Application of a Prediction Model for Directing Antibiotic Use in the Treatment of Urinary Tract Infection in an Ambulatory Setting

NCT06976125 | Recruiting DMC FDA Device

• 1 other identifier: STUDY20250812
• Study Type: interventional
• Target: 47
• Locations: 1 country
• Sites: 1

Brief Summary

Urinary tract infection (UTI) is when bacteria enter the urinary system and cause an infection. UTIs cause symptoms including burning when peeing, a feeling of an increased urge to pee, and cloudy or strong-smelling urine. Sometimes, severe UTIs can also cause fever, abdominal pain, and/or lower back pain. In the emergency department (ED), healthcare providers rely on symptoms, along with a urine analysis and a urine culture to diagnose a UTI. A urine analysis involves taking a sample of urine and analyzing different factors like color, acidity, presence of blood cells, presence of bacteria. An abnormal urine analysis increases the likelihood that patients might have a UTI, but it does not confirm it. A positive urine analysis will lead to provider's sending a sample of urine for a urine culture. A urine culture is used to grow whatever bacteria is in the collected urine. If growth is seen on the culture, then this confirms a patient has a UTI. This also specifies which bacteria grew on the culture. The lab can also take it a step further and do an antibiotic test to check which antibiotic the bacteria is sensitive to. When a urine analysis comes back abnormal in an ER setting, patients are prescribed an antibiotic before the culture and antibiotic sensitivity tests come back. If a patients condition is not critical, they will be discharged home before the culture results come back. If the culture comes back positive, the pharmacists will evaluate the culture and antibiotic sensitivity tests, then call patients to inform them whether they are taking a suitable antibiotic. This study aims to decrease the unnecessary use of antibiotics because this contributes to antibiotic resistance which is considered a global public health issue. Antibiotic resistance occurs when bacteria develop the ability to withstand certain antibiotics that used to be effective against them, which makes it difficult to treat the infection. One of the factors that increase the risk of antibiotic resistance is the overuse of antibiotics. In this study, investigators will be incorporating a prediction model and a negative callback system to decrease unnecessary antibiotic use.

Conditions

Urinary Tract Infections

Keywords

ER visits; Antibiotic use

Outcome Measures

Primary Outcomes (1)

• Number of antibiotic free days as measured by medical record review.

  Up to 2 weeks

Secondary Outcomes (7)

• Percentage of antibiotic prescriptions for patients discharged from the ER as measured by medical record review.

  Up to 2 weeks

• Number of hospitalization since index ER visits as measured by medical record review.

  Up to 2 weeks

• Number of ER readmission as measured by medical record review.

  Up to 2 weeks

• Number of unscheduled primary care visits as measured by medical record review.

  Up to 2 weeks

• Percent of false positive urinalysis as measured by discordance with culture obtained at time of ER visist

  Baseline

Study Arms (1)

Presenting to ER for Urinary Tract Infection (UTI)

EXPERIMENTAL

Patients presenting to the a UH ER location for UTI symptoms.

Device: Decision Aid-prediction model

Interventions

Decision Aid-prediction model DEVICE

ER physician will input the necessary de-identified data into the decision aid application. The decision aid determines if the patient has a high or low likelihood of having a positive urine culture. The patient with high likelihood of positive culture, will be prescribed empiric antibiotics per the UH guidelines for treating UTI in the ambulatory setting. Patients with a low likelihood of having a positive culture, will be discharged without antibiotics. Study team members will give the patient a handout describing what will happen in the event of a positive or negative culture. The culture call-back team, consisting of clinical pharmacists, will be notified.

Presenting to ER for Urinary Tract Infection (UTI)

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: Female sex
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Female sex
• Age \>18 years old
• Discharged from the hospital after ER visit
• Discharge ICD code consistent with a UTI diagnosis
• Antibiotic prescribed for UTI at the time of discharge

You may not qualify if:

• Male sex
• Necessity for chronic bladder catheterization or discharge with a urinary catheter
• Patients who have an Emergency Severity Index (ESI) of 1 and 2
• Patients who verbalize to the study team member that their pain is a 6 or higher
• Patient set to be transferred to inpatient care
• History of bladder augmentation
• Pregnancy (this will be confirmed with a negative pregnancy test which is ordered in the ER)

Sponsors & Collaborators

• University Hospitals Cleveland Medical Center: lead

Study Sites (1)

University Hospitals Cleveland Medical Center

Cleveland, Ohio, 44106, United States

RECRUITING

MeSH Terms

Conditions

Urinary Tract Infections

Condition Hierarchy (Ancestors)

Infections; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Study Officials

• David Sheyn, MD

  University Hospitals Cleveland Medical Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jessica Abou Zeki

CONTACT

216-286-0603; Jessica.AbouZeki@UHhospitals.org

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Physician

Study Record Dates

• First Submitted: May 9, 2025
• First Posted: May 16, 2025
• Study Start: February 20, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: April 8, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)