A Novel Approach to Antimicrobial Resistance: Machine Learning Predictions for Carbapenem-Resistant Klebsiella in ICUs
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 289
- 试验地点
- 1
- 主要终点
- Risk of Carbapenem Resistant Klebsiella Infection
研究概览
简要总结
The aim of this study to predict carbapenem resistant Klebsiella spp. earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence.
Patients with bloodstream infection and pneumonia caused by Klebsiella spp. will be comparatively examined in two groups, as sensitive and resistant. Resistance will be attempted to be predicted with deep machine learning.
详细描述
Antimicrobial resistance is a globally increasing threat and has serious consequences on both public health and the economy. In an infection that may develop with a resistant microorganism, therapeutic options are limited, hence early and effective treatment that can be initiated by predicting resistance will make a difference in patient prognosis.
Today, artificial intelligence and machine learning are changing our medical practice. When the literature is reviewed, there are studies suggesting that machine learning can predict antimicrobial resistance.Risk factors for carbapenem-resistant Klebsiella spp. have been previously identified. These previously identified risk factors will be evaluated retrospectively in our own patients and an algorithm related to the prediction of resistance will be developed with the help of machine learning.
Our goal is to predict bacterial resistance earlier in our patients monitored in our Intensive Care Unit in the future, using artificial intelligence, and to facilitate our patients' access to early and effective treatment options.
Secondarily, it is also aimed to provide economic benefits by preventing unnecessary antibiotic use.
Access to patients' data will be obtained retrospectively through the hospital automation system.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 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
- •Patients monitored in our third-level intensive care unit between June 2017 and June 2023 will be evaluated retrospectively. Patients with pneumonia and bloodstream infection developed with Klebsiella spp. will be included in the study.
排除标准
- •Patients under the age of 18 have not been included in the study.
- •Infections outside of the respiratory tract and bloodstream have not been included in the study.
- •Patients with respiratory tract colonization and without active inflammation have also not been included.
结局指标
主要结局
Risk of Carbapenem Resistant Klebsiella Infection
时间窗: 3 months
The sensitivity and specificity of a diagnostic method based on machine learning will be measured with the AUC-ROC curve (Area Under the Receiver Operating Characteristic curve)
次要结局
未报告次要终点
研究者
Volkan Alparslan
Medical Doctor
Kocaeli University