Application of a Prediction Model for Directing Antibiotic Use in the Treatment of Urinary Tract Infection in an Ambulatory Setting
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Decision Aid-prediction model.
- Who it may be relevant to
- Registry conditions: Urinary Tract Infections. Basic parameters: from 18 years · Female.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Device Decision Aid-prediction model
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 patie
Primary outcome measures
- Number of antibiotic free days as measured by medical record review. [Time frame: Up to 2 weeks]
Secondary outcome measures (7)
- Percentage of antibiotic prescriptions for patients discharged from the ER as measured by medical record review. [Time frame: Up to 2 weeks]
- Number of hospitalization since index ER visits as measured by medical record review. [Time frame: Up to 2 weeks]
- Number of ER readmission as measured by medical record review. [Time frame: Up to 2 weeks]
- Number of unscheduled primary care visits as measured by medical record review. [Time frame: Up to 2 weeks]
- Percent of false positive urinalysis as measured by discordance with culture obtained at time of ER visist [Time frame: Baseline]
- Percent of false negative urinalysis as measured by discordance with culture obtained at time of ER visist [Time frame: Baseline]
- Percentage of non-UTI associated urologic diagnoses as measured by medical record review [Time frame: Up to 2 weeks]
Eligibility criteria
Inclusion criteria
- 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
Exclusion criteria
- 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)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- N/A
- Model
- Single group
- Masking
- Open label
- Primary purpose
- Prevention
Study locations
United States · 1 center
- University Hospitals Cleveland Medical Center — Cleveland
Identifiers
NCT: NCT06976125 · STUDY20250812