Machine learning algorithms show applications in OAB, antibiotic resistance

In this video, Glenn T. Werneburg, MD, PhD, discusses the abstracts "Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort” and "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection,” which were presented at the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction 2024 Winter Meeting in Fort Lauderdale, Florida. Werneburg is a urology resident at Glickman Urological & Kidney Institute at Cleveland Clinic, Cleveland, Ohio.

Transcription:

Dr. Werneburg discusses the SUFU abstract "Machine learning algorithms demonstrate accurate prediction of objective and patient-reported response to botulinum toxin for overactive bladder and outperform expert humans in an external cohort."

We'll start with our efforts regarding overactive bladder. Overactive bladder is a common and costly condition. It includes symptoms of urgency, frequency, and urinary incontinence. Patients who don't respond to behavioral therapies or medical management generally require a third-line therapy. That third-line therapy is usually either sacral neuromodulation or onabotulinumtoxinA injection into the bladder. For these patients, many of them would do well with either of these therapies, but a subset would not respond to them. Our goal was to identify which patients would respond and which patients would not respond to these different treatments for medically refractory overactive bladder. To do so, we developed machine learning algorithms. We assembled a team, of course with urologists, but also with quantitative mathematicians, who have lots of experience making predictions regarding stock market fluctuations and things like this. And we developed these algorithms to predict the responder vs the nonresponder status of our patients. We defined how a patient responded based on whether they had a reduction in urge incontinence episodes following the treatment, and whether they perceived an improvement in their symptoms following the therapy. What we found was that we were able to identify patients who responded vs didn't respond to the treatment with a high degree of accuracy. The accuracy surpassed even that of human experts and other standard algorithms. We found this really encouraging. The algorithms even held up in an external validation set, so when we trained the algorithms on 1 data set, and then validated them on a very different group of patients, they still were able to predict who would respond vs not respond to the treatment.

Dr. Werneburg discusses the SUFU abstract "Machine learning algorithms predict urine culture bacterial resistance to first line antibiotic therapy at the time of sample collection."

Our other study was a very different application. We were looking at what antibiotics would be optimally suited for which particular patients. One of the major threats to humanity right now is antibiotic resistance. The World Health Organization tells us it's 1 of the top 10 greatest threats. One of the main drivers for antibiotic resistance is overuse and misuse of antibiotics. One of the issues in urology is that a urine culture, which is commonly used to diagnose urinary tract infection, takes 3 days to results. In that time period, the clinician provides the best treatment based on his or her clinical judgment. But we know from the literature that about 30% of the time, this isn't the optimal therapy, and this therapy needs to be changed once the final urine culture results come back. So we set out with a goal to optimally predict which antibiotic would be most suitable for which patient at the time the urine culture is ordered, so it equates to 3 days prior to the final results. The idea was that if we target the therapy very specifically, we can improve our antibiotic usage and improve the time to the resolution of symptoms for our patients. It's beneficial at the patient level and beneficial at the population level. We developed a series of algorithms. We focused on only the most clinically relevant antibiotics. We trained these algorithms on a large data set of our patients - about 6.6 million cases were used. And then we validated them on another set of patients and we tested how they would perform in terms of predicting which cultures would be sensitive and which cultures would be resistant to these different antibiotics. They held up very well. The algorithms' accuracy was really good, and it held up even in an external data set. We find this really encouraging. We have some more validation to do. We're really looking forward to being able to implement these algorithms clinically to improve patient care and also to improve our antibiotic stewardship and reduce our selection for antibiotic resistance at the population level.

This transcription was edited for clarity.