Predictive Model for Multidrug Resistance in Patients Admitted to the Emergency Department With Sepsis
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 10,000
- 主要终点
- Proportion of episodes with multidrug-resistant organisms (MDRO)
研究概览
简要总结
Introduction: Timely and accurate antibiotic administration in emergency department (ED) patients with sepsis or septic shock is vital, given mortality rates of 20% and over 40%, respectively. In high antimicrobial resistance (AMR) settings, selecting effective empirical antibiotics is challenging, requiring a balance between efficacy and minimizing multidrug-resistant organism (MDRO) emergence. A predictive model estimating AMR probability could optimize antibiotic use, improve outcomes, and reduce resistance. Although risk factors are known, no single validated model exists for predicting multidrug resistance in sepsis. Accurate prediction must integrate patient history, pathogen profiles, infection source, and antibiotic characteristics.
Objectives: To estimate AMR prevalence in adult ED patients with sepsis or septic shock and develop a validated predictive model estimating AMR probability and likely pathogens. The model will follow a three-phase approach: (1) predict culture positivity, (2) estimate pathogen likelihood, and (3) predict AMR. Additionally, we aim to describe individual-level statistics for both predictable and unpredictable cases based on model performance.
Methods: A cross-sectional study will be conducted at Hospital Italiano's adult ED over 70 months (Jan 1, 2017-Mar 20, 2020 and May 1, 2022-Aug 10, 2025), excluding the COVID-19 period. Primary outcomes include culture positivity, bacterial species, and MDRO prevalence. Frequency analyses will use positive cultures, species, and resistance classifications (MDRO, MDR, XDR, PDR), including mechanisms (e.g., MRSA, ESBL, KPC, MBL, OXA). Denominators will include all sepsis patients and, separately, culture-positive cases. Confidence intervals (95%) will be calculated using normal approximation. Multivariate logistic regression with backward stepwise selection will identify predictors and interactions. A hierarchical model will be developed based on culture results, pathogen identification, and resistance profiles.
详细描述
Introduction:
Timely and accurate antibiotic administration in emergency department (ED) patients with sepsis or septic shock is vital, given mortality rates of 20% and over 40%, respectively. In high antimicrobial resistance (AMR) settings, selecting effective empirical antibiotics is challenging, requiring a balance between efficacy and minimizing multidrug-resistant organism (MDRO) emergence. A predictive model estimating AMR probability could optimize antibiotic use, improve outcomes, and reduce resistance. Although risk factors are known, no single validated model exists for predicting multidrug resistance in sepsis. Accurate prediction must integrate patient history, pathogen profiles, infection source, and antibiotic characteristics.
Objectives
In adult patients who present to an emergency department in a tertiary care center with sepsis or septic shock:
1-Prevalence and Associated Factors
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
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
- 未提供
排除标准
- 未提供
结局指标
主要结局
Proportion of episodes with multidrug-resistant organisms (MDRO)
时间窗: Baseline (within the first 48 hours of admission)
Percentage of episodes with a positive bacterial culture that meet criteria for multidrug resistance (MDR or greater, i.e., MDR/XDR/PDR), defined according to Magiorakos et al., 2012. Unit of Measure: % of culture-positive episodes
次要结局
- Culture positivity(Baseline)
- Bacterial species (Number of Participants with each microorganism species)(Baseline)
- Enzymatic resistance mechanisms (Number of Participants with each enzymatic mechanism detected)(Baseline)
- Carbapenemase genotypes (Number of participants with each genotypic resistance mechanism -KPC, NDM, VIM, OXA-48, IMP- detected by multiplex PCR assay)(Baseline)
- Proportion of episodes with MDR organisms(Baseline)
- Proportion of episodes with XDR organisms(Baseline)
- Proportion of episodes with PDR organisms(Baseline)
研究者
EMILIO FELIPE HUAIER ARRIAZU
Infectious disease physician, member of the Infectious disease department and the Infection control committee of Hospital Italiano de Buenos Aires
Hospital Italiano de Buenos Aires