Evaluation of the Artificial Intelligence-based Prescription Support Software iAST® for the Choice of Empirical and Semi-targeted Antibiotic Treatment
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 325
- 试验地点
- 1
- 主要终点
- To demonstrate the non-inferiority of iAST® compared to physicians for the prescription of the empiric and semitargeted antibiotic therapy in patients with common infectious diseases.
研究概览
简要总结
Inadequate treatment of infections frequently leads to complications that cause new visits to the doctor, lengthen hospital stays and can lead to sepsis, even causing the death of affected patients. Several scientific studies have documented that up to 20%-30% of antibiotic prescriptions are incorrect and do not cover the microorganism causing the infection. iAST® is a simple antibiotic prescribing aid tool that applies complex algorithms based on the latest artificial intelligence technologies to accurately predict the best specific antibiotic for a patient, before knowing the definitive microbiological results (bacterial identification and antibiogram). The objective of the present trial is to demonstrate the non-inferiority of iAST® with respect to physicians for the appropriate choice of empiric and semi-directed therapy of common infectious diseases, including sepsis, urinary tract infections and ventilator-associated pneumonias or tracheobronchitis. The adequacy of the medical prescription and the iAST® prediction will be compared taking the antibiogram report as a reference. The study design is retrospective, so that no intervention will be done on the patients. The investigators will conduct a retrospective search for infection cases and note the antibiotic treatment prescribed by the doctors. In parallel, they will enter basic patient data such as age, sex, service where they were treated, type of infection and microorganism (in the case of semi-directed treatment evaluation) into the iAST® software and will write down the first three treatment options recommended by the tool. The treatments of both arms (medical treatment and iAST® prediction) will be compared with the microbiological results and the success rate of each of them will be calculated.
详细描述
Background:
Infections are one of the main causes of consultation in primary care and emergency services. In addition, a high percentage of the patients admitted to hospitals suffer from an infection during their stays. According to data from the European Center for Disease Prevention and Control (ECDC), approximately 11% of patients admitted to European hospitals suffer from a healthcare-associated infection. Moreover, according to this organization, 35% of patients admitted to European hospitals are under antibiotic therapy, with this percentage varying between 21.4% and 54.7% depending on the hospital and the country.
Inadequate treatment of infections often leads to complications associated with an extension in hospitalization periods or sepsis development, which finally could cause the death of the affected patients. Moreover, ineffective treatments due to an inappropriate antibiotic selection have an enormous cost and impact to health care systems. Conversely, there is extensive scientific evidence that early initiation of adequate antibiotic treatment greatly reduces the morbidity and mortality of infections and significantly reduces patient hospital stays. Previous studies have reported that 20-30% of antibiotic prescriptions are inadequate, leading to health complications, especially health care associated infections. Moreover, an adequate selection of antibiotic treatment avoids the spread of resistant bacteria strains, which has become an increasing problem in recent years.
As previously noted, infectious mortality increases enormously over time if an adequate antibiotic treatment is not initiated. Thus, obtaining microbiological results and identify the bacteria strain causing the infection is crucial to provide effective treatments to the subjects. The microbiological profile description is normally performed by microbiology laboratories, which support the doctors in the antibiotic treatment selection. Nevertheless, although reliable results are generated, there are usually obtained 48 hours after the initial patient evaluation.
Rationale:
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Data for analysis should proceed from subjects over 18 years old that were admitted into HM Hospitals from 01Feb
- •Subjects who:
- •have attended the Emergency Department of the hospital with suspected urinary tract infection (UTI) or;
- •have presented an episode of bacteremia/sepsis at/during hospital admission or;
- •have been admitted to the hospital ICU and presented a tracheobronchitis or pneumonia associated with mechanical ventilation or;
- •have presented another type of infection, were treated and which have a bacterium identified with an antibiogram result.
排除标准
- •Patients with concomitant infections.
- •Data from subjects suffered from infections with no bacterial etiology: fungal or viral infections.
- •Data from subjects with infections without microbiological documentation (including antibiogram results).
- •Data from subjects prescribed with more than one antibiotic.
结局指标
主要结局
To demonstrate the non-inferiority of iAST® compared to physicians for the prescription of the empiric and semitargeted antibiotic therapy in patients with common infectious diseases.
时间窗: 4 months
The appropriateness of antibiotic prescription and the iAST® prediction will be compared with the results from the antibiogram report as standard.Two-sided 95% confidence intervals (CIs) for the difference between treatments will be calculated using the unstratified method of Miettinen and Nurminen. The demonstration of non-inferiority of iAST® to doctor prescription for both primary and secondary efficacy endpoints will be established if the lower limit of the two-sided 95% CI for the treatment difference exceeded 5%. Additionally, a p-value will be computed for the corresponding one-sided non-inferiority hypothesis test.
次要结局
- To assess the accuracy in the antibiotic prescription from the physicians and the software iAST® predictions (for empiric and semitargeted therapy) compared to the antibiogram report, respectively.(4 months)
- To evaluate the software iAST® accuracy in the antibiotic prediction of the 4 study population subgroups compared to the antibiogram report as standard.(4 months)
- To compare the rate of used/recommended antibiotics from the Access, Watch and Reserve antibiotics list (from the WHO Aware classification), between the prescriptions from physicians and the iAST® software predictions.(4 months)
- To collect information related to user experience by completing a usability questionnaire by physicians when working with the software iAST®.(4 months)