Clinical Impact of a Machine Learning Decision Support System for Empirical Antibiotic Therapy: A Prospective Quasi-Experimental Study
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 486
- 主要终点
- Clinical success
研究概览
简要总结
The goal of this quasi-experimental study is to analyze if a Machine Learning Clinical Decision Support System can improve the empirical antibiotic treatment in patients with pneumonia, urinary tract infection and / or sepsis.
The main questions it aims to answer are:
- Primary outcome: clinical success defined as clinical cure (resolution of all signs and symptoms related to infection); no complications until day 30 (recurrence, or development of adverse events- AEs-); no new acquisition of MDROs; and survival at day 30.
- Secondary outcomes: a subgroup analysis of the primary outcome according to the department participants, infectious syndrome, severity of the infection assessed by the SOFA score, and in microbiological confirmed infections. In microbiological confirmed infections, desirability of Outcome Ranking (DOOR) for the Management of Antimicrobial Therapy (MAT) according to the beta-lactam classification
Researchers will compare a pre-intervention group with a post-intervention to see if improve in the DOOR MAT score
Participants in the post-intervention group will:
• Received empirical antibiotic therapy prescribed by their treating physicians according to the machine-learning recommendations
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Sequential
- 主要目的
- Treatment
- 盲法
- None
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
- •Adult patients (aged ≥18 years)
- •Admitted to the Nephrology, Oncology or ICU wards
- •Diagnosis of sepsis, pneumonia and/or UTI
- •Empirical antibiotics prescribed
排除标准
- •informed consent obtained > 48 hours since the infection onset
- •beta-lactam allergy
- •infection syndrome other than sepsis, pneumonia or UTI
- •confirmed no-bacterial infection
- •death within the first 48 hours of inclusion or imminent risk of death at time of the inclusion
- •pregnancy and/or breastfeeding
- •inclusion in a clinical trial of antimicrobial treatment
研究组 & 干预措施
Pre-interventional
Hospital standard practices for diagnosis and/or treatment applied
Interventional
Hospital standard practices + use of a CDSS tool
干预措施: Machine Learning Decision Support System (Other)
结局指标
主要结局
Clinical success
时间窗: 30-Day
resolution of all signs and symptoms related to infection with no complications and survival
次要结局
未报告次要终点
研究者
Sofia De La Villa
Principal Investigator
Instituto de Investigación Sanitaria Gregorio Marañón