Sepsis, Septic Shock
Conditions
Keywords
Antimicrobial resistance, sepsis, predictive models
Brief summary
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.
Detailed description
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 1a- Estimate the prevalence of AMR/resistance patterns with clinical significance. 1b- Describe the predictive factors associated with AMR in this population. 1c- Generally, and in clinically relevant subgroups: by probable focus, clinically relevant pathogens, severity. 2- Generation and Validation of Predictive Models 2a- Generate and validate clinically useful predictive models to predict the probability of AMR. 2b- Generate and validate clinically useful predictive models to predict the probability of common/relevant pathogens. 2c- Evaluate the performance of stepwise predictive models in three stages: 1. Prediction of positive culture, 2. Intermediate prediction of pathogen, and 3. Prediction of AMR. 2d- Describe and evaluate point statistics on deterministic and unpredictable individuals based on the best predictive models. 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). Explanatory variables - Potential predictors of resistance include: Patient characteristics, Invasive devices, Immunosuppression, Comorbidities, Therapeutic adequacy, Medical history, Antibiotic use, Clinical status and Diagnostic studiesDenominators 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. The sample will be randomly divided into a generation sample (2/3 of the sample) and a validation sample (1/3 of the sample). For the generation and validation of predictive models, the positive culture, each selected relevant bacteria, MDRO, MDR, XDR, PDR will be used as outcome variables. List of Abbreviations (Abbreviation - Meaning) ABA - Acinetobacter baumannii AMR - Antimicrobial Resistance ESBL - Extended Spectrum Beta-Lactamase-producing Enterobacterales ESKAPE - Acronym summarizing the main clinically relevant resistant germs currently, each letter represents the initial of the scientific name of the bacterium: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp. CPE - carbapenemase-producing Enterobacteriaceae GNB - Gram-Negative Bacilli GPC - Gram-Positive Cocci KPC - Carbapenem-resistant Klebsiella pneumoniae MBL - Metallo-beta-lactamase MDR - Multidrug-resistant MOR - Multidrug-resistant Organisms MRSA - Methicillin-resistant Staphylococcus aureus OXA - Oxacillinase-type Carbapenemase PAE MR - Multidrug-resistant Pseudomonas aeruginosa PDR - Pan-resistant SOFA - Sepsis-related Organ Failure Assessment SSC - Surviving Sepsis Campaign VRE - Vancomycin-resistant Enterococci XDR - Extremely resistant
Interventions
None listed
Sponsors
Hospital Italiano de Buenos Aires
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Adults aged 18 years or older. * Attended at the Adult Emergency Department of Hospital Italiano de Buenos Aires during the periods: * January 1, 2017 - March 20, 2020, or * May 1, 2022 - August 10, 2025. * Sepsis or septic shock at presentation and at least 48 hours of observation or hospital admission. * Bacterial cultures obtained during the initial evaluation.
Exclusion criteria
* No indication for antibiotic therapy within the first 48 hours of hospital admission. * No bacterial cultures performed within the first 48 hours of hospital admission. * SARS-CoV-2 infection diagnosed within the first 72 hours of hospital admission.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Proportion of episodes with multidrug-resistant organisms (MDRO) | 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 |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Bacterial species (Number of Participants with each microorganism species) | Baseline | Categorical variable; the following will be considered: Escherichia coli, Salmonella, Shigella, Klebsiella pneumoniae, Proteus mirabilis, other Proteus species (P. penneri or P. vulgaris), Morganella morganii, Providencia stuartii, Providencia rettgeri, Serratia marcescens, Citrobacter koseri, Citrobacter freundii, Enterobacter cloacae, Klebsiella aerogenes, Hafnia alvei, Pseudomonas aeruginosa, Acinetobacter baumannii, Stenotrophomonas maltophilia, Enterococcus faecalis, Enterococcus faecium, Staphylococcus aureus, coagulase-negative Staphylococcus, Streptococcus pneumoniae, group A beta-hemolytic Streptococcus, Streptococcus pyogenes, Haemophilus influenzae, Neisseria meningitidis, Listeria monocytogenes, others. |
| Enzymatic resistance mechanisms (Number of Participants with each enzymatic mechanism detected) | Baseline | Categorical variable, the following will be considered: No enzymatic mechanism; MRSA; VRE; KPC; MBL; OXA. |
| Carbapenemase genotypes (Number of participants with each genotypic resistance mechanism -KPC, NDM, VIM, OXA-48, IMP- detected by multiplex PCR assay) | Baseline | Categorical variable; the following will be considered: Types of carbapenemases if studied (for example, KPC, NDM, VIM, IMP, OXA-48 like). |
| Culture positivity | Baseline | Dichotomous variable. It will be considered positive or negative. |
| Proportion of episodes with XDR organisms | Baseline | Percentage of culture-positive episodes classified as XDR (non-susceptible to ≥1 agent in all but ≤2 categories). Unit of Measure: % of culture-positive episodes |
| Proportion of episodes with PDR organisms | Baseline | Percentage of culture-positive episodes classified as PDR (non-susceptible to all agents in all categories). Unit of Measure: % of culture-positive episodes |
| Proportion of episodes with MDR organisms | Baseline | Percentage of culture-positive episodes classified as MDR (resistant to ≥1 agent in ≥3 antimicrobial categories). Unit of Measure: % of culture-positive episodes |
Contacts
Primary ContactEmilio Felipe Huaier Arriazu, MD
Outcome results
None listed