Development of Risk Score Model and Decision Tree Algorithm for Predicting Bloodstream Infections (BSIs) or Other Invasive Infections With Carbapenem Resistant Klebisella Penumoniae (CRKp) in CRKp Colonized Patients (DETERMINE)
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 520
- 试验地点
- 1
- 主要终点
- The factors that are associated with the development of subsequent BSI or other types of invasive infection with CRKp in CRKp carriers.
研究概览
简要总结
DETERMINE trial is a prospective multicenter multinational cohort study. This study will be carried out to predict the risk of bloodstream infections (BSIs) or other types of invasive infection with carbapenem resistant K.pneumoniae in patients being colonized by CRKp. The results of DETERMINE trial would be quite important to prevent unnecessary coverage of carbapenem resistant Klebsiella pneumoniae in empirical treatment of colonized patients. In this study, both risk score model and decision tree algorithm will be constructed and compared with each other in terms of sensitivity, specificity, positive predictive value and negative predictive value.
详细描述
-
Main Objectives:
-
Building a risk score model and decision tree algorithm with an acceptable certainty for early prediction of BSI or other invasive infections caused by CRKp in CRKp carriers.
-
Comparison of risk score model and decision tree algorithm in terms of sensitivity and specificity rates, positive predictive and negative predictive values
-
Evaluation of risk score model and decision tree algorithm in terms of convenience for routine clinical use
-
Hypothesis
2.1 Main hypothesis:
Presence of immunosuppression, invasive devices, gastrointestinal surgery within prior 3 months, multi-site colonization beside stool, older age, diabetes as a comorbidity, admission to ICUs, presence of carbapenemase gene in CRKp that causes colonization, type of carbapenemase (eg. blaOXA-48 and blaKPC ), high SOFA score, high APACHEII score, high ECOG score (>2), short time interval between identification of colonization and development of BSI or other type of invasive infection are independent risk factors for development of subsequent BSI or other invasive infections in CRKp colonized participants
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
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
- •All adult (≥18 years) patients having rectal colonization or history of previous invasive infection with CRKp
- •Group 1 cases are constituted by one BSI episode or non-bactereamic invasive infection episode (eg. pneumonia, intra-abdominal infection or urinary tract infection) with CRKp and a positive rectal swab screening or invasive infection (e.g. pneumonia, urinary tract infection and BSI) with CRKp within 90 days before identification of index BSI or other invasive infection with CRKp
- •Group 2 cases who are colonized with CRKp or had invasive infection (e.g. pneumonia, urinary tract infection and BSI) with CRKp within 90 days before identification of index BSI or other types of invasive infection with any bacteria other than CRKp and develop subsequent BSI or non-bactereamic invasive infection with these bacteria
- •Group 3 cases involve the colonized patients with CRKp who do not develop subsequent BSI or other invasive infections with CRKp or any other bacteria
排除标准
- •<18 years old patients
- •Palliative patients
- •Pregnant and breast-feeding patients
- •Patients who cannot be followed through 90-days.
- •Patients who are de-colonized with antibiotics, prebiotics-probiotics or fecal microbiota transplantation
结局指标
主要结局
The factors that are associated with the development of subsequent BSI or other types of invasive infection with CRKp in CRKp carriers.
时间窗: 90-day
The independent risk factors (eg. presence of central venous catheter and presence of absolute neutropenia) for development of BSI or other invasive infections wtihin 90-days follow-up in CRKp carriers will be analyzed by constructing mutli-variate logistic regression analysis model.
次要结局
- Calculation of sensitivity and specificity rates, positive and negative predictive values of decision tree algorithm.(90-days)
- Calculation of sensitivity and specificity rates, positive and negative predictive values of risk score model.(90-day)