Application of a Prediction Model for Directing Antibiotic Use in the Treatment of Urinary Tract Infection in an Ambulatory Setting
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 47
- 试验地点
- 1
- 主要终点
- Number of antibiotic free days as measured by medical record review.
研究概览
简要总结
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Prevention
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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)
研究组 & 干预措施
Presenting to ER for Urinary Tract Infection (UTI)
Patients presenting to the a UH ER location for UTI symptoms.
干预措施: Decision Aid-prediction model (Device)
结局指标
主要结局
Number of antibiotic free days as measured by medical record review.
时间窗: Up to 2 weeks
次要结局
- Percent of false negative urinalysis as measured by discordance with culture obtained at time of ER visist(Baseline)
- Number of hospitalization since index ER visits 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)
- Percentage of non-UTI associated urologic diagnoses as measured by medical record review(Up to 2 weeks)
- Percentage of antibiotic prescriptions for patients discharged from the ER 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)
- Number of ER readmission as measured by medical record review.(Up to 2 weeks)
研究者
David Sheyn
Physician
Case Western Reserve University