learning
models that have been used to predict antibiotic resistance in infectious
syndromes such as bloodstream infections and using pathogen genomic data. While many of these
approaches provide new clinical information, most of them haven’t been widely
adopted due to their lack of interpretability, difficulty integrating into
clinical workflows, and absence of evidence proving that they work in actual
hospital settings. 

“What’s exciting about this research is that it presents a
blueprint for the right way to do retrospective evaluation,” says Sontag. “We
do this by showing that one can do an apples-to-apples comparison within the
existing clinical practice. When we say we can reduce second-line antibiotic
use and inappropriate treatment by certain percentages, we have confidence in
those numbers relative to clinicians.” 

The team is
quick to point out that they haven’t tested their algorithm on more complicated
forms of UTIs that involve pre-existing conditions, and that the ultimate proof
of utility can only be assessed using a randomized controlled trial. However,
they say that the vast majority of UTI cases are compatible with the system. 

Moving forward, Sontag says that future efforts will focus
on doing a randomized controlled trial comparing usual practice to algorithm
supported decisions. They also plan to increase the diversity of their sample
size to improve recommendations across race, ethnicity, socioeconomic status,
and more complex health backgrounds. 

Sontag and Kanjilal co-wrote the paper with MIT graduate
student Michael Oberst, MIT undergraduate Sooraj Boominathan, Carnegie Mellon
PhD student Helen Zhou, and Dr. David C. Hooper, chief of MGH’s infection
control unit. Sontag is an associate professor in the Department of Electrical
Engineering and Computer Science (EECS) and the Institute for Medical
Engineering and Science (IMES).

This project was supported in part by the MGH-MIT Grand
Challenges Award, a Harvard Catalyst grant, and a National Science Foundation
CAREER award.