Predictive Model for Multidrug Resistance in Patients Admitted to the Emergency Department With Sepsis
Conditions: Sepsis, Septic Shock
Sponsor: Hospital Italiano de Buenos Aires

 
 
 
Official Title of the study: Predictive Model for Multidrug 
Resistance in Patients Admitted to the Emergency Department 
with Sepsis 
 
date of the document: 08/04/2025 
 
NCT number: not available yet 
 
Protocol ID: 7441 (Institutional Ethics Committee protocol identification provided) 
 
 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
Table of Contents 
I. List of Abbreviations.......................................................................................................... 3 
II. Abstract...............................................................................................................................4 
1. Introduction........................................................................................................................ 5 
1.1 Sepsis and AMR...........................................................................................................5 
1.1.1 Definition of Sepsis and AMR..............................................................................5 
1.2 Sepsis as a Public Health Problem.............................................................................. 5 
1.2.3. Antimicrobial resistance as a global and local issue.......................................... 6 
1.3. Types of AMR..............................................................................................................8 
1.4. Factors Associated with Antimicrobial Resistance (AMR)...........................................9 
1.5. Relevance and Purpose............................................................................................ 11 
2. Question and Objectives................................................................................................. 12 
2.1. Questions.................................................................................................................. 12 
2.2. Objectives..................................................................................................................13 
2.2.1. Prevalence and Associated Factors.................................................................13 
2.2.2. Generation and Validation of Predictive Models...............................................13 
3. Methods............................................................................................................................ 14 
3.1. Design....................................................................................................................... 14 
3.2. Setting....................................................................................................................... 14 
3.3. Population................................................................................................................. 15 
3.3.1. Inclusion Criteria...............................................................................................15 
3.3.2. Exclusion Criteria............................................................................................. 15 
3.3.3. Definitions.........................................................................................................15 
3.4. Data collection...........................................................................................................16 
3.5. Variables....................................................................................................................16 
3.5.1. Outcome variables........................................................................................... 16 
3.5.2. Explanatory variables - Potential predictors of resistance................................19 
4. Statistical Considerations............................................................................................... 22 
4.1. Sampling and Sample Size Calculation.................................................................... 22 
4.2. Statistical Analysis.....................................................................................................22 
4.2.1. Missing Data.....................................................................................................22 
4.2.2. Generalities...................................................................................................... 22 
4.2.3. Descriptive Analysis......................................................................................... 22 
4.2.4. Frequency Calculation......................................................................................23 
4.2.5. Associated Factors...........................................................................................23 
4.2.6. Predictive Models............................................................................................. 23 
4.2.7. Generation of Predictive Models...................................................................... 23 
4.2.8. Validation of Predictive Models.........................................................................23 
5. Ethical Considerations.................................................................................................... 24 
6. Funding............................................................................................................................. 26 
7. Schedule........................................................................................................................... 27 
8. Bibliographic references................................................................................................. 28 
 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
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I. 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. 
EPC 
Enterobacterales productores de carbapenemasas 
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 
 
 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
II. Abstract 
Introduction: Early and accurate antibiotic administration in emergency department sepsis 
and septic shock patients is critical, given mortality rates of 20% and exceeding 40%, 
respectively, making sepsis a leading cause of in-hospital death. In settings with high 
antimicrobial resistance (AMR), effective empirical antibiotic selection is particularly 
challenging, balancing treatment efficacy with the need to prevent multidrug-resistant 
organism (MDRO) emergence. A predictive model for AMR probability could be a valuable 
tool for optimizing antibiotic use, improving patient outcomes, and mitigating AMR. While risk 
factors exist, a single, validated predictive model for multidrug resistance in sepsis is lacking. 
Accurate treatment prediction necessitates integrating patient characteristics and history, the 
causative microorganism and its resistance profile, the infection source, and antibiotic 
properties. Objectives: To estimate the prevalence of AMR in adult patients presenting to an 
adult emergency department with sepsis or septic shock We aim to develop and validate a 
clinically useful predictive model for estimating the probability of AMR and the likelihood of 
specific pathogens in patients. To evaluate the performance of a stepwise, three-phase 
model to: (1) predict culture positivity, (2) intermediate predict the likelihood of certain 
pathogens, and (3) predict AMR. Furthermore, to describe and evaluate individual-level 
statistics for deterministic and unpredictable patients based on the best-performing models... 
Methods: A cross-sectional study will be conducted in individuals presenting with sepsis or 
septic shock to the adult Emergency Department at Hospital Italiano over a total study period 
of 70 months, from January 1, 2017 to March 20, 2020, and from May 1, 2022 to August 10, 
2025, excluding the COVID-19 pandemic period. The study will assess three primary 
outcomes: culture positivity; bacterial species; MDRO prevalence. For frequency analyses, 
numerators will comprise positive cultures, specific bacterial species, and resistance 
classifications (MDRO, MDR, XDR, PDR) including mechanisms (e.g., non-enzymatic, 
MRSA, ESBL, KPC, MBL, OXA). Denominators will be total sepsis patients (including 
culture-negatives) and, separately, culture-positive patients. 95% confidence intervals will be 
estimated using normal approximation for sufficiently large proportions. Multivariate logistic 
regression with backward stepwise selection will model predictors and interactions. A 
predictive model for multidrug resistance will be developed based on the hierarchical 
relationship between culture positivity, pathogen identification, and resistance profiles. 
  
Keywords: Antimicrobial resistance, sepsis, septic shock, predictive models. 
 
 
 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
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1. Introduction 
Antimicrobial resistance (AMR) is one of the main challenges in the treatment of sepsis. On 
the one hand, early initiation of broad-spectrum antibiotics to cover potentially involved 
pathogens is directly associated with therapeutic success [1]. On the other hand, avoiding 
antibiotic overprescription, both in quantity and overly broad spectrum, is one of the key 
measures to combat the global rise in AMR [2–4]. Balancing the need for rapid initiation of 
appropriate antibiotics with the imperative of rational use without overextending antimicrobial 
coverage represents a major challenge when making decisions to determine optimal 
empirical antibiotic therapy [5]. 
1.1 Sepsis and AMR 
1.1.1 Definition of Sepsis and AMR 
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) 
defines sepsis as a life-threatening organ dysfunction caused by a dysregulated host 
response to infection [6]. This description was the first to highlight the vital role of both innate 
and adaptive immune responses in the early stages of disease. Septic shock is its most 
severe form, characterized by a drop in blood pressure that reduces tissue perfusion 
pressure, causing the characteristic hypoxia of shock [7]. Patients with septic shock can be 
clinically identified by the requirement for vasopressors to maintain a mean arterial pressure 
of ≥65 mm Hg and a serum lactate level >2 mmol/L (>18 mg/dL) in the absence of 
hypovolemia. Among patients with sepsis, 4–7.4% will progress to septic shock regardless of 
management [8]. 
AMR is the phenomenon by which bacteria develop the ability to resist or evade antibiotics 
or antiseptics designed to kill them, allowing them to continue growing and spreading [9,10]. 
While the impact of AMR in sepsis remains a matter of controversy, and its effect on sepsis 
mortality is debated, several studies have reported unfavorable outcomes such as prolonged 
hospital stays or rehospitalizations [11], mainly due to delays in administering an effective 
antibiotic. 
1.2 Sepsis as a Public Health Problem 
The incidence and mortality of sepsis vary considerably across regions, with the highest 
burden in low- and middle-income countries [12,13]. The age-standardized incidence rate of 
sepsis worldwide is 677.5 cases per 100,000 people [12]. In North America, the incidence 
ranges from 500 to 1,000 cases per 100,000 people [14], and in Europe, between 400 and 
800 cases per 100,000 people [15]. It is more common in low- and middle-income countries, 
with rates exceeding 1,500 cases per 100,000 people [16,17]. In 2017, approximately 48.9 
million incident cases of sepsis were recorded globally, with 11 million sepsis-related deaths 
reported, representing 19.7% of all global deaths [12]. 
Sepsis has an approximate mortality rate of 20% [18], and septic shock can exceed 40% 
[19,20]. It is the leading cause of in-hospital mortality [21]. Mortality rates also vary 
significantly between geographic areas, from 15–25% in high-income countries to 30–40% in 
middle-income countries [19], and significantly higher in low-income countries, frequently 
exceeding 40% in sepsis and 50% in septic shock [22,23]. Sepsis mortality rates have 
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improved over time, especially in high-income countries; for example, in the U.S., sepsis 
mortality rates have decreased from over 35% in the early 2000s to 15–20% [24,25]. In 
middle- and low-income countries, rates have remained high over time, indicating the need 
for better access to healthcare resources and implementation of evidence-based treatment 
practices [26,27]. Notable differences exist in the prevalence of sepsis and septic shock 
between industrialized and low-income countries. Sepsis is less frequent in high-income 
countries, but the overall burden remains significant due to aging populations and rising 
rates of chronic comorbidities [13]. 
Mortality is directly linked to delays in appropriate management [1]. A critical point is patient 
detection. For early recognition and management, several clinical scores (such as NEWS, 
SOFA, or qSOFA) are used to identify patients with sepsis and at risk of mortality. The 
diagnosis of sepsis involves the detection of organ dysfunction and infection. The SOFA 
score (Sequential Organ Failure Assessment) was designed to detect organ failure and 
predict the risk of mortality associated with sepsis or septic shock [28]. This scale assesses 
the function of organ systems using objective parameters: respiratory, coagulation, hepatic, 
cardiovascular, renal, and central nervous systems. 
The consequences of sepsis extend beyond the acute event in survivors. Post-sepsis 
syndrome 
involves 
deficits 
in 
multiple 
systems: 
immune, 
cognitive, psychiatric, 
cardiovascular, and renal. Collectively, these adverse outcomes lead to rehospitalizations, 
poorer quality of life, and increased long-term mortality [29]. In a retrospective cohort study 
evaluating various 30-day outcomes in patients after discharge, surviving sepsis was 
associated with a tripled risk of subsequent infections (p=0.0006), rehospitalization for 
infection within the year following the initial hospitalization (p=0.0009), and post-discharge 
mortality (p=0.003) [30]. 
In high-income countries, the cost of care for sepsis patients is estimated between USD 
20,000 and USD 50,000 per patient episode [31,32]. In the United States, sepsis is among 
the most expensive conditions treated in hospitals, accounting for over USD 20 billion in 
annual healthcare costs [13]. Similarly high costs have been reported in Europe, with 
sepsis-related expenses ranging from €7,500 to €27,000 per patient [33]. In low- and 
middle-income countries, the direct costs of sepsis care are generally lower than in 
high-income countries but still represent a significant burden on healthcare systems and 
patients [21]. In Latin America, the best available estimate comes from a Brazilian study, 
which estimated that the average cost of sepsis treatment is approximately USD 10,000 per 
patient [34]. 
The Surviving Sepsis Campaign (SSC) is a global collaboration aiming to reduce sepsis 
mortality [35]. Since its first publication in 2004, the SSC and the Institute for Healthcare 
Improvement (IHI) have partnered to achieve a 25% reduction in sepsis mortality by 2009, 
producing high-quality, evidence-based clinical practice guidelines, with subsequent updates 
in 2016 and 2021 [1]. 
1.2.3. Antimicrobial resistance as a global and local issue 
In the past, antimicrobial resistance (AMR) was a phenomenon confined mainly to 
nosocomial infections. However, it has now become a public health problem at the 
community level as well. Several factors contribute to this situation, including the extensive 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
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and often indiscriminate use of antibiotics, the excessive use of broad-spectrum drugs, and 
the limited availability of narrow-spectrum antimicrobials [36]. Meanwhile, the incidence of 
multidrug-resistant organisms (MDROs) continues to rise, while the availability of effective 
antimicrobials becomes increasingly limited. Additionally, the development of resistance is 
occurring at an accelerating pace, narrowing the gap in the “arms race” between the 
emergence of new antimicrobials and the appearance of new resistance mechanisms [37]. 
MDROs also have a remarkable capacity for adaptation and can acquire and transmit 
resistance genes through mechanisms such as horizontal plasmid transfer, which amplifies 
their spread beyond the individual, impacting both the ecosystem and the broader 
community [38]. 
The One Health approach considers humans as part of an interdependent ecological 
system, constantly interacting with microorganisms, animals, plants, and the environment. 
Under this framework, the health of people, animals, plants, and ecosystems are closely 
interconnected. One of the main drivers of AMR is the excessive and inappropriate use of 
antimicrobials, not only in human healthcare but also in livestock, agriculture, and the food 
chain [39]. 
AMR has been called a “new pandemic,” and it is projected to become the leading cause of 
death worldwide by 2050, a process that has been accelerated by the COVID-19 pandemic 
[40]. Multidrug-resistant organisms have been recognized by the World Health Organization 
(WHO) as a critical threat to global health security [41]. In 2011 [42], consensus definitions 
were established to classify microorganisms based on their resistance profiles: 
multidrug-resistant (MDR, resistant to at least three tested antimicrobial groups), extensively 
drug-resistant (XDR, resistant to all but one or two groups), and pandrug-resistant (PDR, 
resistant to all available antibiotics for that bacterial family) [43]. 
Antimicrobial exposure is directly related to the development of AMR; therefore, efforts to 
reduce both the number of administered antibiotics and their spectrum of activity are crucial 
strategies in patients with sepsis and septic shock [44]. 
Excessive antibiotic prescribing is associated with multiple adverse consequences, including 
an increased risk of side effects, more frequent relapses, unnecessary medicalization of 
self-limiting diseases, and the emergence of resistance. This issue is particularly problematic 
in primary care, where most infections are viral. Approximately 90% of antibiotic 
prescriptions are written by general practitioners, particularly in emergency departments or 
outpatient clinics [2]. In patients with sepsis, following the Surviving Sepsis Campaign (SSC) 
guidelines has promoted a “treat first, ask questions later” culture, which tends to drive 
overprescribing [45]. 
Other relevant aspects include treatment duration (the “less is more” principle) [46] and the 
failure to de-escalate antibiotic therapy [47]. De-escalation, which consists of narrowing the 
spectrum based on microbiological findings, has been shown to be safe in septic patients, 
with additional benefits such as cost savings, reduced toxicity and side effects, and 
decreased emergence of multidrug resistance [48]. 
The pressure to initiate immediate treatment, prescribe unnecessary antibiotics, prolong 
therapy, or use excessively broad-spectrum agents can lead to overtreatment and related 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
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harms. At the individual level, antibiotics disrupt the microbiota, causing imbalances and 
promoting colonization by resistant organisms [49]. At the ecosystem level, they foster the 
emergence and transfer of bacteria and resistance genes among humans, animals, and the 
environment [50,51]. These harms include drug adverse reactions, acute kidney injury, 
Clostridioides difficile infections, antibiotic resistance, superinfections, and even potentially 
increased mortality [52]. 
In the context of sepsis, correctly identifying patients is crucial to avoid overprescription, and 
three levels of optimization of antimicrobial treatment can be distinguished: 
●​ Appropriate: This has a double meaning: on the one hand, that the treatment covers 
the presumed pathogen according to in vitro susceptibility; on the other, that it aligns 
with published guidelines and consensus regarding agent selection and duration [53]. 
It may also include adherence to local or institutional clinical practice guidelines. 
●​ Adequate: In addition to being appropriate, the treatment is considered adequate if 
pharmacokinetic and pharmacodynamic strategies are applied, such as shorter 
dosing intervals, consideration of physicochemical properties when targeting specific 
infection sites, therapeutic drug monitoring, and prevention of drug interactions 
[54,55]. 
●​ Optimal: Beyond being appropriate and adequate, optimal therapy involves 
administering a dose that achieves effective concentrations at the site of infection to 
ensure eradication. Examples include prolonged infusions of beta-lactam antibiotics 
[56,57] or early adjustment of doses according to renal function in septic patients 
[58,59]. 
1.3. Types of AMR 
Among the so-called “superbugs,” current concerns focus mainly on resistant Gram-negative 
bacilli (GNB), along with some problematic Gram-positive cocci (GPC), grouped under the 
acronym ESKAPE: vancomycin-resistant Enterococcus faecium (VRE), methicillin-resistant 
Staphylococcus aureus (MRSA), extended-spectrum beta-lactamase (ESBL)-producing and 
carbapenem-resistant Enterobacterales (CRE), Acinetobacter baumannii, Pseudomonas 
aeruginosa, and Enterobacter spp. 
Bacteria exhibit various AMR mechanisms, including efflux pumps, reduced permeability to 
antibiotics, enzymatic inactivation, target site protection or modification, and biofilm 
formation. Some enzymatic mechanisms are potentially transmissible and can be shared 
across bacterial species via plasmids, facilitating the spread of resistance to beta-lactam 
antibiotics, which are the most commonly used antimicrobial group in clinical practice. This 
group includes penicillins, cephalosporins, carbapenems, and monobactams. Among GNB, 
ESBL- and CRE-producing strains are of particular concern due to their increasing 
prevalence [60], limited treatment options, the faster emergence of resistance compared to 
the development of new antimicrobials, healthcare costs, length of hospital stays [61], and 
associated mortality [62]. The WHO has included these pathogens in its 2024 update of 
critical priority pathogens  [63]. 
According to the Ambler classification, beta-lactamases, including carbapenemases, are 
divided into four classes [64]. 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
Class A 
Enzymes 
with 
predominant 
activity 
against 
penicillins. 
The 
main 
carbapenemase-producing strains (CPE) in this group are Klebsiella pneumoniae 
carbapenemase (KPC) producers [65].  
Class B 
Metalloenzymes 
with 
predominant 
activity 
against 
cephalosporins. 
Metallo-β-lactamases (MBLs) confer resistance to penicillins, cephalosporins, and 
carbapenems but retain susceptibility to aztreonam (a monobactam). There are three 
main types: NDM [66], VIM [67] and IMP [68]. 
Class C 
Inducible chromosomal cephalosporinases (CCI) of Gram-negative bacteria, also 
known as AmpC β-lactamases. Some Enterobacterales species have genes encoding 
these enzymes, which confer resistance to penicillins and cephalosporins (except 
cefepime and aztreonam). High-risk species include Enterobacter cloacae complex, 
Citrobacter freundii, and Klebsiella aerogenes; lower-risk species include Serratia 
marcescens, Morganella morganii, and Providencia stuartii [69]. 
Class D 
Enzymes that include oxacillinase-type (OXA-type) carbapenemases, such as OXA-11, 
OXA-13, OXA-15, and OXA-18 [65]. 
 
Some types of carbapenemase-producing Enterobacterales (CPE) are endemic in certain 
regions of the world. For instance, KPC is prevalent in the United States, Mexico, Portugal, 
Italy, Israel, and Greece [70], while MBLs are more common in Southeast Asia, the 
Mediterranean, Romania, Denmark, Poland, and the Pacific region of Asia, including 
Australia, China, Japan, and Turkey [71]. Latin America and the Caribbean experienced a 
significant increase in CPE during the COVID-19 pandemic [72], with Argentina currently 
presenting the highest rate of carbapenem resistance [73]. 
 
The RECAPT-AR study, conducted in Argentina in the post-COVID-19 era, is a prospective, 
multicenter study aiming to evaluate the prevalence of carbapenemase-producing 
Enterobacterales in infectious processes during the post-pandemic period. Among 821 CPE 
isolates, Klebsiella pneumoniae was the most frequently recovered species, accounting for 
76% of the isolates. MBLs (42.0%) and KPC (39.8%) together represented 81.8% of 
carbapenemases; dual producers were found in 7.8% of isolates [74]. 
 
Another relevant pathogen is methicillin-resistant Staphylococcus aureus (MRSA), which is 
highly prevalent in skin and soft tissue infections, surgical site infections, and 
catheter-associated bloodstream infections. In skin and soft tissue infections from three 
tertiary care centers in Buenos Aires City, resistance rates among isolates ranged from 64% 
to 77% [75–77]. Additionally, the Whonet network for antimicrobial resistance surveillance 
reports that MRSA accounts for over 30% of isolates from blood cultures and nearly 45% 
from skin and soft tissue samples [78]. 
 
1.4. Factors Associated with Antimicrobial Resistance (AMR) 
There is no single validated predictive model for multidrug resistance in patients with sepsis. 
However, several risk factors have been identified. Predicting appropriate treatment for a 
given scenario should include [79]: 
●​ Physiological and pathophysiological characteristics of the patient. 
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Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
●​ The causative bacterium and its resistance profile. 
●​ Site of infection. 
●​ The antibiotic and its pharmacological properties. 
 
The main individual risk factors for AMR are: prior antibiotic exposure within the last 90 days, 
hospitalization within the past 3 months, current hospitalization lasting more than 5 days, 
presence of invasive devices, and prior colonization or infection by multidrug-resistant 
organisms (MDROs) [80]. Local epidemiology with high resistance rates to multiple 
antibiotics is an important risk factor that must be considered [81].  
 
The severity of illness as a risk factor for multidrug resistance is controversial. However, the 
presence of septic shock is considered a risk factor for MDROs in hospital-acquired 
pneumonia according to both European and American guidelines [82]. In contrast, in 
critically ill patients with intra-abdominal infections, no differences were found in the 
incidence of MDROs between those with or without sepsis or septic shock [83]. 
 
The risk score by Tumbarello et al. [84], published in 2011, was developed to estimate the 
risk of infection with ESBL-producing organisms in patients presenting with urinary tract 
infections. Higher risk was associated with age ≥70 years, recent hospitalization, use of a 
urinary catheter, comorbidities (Charlson score ≥4), and prior antibiotic use—especially 
fluoroquinolones. 
 
Most nosocomial infections are endogenous, originating from the mucosal microbiota. These 
endogenous infections result from the translocation of predominant aerobic microorganisms 
in the intestinal mucosa [85]. Patients colonized with CPE are at higher risk of developing 
infections caused by MDROs and have higher infection rates compared to the general 
population. In Ontario, Canada, among a cohort of 69,998 individuals with a positive CPE 
surveillance test, 20.3% developed an infection within a median of 10 days [86]. In another 
study of 6,828 CPE carriers, the cumulative incidence of CPE bacteremia was 2.4%. Certain 
species, like Klebsiella pneumoniae, carry a higher risk of infection than others like 
Escherichia coli. Among resistance mechanisms, MBLs seemed to carry a lower risk than 
KPCs, although the difference was not statistically significant [87]. The highest risk of 
infection occurs within 10 to 14 days after initial CPE colonization detection [88]. 
 
In colonized patients, the study by Gianella et al. found that ICU admission, history of 
invasive abdominal procedures, chemotherapy/radiotherapy, and the number of additional 
colonization sites were independent risk factors for developing CPE bacteremia [89]. 
 
In patients with sepsis or septic shock, the decision to include an anti-MRSA agent in the 
empirical treatment regimen depends on a risk assessment based on patient characteristics 
and the infection site [1]. Risk factors for MRSA in community-associated infections include 
recent 
hospitalization, 
long-term 
care 
facility residence, recent surgery, chronic 
hemodialysis, prior infection or colonization, and prior quinolone use [90,91]. In skin and soft 
tissue infections, due to high local prevalence, empirical coverage is always recommended 
in purulent infections [92]. 
 
In a cohort study of 17,430 adults with community-onset sepsis and positive cultures 
admitted to 104 U.S. hospitals, 67.0% received broad-spectrum empirical antibiotics. 
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However, Gram-positive cocci (GPC) were isolated in only 13.6% and Gram-negative bacilli 
(GNB) in 13.2%. Both undertreatment (uncovered pathogen) and overtreatment (resistant 
organisms identified but not isolated) were associated with increased mortality after detailed 
risk adjustment [52]. 
 
In patients diagnosed with infection and suspected sepsis, initiating appropriate empirical 
antibiotics within one hour of recognition is one of the key measures to reduce mortality. For 
every hour of delay within the first 24 hours, the risk of death increases by 4% [93]. The 
Surviving Sepsis Campaign guidelines emphasize the immediate administration of 
broad-spectrum empirical antibiotics upon suspicion of sepsis [1]. 
 
To date, there is no universally validated methodology to predict AMR in patients with sepsis, 
which remains one of the greatest challenges. Empirical treatment is generally prescribed 
within the first 48 to 72 hours after sample collection. In terms of patient care, validated 
prediction of AMR could improve the adequacy of empirical treatment by reducing the risk of 
using an antibiotic to which the causative microorganism is resistant [94]. These predictions 
could also have local and global benefits by reducing broad-spectrum antibiotic prescribing 
and decreasing the risk of MDRO transmission [95]. 
 
The challenges of antibiotic treatment in sepsis include proper stewardship programs based 
on infection-related knowledge in critically ill patients and the context of multidrug-resistant 
organisms [96]—especially Gram-negative bacteria—focused on carbapenem-sparing 
strategies and the rational empirical use of new antibiotics [5]. Predicting the occurrence of 
MDRO infections requires consideration of individual patient characteristics, the infectious 
focus, the community to which the patient belongs, and the likely bacteria along with their 
natural and acquired resistance mechanisms. 
1.5. Relevance and Purpose 
Sepsis remains a daily clinical challenge, especially in emergency departments, due to the 
urgent need for early detection, timely antibiotic treatment, and high mortality, especially if 
treatment fails. In countries with high prevalence of antimicrobial resistance (AMR), choosing 
the right antibiotic treatment can be especially difficult due to the possibility of AMR on one 
hand, and the duty to rationally use antimicrobials to avoid the emergence of 
multidrug-resistant organisms (MDRO) on the other. In this context, a predictive model for 
AMR that anticipates this probability and makes better use of antibiotics could be a highly 
useful tool for improving patient care and combating AMR. 
Challenges in antibiotic treatment in sepsis include proper management programs based on 
knowledge related to infections in critically ill patients and in the context of 
multidrug-resistant pathogens [96], particularly Gram-negative bacteria, focusing on 
carbapenem-sparing and the empirical and rational use of new antibiotics [5]. The challenge 
is to predict the occurrence of MDRO infections based on the patient's individual 
characteristics, the infectious focus, the community they belong to, and the likely bacteria 
and their natural and acquired resistance mechanisms. 
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This project directly aligns with two of the Sustainable Development Goals (SDGs) proposed 
by the United Nations [97]: SDG 3 ("Good Health and Well-being"), SDG 9 ("Industry, 
Innovation, and Infrastructure"), and SDG 10 ("Reduced Inequality"). 
SDG 3: Ensure healthy lives and promote well-being for all at all ages [98]. This project 
contributes directly to SDG 3 by addressing one of the most urgent challenges in global 
health: AMR, especially in patients with sepsis, a critical condition with high mortality. By 
developing a predictive model to identify individuals at high risk of infections from 
multidrug-resistant pathogens, the goal is to optimize antibiotic use in emergency services, 
improve clinical outcomes, reduce mortality, and promote more rational use of healthcare 
resources. In this sense, the development of a predictive tool that enables more rational and 
targeted prescribing is in line with SDG 3 goals, contributing to access to safe, effective, and 
quality treatments and reducing avoidable mortality. 
SDG 9: Build resilient infrastructure, promote inclusive and sustainable industrialization, and 
foster innovation [99]. The project promotes innovation in the field of medicine and public 
health by applying predictive models and data analysis tools in real clinical contexts. This 
integration 
of 
data 
science, 
epidemiology, 
and 
artificial 
intelligence 
in 
clinical 
decision-making represents a commitment to smarter, more efficient healthcare systems 
capable of adapting to emerging challenges. 
SDG 10: Reduce inequality [100]. The burden of disease associated with AMR 
disproportionately affects low- and middle-income countries [40], where limitations in 
diagnostic infrastructure, access to new therapies, and infection control resources 
exacerbate existing health inequalities. Furthermore, the mortality associated with sepsis 
disproportionately affects lower-income regions within the same country [101] and across 
countries [13]. In this context, a tool that can be applied in high-demand settings such as 
emergency departments, and that favors the rational use of antimicrobials even in 
resource-limited scenarios, could help reduce these inequities. Therefore, this project also 
aligns with SDG 10 by proposing a potentially replicable strategy that promotes equity in 
addressing sepsis and AMR locally and globally. 
2. Question and Objectives 
2.1. Questions 
In adult patients who present to an emergency department in a tertiary care center with 
sepsis or septic shock: 
●​ What is the prevalence of AMR/resistance patterns with clinical significance?​
 
●​ What are the predictive factors associated with AMR in this population?​
 
●​ Is it possible to generate and validate clinically useful predictive models to predict the 
probability of AMR?​
 
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2.2. Objectives 
In adult patients who present to an emergency department in a tertiary care center with 
sepsis or septic shock: 
2.2.1. Prevalence and Associated Factors 
●​ Estimate the prevalence of AMR/resistance patterns with clinical significance.​
 
●​ Describe the predictive factors associated with AMR in this population.​
 
●​ Generally, and in clinically relevant subgroups: by probable focus, clinically relevant 
pathogens, severity.​
 
2.2.2. Generation and Validation of Predictive Models 
●​ Generate and validate clinically useful predictive models to predict the probability of 
AMR.​
 
●​ Generate and validate clinically useful predictive models to predict the probability of 
common/relevant pathogens.​
 
●​ 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.​
 
●​ Describe and evaluate point statistics on deterministic and unpredictable individuals 
based on the best predictive models. 
 
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3. Methods 
3.1. Design 
A cross-sectional study will be conducted on patients presenting with sepsis or septic shock 
at the emergency department of the Hospital Italiano de Buenos Aires (HIBA) in both its 
central and San Justo "Agustín Rocca" campuses who are subsequently admitted. All 
individuals with sepsis will be included at the time of their entry to the adult emergency 
department. 
Since the goal is to develop stepped predictive models capable of validly predicting culture 
positivity, pathogen identification, and antimicrobial resistance (AMR), all potentially 
predictive variables will be measured at the time of study inclusion (time zero).​
 The events of interest in the study will also be measured at time zero. Although culture 
results might seem like an event that occurs over time, the measurement occurs at time zero 
upon inclusion in the study. The delay in results is simply due to technical reasons and does 
not constitute patient follow-up. 
The study period will span 70 months, from 01/01/2017 to 20/03/2020 and from 01/05/2022 
to 10/08/2025. During the COVID-19 pandemic, emergency services were restructured, with 
an increase in telemedicine as a protective strategy for both staff and patients, avoiding 
crowding in waiting rooms. The total number of consultations decreased, and the proportion 
of low-complexity consultations declined, while high-complexity consultations increased 
[102]. The Hospital's infection control committee action plan and recommendations restricted 
usual practices for the initial management of sepsis patients, such as the decision to perform 
invasive procedures for respiratory sample collection, like bronchoalveolar lavage [103]. 
Some specialties, such as pulmonology and otorhinolaryngology, also restricted their 
practices for safety reasons [104]. 
3.2. Setting 
HIBA is an integrated care network comprising two hospitals: the central hospital and San 
Justo (HISJ). It is a high-complexity university hospital in Buenos Aires, with 789 beds, 246 
of which are for critical care; approximately 2.5 million outpatient consultations annually, 
41,000 discharges, and about 45,000 surgical procedures performed in 57 operating rooms 
[105]. Additionally, HIBA offers a prepaid health plan with about 184,000 members. Care 
across all areas is centralized in a unified Electronic Health Record (EHR). This data 
repository contains both administrative information about patients (such as studies, 
appointment requests, outpatient visits, hospitalizations, and drug consumption) and all 
clinical information (health problems, clinical diagnoses, medical evolutions, study results, 
etc.). All health issues or comorbidities are automatically coded through a terminology server 
with a local thesaurus that maps and encodes the information using the controlled 
vocabulary SNOMED [106]. 
At the Adult Emergency Department (CEA) of HIBA's central hospital, approximately 500 
consultations are handled daily. It has 23 patient care stations for patients arriving by their 
own means and 30 observation/inpatient beds covering the full spectrum of complexity and 
specialties. Around 6% of patients are admitted. Given the closed prepaid system, 80% of 
ambulances arriving at the CEA are not referred to another emergency department outside 
the HIBA care network. 
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Patients with infections presenting to the CEA are initially triaged by trained nurses, who 
assess severity by dividing patients into four sectors: A for critical patients, B for moderate 
complexity, C for low complexity, and D for spontaneous demand. Vital signs are recorded in 
the EHR when potential severe cases are detected. Patients arriving by ambulance bypass 
this step and are directly assigned to sector B. Once in sector B, patients undergo further 
evaluation by doctors and nurses to determine the definitive site for initial assessment (A or 
B depending on sepsis or septic shock), and blood samples for laboratory tests, blood 
cultures, and urine cultures (if prescribed) are collected. The first dose of antibiotics is 
administered within the first hour [107,108]. Other cultures may require further evaluation 
and are often deferred during the first 12 to 24 hours, as are other complementary studies 
such as imaging. There is a clinical practice guideline for sepsis management for emergency 
doctors [109] and one from the adult infectious diseases section of the medical clinic service  
[110], both based on SSC guidelines [1]. 
3.3. Population 
The study population will include all individuals aged 18 years or older who present to the 
CEA at HIBA. 
 
3.3.1. Inclusion Criteria 
 
●​ Adults aged 18 years or older. 
●​ Presenting to the emergency department at the Hospital 
Italiano 
de 
Buenos 
Aires 
between 
01/01/2017 
and 
20/03/2020, and from 01/05/2022 to 10/08/2025. The period 
affected by the COVID-19 pandemic, starting on 20/03/2020 
with mandatory confinement, will be excluded  [111]. Given 
changes in care practices, such as the exclusion of 
respiratory sample collection to avoid exposure to infections, 
the study will also exclude the three waves of COVID-19 in 
Argentina. 
●​ Presenting with sepsis or septic shock (see section 5.3.3 for 
definitions) and requiring at least 48 hours of observation or 
admission. 
●​ Bacterial cultures taken during the initial evaluation. 
3.3.2. Exclusion 
Criteria 
●​ Patients without an indication for antibiotics within the first 48 
hours of hospital admission. 
●​ Patients without bacterial cultures performed within the first 
48 hours of hospital admission. 
●​ Patients diagnosed with SARS-CoV-2 infection within the first 
72 hours of hospital admission. 
3.3.3. Definitions 
Sepsis​
 Individuals meeting any of the following two criteria within the first 24 hours of hospital 
admission: 
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●​ Sepsis or septic shock and its subset, as the reason for consultation, discharge from 
the emergency department, or reason for hospitalization. 
●​ SOFA score (Sepsis-related Organ Failure Assessment) upon admission to the Adult 
Intensive Care Unit or Adult Intermediate Care Unit with a score of 2 or higher. 
●​ Vital sign recordings within 24 hours of hospital admission with a SOFA score of 2 or 
more. SOFA Scale: A scale that assesses organ dysfunction. In patients with 
infection, a SOFA score ≥ 2 points (or a 2-point increase from baseline in patients 
with chronic organ dysfunction) is diagnostic of sepsis. 
SOFA (Sepsis related Organ Failure Assessment) 
Criteria 
0 
1 
2 
3 
4 
CNS 
Glasgow Coma Scale 
15 
13-14 
10-12 
6-9 
<5 
Renal 
Creatinine (mg/dl) 
Urine output (ml/day) 
<1,2 
1,2-1,9 
2-3,4 
3,5-4,9 o  
<500 ml 
>5 o 
<200 ml 
Hepatic 
Bilirubin (mg/dl) 
<1,2 
1,2-1,9 
2-5,9 
6-11,9 
>12 
Coagulation 
Plaquetas 103/mm3 
≥150 
<150 
<100 
<50 
<20 
Respiratory 
PaO2/FiO2 (mmHg) 
 
≥400 
 
<400 
 
<300 
<200 ventilatory 
support 
<100  ventilatory 
support 
Cardiovascular 
MAP(mmHg) 
Vasoactive 
drugs 
(μg/kg/min) 
≥70 
<70 
Dopamina <5 o 
dobutamina at 
any dose 
Dopamina 5-15 
Noradrenalina o 
adrenalina ≤0,1 
Dopamina >15, 
noradrenalina o 
adrenalina >0,1 
Abreviaturas: CNS Central Nervous System, PaO2 Arterial oxygen pressure, FiO2 Fraction of inspired 
oxygen, MAP Mean arterial pressure. 
 
Septic shock 
Individuals who, within 6 hours of hospital admission, require vasopressors to maintain a 
mean arterial pressure of 65 mmHg or higher and have a serum lactate level greater than 2 
mmol/L (>18 mg/dL) in the absence of hypovolemia [8].  
 
3.4. Data collection 
A coded and de-identified database will be requested through a help desk from the 
Research Department at HIBA. 
 
3.5. Variables 
3.5.1. Outcome variables 
 
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1. Culture positivity 
Variable dicotómica. Se considerará positivo o negativo. 
2. Bacterial species 
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. 
3. 
Multidrug-resistant 
microorganism [43] 
 
Categorical variable; the following will be considered: 
Multidrug-resistant (MDR): resistant to at least one antibiotic in 
three of the antibiotic groups tested for that family, as described 
below. 
Extensively drug-resistant (XDR): non-susceptible to at least one 
agent in all but two or fewer antimicrobial categories. 
Pandrug-resistant (PDR): resistant to all antibiotics available for 
that family. 
For enterobacterales 
 
The yellow exclamation mark corresponds to species carrying the AmpC gene, which can 
become deregulated and produce inducible chromosomal cephalosporinases (ICC), with 
yellow indicating low-risk species and orange indicating high-risk species [69]. 
For 
non-fermenters 
(Pseudomonas 
aeruginosa, 
Acinetobacter 
baumannii, 
and 
Stenotrophomonas maltophilia). 
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For Staphylococcus aureus, the antibiotic groups are: Aminoglycosides (gentamicin), 
Ansamycins (rifampin), Anti-staphylococcal beta-lactams (methicillin / cefazolin), 
Anti-MRSA cephalosporins (ceftaroline), Fluoroquinolones, Folate pathway inhibitors 
(trimethoprim-sulfamethoxazole, TMP-SMX), Lincosamides (clindamycin), Macrolides 
(erythromycin), Glycopeptides (vancomycin / teicoplanin), Tetracyclines, Glycylcyclines 
(tigecycline), Lipopeptides (daptomycin), Oxazolidinones (linezolid). 
For Enterococcus, the antibiotic groups are: Aminoglycosides (gentamicin), Streptomycin, 
Carbapenems (imipenem or meropenem) or Penicillins (ampicillin), to which Enterococcus 
faecium is resistant in over 90% of cases, Fluoroquinolones, Glycopeptides (vancomycin / 
teicoplanin), Tetracyclines (minocycline / doxycycline), Glycylcyclines (tigecycline), 
Lipopeptides (daptomycin), Oxazolidinones (linezolid). 
3.1. Enzymatic 
resistance 
mechanisms 
Categorical variable, the following will be considered: No 
enzymatic mechanism; MRSA; VRE; KPC; MBL; OXA. 
 
3.2. Carbapenemase 
genotypes 
Categorical variable; the following will be considered: Types of 
carbapenemases if studied (for example, KPC, NDM, VIM, IMP, 
OXA-48 like). 
 
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3.5.2. Explanatory variables - Potential predictors of resistance 
Domain 
Variables 
1. Patient 
characteristics 
Age, Sex, and Place of origin according to the 24 provinces 
[112,113]. 
Place of origin (Community / Tertiary level center / Acute 
hospitalization (<30 days) / Prolonged hospitalization (31 or 
more days) / Chronic hospitalization (>90 days). 
2. Invasive devices 
Dichotomous variables for the presence or absence of each of 
the following: bladder catheter, central venous catheter, 
tracheostomy, nasogastric tube. This will be recorded 
according to the first nursing assessment upon admission to 
the CEA. 
3. Immunosuppression Any of the following will be considered severe [114]: 
●​ Active leukemia or lymphoma. 
●​ Disseminated malignancy. 
●​ Aplastic anemia. 
●​ Graft-versus-host disease (GVHD). 
●​ Congenital immunodeficiencies (e.g., common variable 
immunodeficiency). 
●​ Recent treatment with checkpoint inhibitors or radiation 
therapy. 
●​ Solid organ transplant with immunosuppressive treatment. 
●​ Immunosuppressive 
drugs 
related 
to 
transplant: 
cyclosporine, 
tacrolimus, 
sirolimus, 
everolimus, 
azathioprine, mycophenolate mofetil. 
●​ Hematopoietic stem cell transplant (HSCT) within two 
years or ongoing immunosuppressive therapy. 
●​ High-dose systemic corticosteroids (20 mg or less of 
prednisone per day or its equivalent) for at least two 
weeks. 
●​ Alkylating agents (cyclophosphamide) or antimetabolites 
(methotrexate 
>0.4 
mg/kg/week, 
azathioprine 
≥3 
mg/kg/day, or 6-mercaptopurine ≥1.5 mg/kg/day). 
●​ Chemotherapy for cancer. 
●​ Tumor 
necrosis 
factor 
alpha 
blockers 
(etanercept, 
adalimumab, certolizumab, golimumab, infliximab). 
●​ Biologics, 
lymphocyte-depleting 
agents 
such 
as 
thymoglobulin or alemtuzumab. 
●​ B-cell depleting agents such as rituximab. 
●​ HIV patients with a CD4 count <200 cells/mm³, AIDS 
diagnosis, or symptomatic HIV. 
Mild Immunosuppression [115]. 
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●​ Asymptomatic HIV infection with a CD4 count between 200 
and 499 cells/mm³. 
●​ Low-dose systemic corticosteroids (19 mg or less of 
prednisone per day or its equivalent) for at least two 
weeks. 
●​ Methotrexate ≤0.4 mg/kg/week. 
●​ Azathioprine ≤3 mg/kg/day or 6-mercaptopurine ≤1.5 
mg/kg/day. 
 
Neutropenia. Cytopenias [116]: Leukopenia: white blood cell 
count below 5000/mL. Absolute neutrophil count (ANC): 
●​ Neutropenia: ANC <1500/mL 
●​ Mild: 1000-1500/mL 
●​ Moderate: 500-1000/mL 
●​ Severe: <500/mL 
●​ Agranulocytosis: <200/mL 
4. Comorbidities 
Nominal dichotomous categorical variable for the presence or 
absence of the following: 
Chronic renal failure, Hemodialysis, Arrhythmia, Cardiac 
disease, 
Inflammatory 
Bowel 
Disease, 
Diabetes, 
Cerebrovascular disease, Psychiatric conditions, Mild hepatic 
disease, Obesity, Previous infection, Moderate lung disease, 
Rheumatologic disease, Peptic ulcer, Previous tumor, Valvular 
heart disease, Severe pulmonary disease, Severe hepatic 
disease. 
5. Therapeutic 
adequacy 
 
 
Dichotomous variable for the presence or absence of nursing 
NEWS assessment or medical indication of: "Do not 
resuscitate order" or "Do not follow NEWS protocol." 
Follow-up by palliative care: evolution prior to the event by 
palliative care. 
6. Medical history 
Previous hospitalization, Duration of previous hospitalization. 
Time since discharge. 
Use of antibiotics during previous hospitalization. 
Stay 
in 
Intensive 
Care 
Unit 
(ICU) 
during 
previous 
hospitalization. 
Previous colonization by multiresistant germs. 
Previous infection by resistant germs and time since that event. 
Invasive procedure in the previous 90 days: VEDA, CVC, 
ERCP, Cystoscopy, Bronchoscopy, Other invasive procedures. 
Surgery in the previous 90 days. 
Hospitalization <48 hours and >48 hours. 
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Extended antibiotic prophylaxis. 
Site of hospitalization. 
Center of hospitalization. 
Place of hospitalization. 
ICU admission in the previous year and time since ICU 
discharge. 
Use of medications: chemotherapy, intravenous infusions. 
Transfusions. 
7. Antibiotic use 
Use of antibiotics: Outpatient antibiotics (prescription or 
dispensing), antibiotics in emergency consultation, home 
hospitalization antibiotics. 
8. Clinical status 
Numerical value and each category of SOFA. 
Lactate level. 
Infectious focus. 
Sepsis-induced hypoperfusion: defined by any of the following 
criteria in the context of sepsis: 
●​ Systolic blood pressure (SBP) < 90 mmHg or Mean 
Arterial Pressure (MAP) < 65 mmHg. 
●​ Lactate > 4 mmol/L. 
●​ Urine output < 0.5 ml/kg/h. 
PaO2/FiO2 ratio: an indirect marker of lung injury. It measures 
the ratio of arterial oxygen pressure to the concentration of 
oxygen in inspired air. It is used to categorize Acute 
Respiratory Distress Syndrome (ARDS) into: 
●​ Mild: PaO2/FiO2 ≤ 300 and > 200 mmHg. 
●​ Moderate: PaO2/FiO2 ≤ 200 and > 100 mmHg. 
●​ Severe: PaO2/FiO2 ≤ 100 mmHg. 
9. Diagnostic studies 
Cultures 
(Blood 
cultures, 
Urine culture, Stool culture, 
Cerebrospinal fluid culture, Sputum, Bronchoalveolar lavage, 
Tracheal aspirate, Abscess culture). 
Complementary: Surveillance swab, Nasal swab SAMR, Viral 
panel, FilmArray. 
Imaging: Chest X-ray, Abdominal X-ray, CT scan, Ultrasound, 
MRI. 
 
 
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4. Statistical Considerations 
4.1. Sampling and Sample Size Calculation 
A consecutive random sampling will be carried out of all individuals assisted in the adult 
emergency department at HIBA during the period from 01/01/2017 to 20/03/2020 and from 
01/05/2022 to 10/08/2025. That is, the entire 70-month period of the study will be included. 
The sample size is fixed, including all individuals with sepsis who consult the emergency 
department during the study period. Given that temporal trends could potentially change 
both the incidence of sepsis and the occurrence and frequency of multidrug resistance, it 
was decided to divide the generation and validation samples into 2/3 and 1/3, respectively. 
No additional sample size calculation was performed for internal validation. 
4.2. Statistical Analysis 
4.2.1. Missing Data 
Missing data will be considered as missing at random (MAR), and therefore, the general 
strategy will be to use complete case analysis unless specified otherwise. 
Since secondary databases will be used, there will be no missing data for categories where 
only positive results are recorded. For example, "diabetes" is a variable that only records 
positive results in databases derived from healthcare systems, as "non-diabetics" are not 
recorded. In this way, and as is customary, "non-diabetics" will be considered as those not 
registered with the "diabetes" attribute. In this case, no missing data will exist. 
A special case that requires separate mention is negative cultures. By definition, a negative 
culture represents a missing data point, as the germ or potential antimicrobial resistance is 
unknown. Although predictive models will include individuals with negative cultures, 
validation will be repeated in the subset of positive cultures. Multiple imputation strategies for 
negative cultures could be explored. 
4.2.2. Generalities 
Although individuals may have more than one episode of sepsis during the study period, 
considering the intention to generate predictive models for each new episode, each episode 
will be included as an independent unit of analysis for descriptive analysis, exploration of 
associated factors, and the generation and validation of predictive models as described 
below. 
Statistical significance will be considered for p-values less than 0.05 unless otherwise 
specified. In line with current understanding of hypothesis testing, emphasis will be placed 
on presenting association measures with their confidence intervals rather than p-values. 
Statistical analysis will be performed using STATA version 16 MP - Parallel Edition 
(Copyright 1985-2017 StataCorp LLC - StataCorp, 4905 Lakeway Drive, College Station, 
Texas 77845 USA). 
4.2.3. Descriptive Analysis 
Continuous variables will be presented as mean and standard deviation or median and 
interquartile range (IQR), depending on the observed distribution. Categorical variables will 
be presented as absolute and relative frequencies. 
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4.2.4. Frequency Calculation 
For the calculation of frequencies, the positive culture will be used as the numerator, along 
with each selected relevant bacteria, MOR, MDR, XDR, PDR, resistance mechanisms 
(non-enzymatic mechanisms, SAMR, EVR, KPC, MBL, OXA) for each case, with the total 
number of individuals with sepsis (including negative cultures) as the denominator. This will 
be repeated using positive cultures as the denominator. Assuming sufficiently large 
proportions, 95% confidence intervals (CI95%) will be presented, estimated with the normal 
approximation. 
4.2.5. Associated Factors 
This analysis will include only individuals in the generation sample. Logistic regression 
models will be used to explore associated factors for each potential predictor. The crude 
odds ratios (OR) with their CI95% will be presented. 
4.2.6. Predictive Models 
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, MOR, MDR, XDR, PDR will be used as outcome variables. 
4.2.7. Generation of Predictive Models 
A multivariate logistic regression model will be used with a stepwise backwards algorithm, 
including potential predictors and interaction terms considered relevant. The different models 
will be compared using the Akaike Information Criterion (AIC), selecting the model with the 
lowest AIC. Since STATA does not consider AIC in its stepwise algorithms, a p-value of 
0.157 will be used as a proxy [117]. 
The assumptions of the model, such as logit linearity and the absence of multicollinearity, will 
be evaluated. The presence of influential or high-leverage data will also be assessed. 
All predictors will be presented with OR, CI95%, coefficients, and standard errors. Both the 
complete predictor model and an adjusted model considering shrinkage will be presented. 
Discrimination measures (area under the ROC curve of the model) and calibration measures 
(calibration in the large, slope of calibration, calibration plots) for the generation sample will 
also be presented. 
4.2.8. Validation of Predictive Models 
Using the validation sample, measures of discrimination (area under the ROC curve of the 
model) and calibration (calibration in the large, slope of calibration, calibration plots) will be 
presented. 
The clinical utility of the model will be evaluated in the validation sample, estimating 
sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood 
ratio, and negative likelihood ratio for selected events. 
 
4.2.9. Staged Models 
Considering the structure between positive culture, germ, and multidrug resistance, a 
predictive model of multidrug resistance will be built, considering this information and 
structure. Initially, a predictive model of positive culture will be constructed. The second step 
will involve constructing models of specific relevant germs, considering the predicted value 
for positive culture as an explanatory variable. The third step will be to generate predictive 
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models for MOR, considering as explanatory variables the predicted probabilities for relevant 
germs. All potential predictors could participate in the construction of predictive models at 
each stage. 
 
4.2.10. Visualization of Predicted Probabilities 
All objectives of this study aim to generate useful tools for clinical decision-making. 
Therefore, we will develop a visual interface to represent the probabilities of relevant germs 
and potential multidrug resistance. Structures similar to microarrays could be used with 
heatmaps, where colors represent probabilities that are easily identifiable by simple visual 
inspection. 
 
4.2.11. Exploration of Predictable and Unpredictable 
A predictive model can be considered a diagnostic test for classifying observations or 
individuals [118]. Each predictive model generated with different algorithms can be 
considered a distinct diagnostic test and compared to the known observed results, similar to 
the standard approach for exploring the validity of predictive models. In this comparison, 
observations can be correctly classified in two ways: the model predicts that the individual 
will achieve the outcome, and it does so (True Positive - TP), or the model predicts that the 
individual will not achieve the outcome, and it does not (True Negative - TN). Conversely, the 
predictive model can incorrectly classify individuals in two ways: the model predicts that the 
individual will achieve the outcome, but does not (False Positive - FP), or the model predicts 
that the individual will not achieve the outcome, but does (False Negative - FN). These four 
classification cells depend on the cutoff values in the continuous output of the predictive 
model, which can take a range of values. Thus, those that the model correctly predicts, true 
positives and negatives, can be called deterministic; while false positives and negatives 
might be called unpredictable. 
Using cutoff points that leave 10% of individuals above and 10% below, it is possible to 
define at one extreme TP and FP, and at the other extreme TP and FN. By comparing the 
characteristics between TP and FP on one hand, and TN and FN on the other, we aim to 
expose the differences between these subgroups of individuals in order to better understand 
the errors of the model in correctly identifying individuals; to describe and evaluate point 
statistics about deterministic and unpredictable individuals according to the best predictive 
models. 
Predictors, multidimensional distances, heterogeneity, information, point estimates (such as 
influence and leverage) in the outcome (subtypes of events or severity), and many other 
statistics and metrics will be compared. 
 
 
5. Ethical Considerations 
This is a retrospective observational study, which does not involve any procedures or 
activities beyond standard care during patient evaluation and treatment. Therefore, it poses 
no risk to patient health and entails no additional costs for patients or their healthcare 
coverage. Patients will only be evaluated retrospectively through their electronic health 
records (EHR). 
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Given that this study involves only minimal risk due to the handling of participant data, we 
request a waiver of informed consent, based on CIOMS 2019 Guideline 10: Modifications 
and Waivers of Informed Consent, on the grounds that: 
A.​ it would not be feasible or practical to conduct the research without such a waiver; 
B.​ the research has significant social value; and 
C.​ the research entails only minimal risk to participants. 
Regarding point A, this is a retrospective observational study involving hospitalized 
individuals who have already signed an informed consent upon admission, which includes 
authorization for the use of clinical data for academic and scientific purposes, while ensuring 
confidentiality. Due to the retrospective nature of this study, it would not be feasible to obtain 
prospective informed consent from participants. 
Regarding point B, this research has social value as it will provide insight into the 
epidemiology of resistant microorganisms (ROs) in sepsis and contribute to the development 
of a tool to optimize antimicrobial use—particularly relevant in a country with a high 
incidence of carbapenemase-producing Enterobacterales (CPE), and in the broader context 
of low- and middle-income countries. 
Finally, regarding point C, the study involves only minimal risk to patients, as it is 
retrospective and observational. The primary risk relates to patient privacy; therefore, all 
data will be processed with maximum confidentiality and codified, with access restricted 
solely to authorized study personnel. Data management will comply with national 
regulations, including Argentina's Personal Data Protection Law No. 25.326/00 (Habeas 
Data Law) and Law No. 26.529/09 on patient rights. 
We remain fully available for any audits, monitoring, or reviews of processes, 
documentation, or datasets (intermediate or final versions) that the Ethics Committee may 
deem necessary for the study. 
 
 
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6. Funding 
 
The project will be fully funded by the Internal Medicine Service and its Adult Infectious 
Diseases section of the Italian Hospital of Buenos Aires.
 
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7. Schedule 
Meses 
Actividades 
02-03/2025 
03/2025 
04-08/2025 
09-12/2025 
01-04/2026 
05-07/2026 
Literature 
review 
 
 
 
 
 
 
Definition of 
question and 
hypothesis 
 
 
 
 
 
 
Writing of the 
protocol 
 
 
 
 
 
 
Presentation 
Ethics 
Committee 
 
 
 
 
 
 
Database 
setup 
 
 
 
 
 
 
Data 
collection 
 
 
 
 
 
 
Database 
validation 
 
 
 
 
 
 
Statistical 
analysis 
 
 
 
 
 
 
Final report 
of results 
 
 
 
 
 
 
 
 
 
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8. Bibliographic references 
 
1. ​ Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. 
Surviving sepsis campaign: international guidelines for management of sepsis and 
septic shock 2021. Intensive Care Med. 2021;47: 1181–1247. 
2. ​ Llor C, Bjerrum L. Antimicrobial resistance: risk associated with antibiotic overuse and 
initiatives to reduce the problem. Ther Adv Drug Saf. 2014;5: 229–241. 
3. ​ CDC. Controlling the Emergence and Spread of. In: Antimicrobial Resistance [Internet]. 
29 Jan 2025 [cited 24 Mar 2025]. Available: 
https://www.cdc.gov/antimicrobial-resistance/prevention/index.html 
4. ​ Muteeb G, Rehman MT, Shahwan M, Aatif M. Origin of Antibiotics and Antibiotic 
Resistance, and Their Impacts on Drug Development: A Narrative Review. 
Pharmaceuticals (Basel). 2023;16. doi:10.3390/ph16111615 
5. ​ Ramasco F, Méndez R, Suarez de la Rica A, González de Castro R, Maseda E. Sepsis 
Stewardship: The Puzzle of Antibiotic Therapy in the Context of Individualization of 
Decision Making. Journal of Personalized Medicine. 2024;14: 106. 
6. ​ Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. 
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). 
JAMA. 2016;315: 801–810. 
7. ​ CDC. About. In: Sepsis [Internet]. 13 May 2024 [cited 9 Mar 2025]. Available: 
https://www.cdc.gov/sepsis/about/index.html 
8. ​ Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, et al. 
Developing a New Definition and Assessing New Clinical Criteria for Septic Shock: For 
the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). 
JAMA. 2016;315: 775. 
9. ​ Velazquez-Meza ME, Galarde-López M, Carrillo-Quiróz B, Alpuche-Aranda CM. 
Antimicrobial resistance: One Health approach. Vet World. 2022;15: 743–749. 
10. ​ One Health. [cited 22 Mar 2025]. Available: 
https://www.who.int/europe/initiatives/one-health 
11. ​ Kumar NR, Balraj TA, Kempegowda SN, Prashant A. Multidrug-Resistant Sepsis: A 
Critical Healthcare Challenge. Antibiotics. 2024;13: 46. 
12. ​ Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, 
regional, and national sepsis incidence and mortality, 1990-2017: analysis for the 
Global Burden of Disease Study. Lancet. 2020;395: 200–211. 
13. ​ La Via L, Sangiorgio G, Stefani S, Marino A, Nunnari G, Cocuzza S, et al. The Global 
Burden of Sepsis and Septic Shock. Epidemiologia. 2024;5: 456–478. 
14. ​ Rhee C, Dantes R, Epstein L, Murphy DJ, Seymour CW, Iwashyna TJ, et al. Incidence 
and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014. JAMA. 
2017;318: 1241–1249. 
15. ​ Fleischmann-Struzek C, Mikolajetz A, Schwarzkopf D, Cohen J, Hartog CS, Pletz M, et 
27 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
al. Challenges in assessing the burden of sepsis and understanding the inequalities of 
sepsis outcomes between National Health Systems: secular trends in sepsis and 
infection incidence and mortality in Germany. Intensive Care Med. 2018;44: 1826–1835. 
16. ​ Southeast Asia Infectious Disease Clinical Research Network. Causes and outcomes of 
sepsis in southeast Asia: a multinational multicentre cross-sectional study. Lancet Glob 
Health. 2017;5: e157–e167. 
17. ​ Weng L, Xu Y, Yin P, Wang Y, Chen Y, Liu W, et al. National incidence and mortality of 
hospitalized sepsis in China. Crit Care. 2023;27: 84. 
18. ​ References & Sources. In: The UK Sepsis Trust [Internet]. 9 Aug 2024 [cited 9 Mar 
2025]. Available: https://sepsistrust.org/about-sepsis/references-sources/ 
19. ​ Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, et al. Hospital deaths in 
patients with sepsis from 2 independent cohorts. JAMA. 2014;312: 90–92. 
20. ​ Shankar-Hari M, Harrison DA, Rowan KM. Differences in Impact of Definitional 
Elements on Mortality Precludes International Comparisons of Sepsis Epidemiology-A 
Cohort Study Illustrating the Need for Standardized Reporting. Critical care medicine. 
2016;44. doi:10.1097/CCM.0000000000001876 
21. ​ Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. 
Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, 
and associated costs of care. Crit Care Med. 2001;29: 1303–1310. 
22. ​ Divatia JV, Amin PR, Ramakrishnan N, Kapadia FN, Todi S, Sahu S, et al. Intensive 
Care in India: The Indian Intensive Care Case Mix and Practice Patterns Study. Indian J 
Crit Care Med. 2016;20: 216–225. 
23. ​ Machado FR, Cavalcanti AB, Bozza FA, Ferreira EM, Fs AC, Sousa JL, et al. The 
epidemiology of sepsis in Brazilian intensive care units (the Sepsis PREvalence 
Assessment Database, SPREAD): an observational study. The Lancet Infectious 
diseases. 2017;17. doi:10.1016/S1473-3099(17)30322-5 
24. ​ Levy MM, Rhodes A, Phillips GS, Townsend SR, Schorr CA, Beale R, et al. Surviving 
Sepsis Campaign: association between performance metrics and outcomes in a 
7.5-year study. Intensive care medicine. 2014;40. doi:10.1007/s00134-014-3496-0 
25. ​ Kaukonen KM, Bailey M, Suzuki S, Pilcher D, Bellomo R. Mortality related to severe 
sepsis and septic shock among critically ill patients in Australia and New Zealand, 
2000-2012. JAMA. 2014;311. doi:10.1001/jama.2014.2637 
26. ​ Thwaites CL, Lundeg G, Dondorp AM, sepsis in resource-limited settings–expert 
consensus recommendations group of the European Society of Intensive Care 
Medicine (ESICM) and the Mahidol-Oxford Research Unit (MORU) in Bangkok, 
Thailand. Recommendations for infection management in patients with sepsis and 
septic shock in resource-limited settings. Intensive Care Med. 2016;42: 2040–2042. 
27. ​ Schultz MJ, Dunser MW, Dondorp AM, Adhikari NKJ, Iyer S, Kwizera A, et al. Current 
challenges in the management of sepsis in ICUs in resource-poor settings and 
suggestions for the future. Intensive Care Med. 2017;43: 612–624. 
28. ​ Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The 
SOFA (Sepsis-related Organ Failure Assessment) score to describe organ 
28 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the 
European Society of Intensive Care Medicine. Intensive Care Med. 1996;22: 707–710. 
29. ​ van der Slikke EC, An AY, Hancock REW, Bouma HR. Exploring the pathophysiology of 
post-sepsis syndrome to identify therapeutic opportunities. EBioMedicine. 2020;61: 
103044. 
30. ​ Wang T, Derhovanessian A, De Cruz S, Belperio JA, Deng JC, Hoo GS. Subsequent 
Infections in Survivors of Sepsis. Journal of Intensive Care Medicine. 2014 [cited 13 Apr 
2025]. doi:10.1177/0885066612467162 
31. ​ Paoli CJ, Reynolds MA, Sinha M, Gitlin M, Crouser E. Epidemiology and Costs of 
Sepsis in the United States-An Analysis Based on Timing of Diagnosis and Severity 
Level. Crit Care Med. 2018;46: 1889–1897. 
32. ​ Buchman TG, Simpson SQ, Sciarretta KL, Finne KP, Sowers N, Collier M, et al. Sepsis 
Among Medicare Beneficiaries: 1. The Burdens of Sepsis, 2012-2018. Crit Care Med. 
2020;48: 276–288. 
33. ​ Arefian H, Heublein S, Scherag A, Brunkhorst FM, Younis MZ, Moerer O, et al. 
Hospital-related cost of sepsis: A systematic review. J Infect. 2017;74: 107–117. 
34. ​ Conde KAP, Silva E, Silva CO, Ferreira E, Freitas FGR, Castro I, et al. Differences in 
Sepsis Treatment and Outcomes between Public and Private Hospitals in Brazil: A 
Multicenter Observational Study. PLOS ONE. 2013;8: e64790. 
35. ​ Surviving Sepsis Campaign. In: Society of Critical Care Medicine (SCCM) [Internet]. 
[cited 6 Apr 2025]. Available: https://www.sccm.org/survivingsepsiscampaign 
36. ​ Aslam B, Khurshid M, Arshad MI, Muzammil S, Rasool M, Yasmeen N, et al. Antibiotic 
Resistance: One Health One World Outlook. Front Cell Infect Microbiol. 2021;11: 
771510. 
37. ​ Bharadwaj A, Rastogi A, Pandey S, Gupta S, Sohal JS. Multidrug-Resistant Bacteria: 
Their Mechanism of Action and Prophylaxis. BioMed Research International. 
2022;2022: 5419874. 
38. ​ Coluzzi C, Rocha EPC. The Spread of Antibiotic Resistance Is Driven by Plasmids 
Among the Fastest Evolving and of Broadest Host Range. Mol Biol Evol. 2025;42. 
doi:10.1093/molbev/msaf060 
39. ​ One Health. [cited 22 Mar 2025]. Available: 
https://www.who.int/europe/initiatives/one-health 
40. ​ GBD 2021 Antimicrobial Resistance Collaborators. Global burden of bacterial 
antimicrobial resistance 1990-2021: a systematic analysis with forecasts to 2050. 
Lancet. 2024;404: 1199–1226. 
41. ​ Global antimicrobial resistance and use surveillance system (‎GLASS)‎ report: 2022. 
World Health Organization; 9 Dec 2022 [cited 3 Oct 2023]. Available: 
https://www.who.int/publications/i/item/9789240062702 
42. ​ Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. 
Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an 
international expert proposal for interim standard definitions for acquired resistance. 
29 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
Clin Microbiol Infect. 2012;18: 268–281. 
43. ​ Magiorakos A-P, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. 
Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an 
international expert proposal for interim standard definitions for acquired resistance. 
Clin Microbiol Infect. 2012;18: 268–281. 
44. ​ Arulkumaran N, Routledge M, Schlebusch S, Lipman J, Morris AC. 
Antimicrobial-associated harm in critical care: a narrative review. Intensive Care 
Medicine. 2020;46: 225. 
45. ​ Website. doi:10.1093/cid/ciad105 
46. ​ Spellberg B, Rice LB. Duration of Antibiotic Therapy: Shorter Is Better. Ann Intern Med. 
2019;171: 210–211. 
47. ​ Teshome BF, Park T, Arackal J, Hampton N, Kollef MH, Micek ST. Preventing New 
Gram-negative Resistance Through Beta-lactam De-escalation in Hospitalized Patients 
With Sepsis: A Retrospective Cohort Study. Clin Infect Dis. 2024;79: 826–833. 
48. ​ De Bus L, Depuydt P, Steen J, Dhaese S, De Smet K, Tabah A, et al. Antimicrobial 
de-escalation in the critically ill patient and assessment of clinical cure: the DIANA 
study. Intensive Care Medicine. 2020;46: 1404. 
49. ​ Santacroce L, Di Domenico M, Montagnani M, Jirillo E. Antibiotic Resistance and 
Microbiota Response. Curr Pharm Des. 2023;29: 356–364. 
50. ​ Larsson DGJ, Flach C-F. Antibiotic resistance in the environment. Nature Reviews 
Microbiology. 2021;20: 257–269. 
51. ​ Ecological effects of antibiotics on natural ecosystems: A review. Microchemical 
Journal. 2018;136: 25–39. 
52. ​ Rhee C, Kadri SS, Dekker JP, Danner RL, Chen H-C, Fram D, et al. Prevalence of 
Antibiotic-Resistant Pathogens in Culture-Proven Sepsis and Outcomes Associated 
With Inadequate and Broad-Spectrum Empiric Antibiotic Use. JAMA Netw Open. 
2020;3: e202899. 
53. ​ Khilnani GC, Zirpe K, Hadda V, Mehta Y, Madan K, Kulkarni A, et al. Guidelines for 
Antibiotic Prescription in Intensive Care Unit. Indian Journal of Critical Care Medicine : 
Peer-reviewed, Official Publication of Indian Society of Critical Care Medicine. 2019;23: 
S1. 
54. ​ Roberts JA, Taccone FS, Lipman J. Understanding PK/PD. Intensive Care Medicine. 
2015;42: 1797–1800. 
55. ​ Cusack R, Little E, Martin-Loeches I. Practical Lessons on Antimicrobial Therapy for 
Critically Ill Patients. Antibiotics. 2024;13: 162. 
56. ​ Shappell CN, Klompas M, Rhee C. Do Prolonged Infusions of β-Lactam Antibiotics 
Improve Outcomes in Critically Ill Patients With Sepsis? JAMA. 2023;330: 126–128. 
57. ​ Li X, Jiang Z. Do prolonged infusions of β-lactam antibiotics improve outcomes in 
critically ill patients with sepsis? It is time to say yes. Critical Care. 2024;28: 1–3. 
30 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
58. ​ Winiszewski H, Despres C, Puyraveau M, Lagoutte-Renosi J, Montange D, Besch G, et 
al. β-lactam dosing at the early phase of sepsis: Performance of a pragmatic protocol 
for target concentration achievement in a prospective cohort study. J Crit Care. 
2022;67: 141–146. 
59. ​ Bastin MT, Rech M, Betthauser K, Heavner M, Horng M, King T, et al. 1206: 
Beta-lactam dosing in patients with sepsis and Aki: A multicenter observational study. 
Crit Care Med. 2023;51: 601–601. 
60. ​ Surveillance of antimicrobial resistance in Europe 2017. In: European Centre for 
Disease Prevention and Control [Internet]. 15 Nov 2018 [cited 3 Oct 2023]. Available: 
https://www.ecdc.europa.eu/en/publications-data/surveillance-antimicrobial-resistance-e
urope-2017 
61. ​ van Duin D, Doi Y. The global epidemiology of carbapenemase-producing 
Enterobacteriaceae. Virulence. 2017;8: 460–469. 
62. ​ Zhou R, Fang X, Zhang J, Zheng X, Shangguan S, Chen S, et al. Impact of 
carbapenem resistance on mortality in patients infected with : a systematic review and 
meta-analysis. BMJ Open. 2021;11: e054971. 
63. ​ World Health Organization. WHO bacterial priority pathogens list, 2024: bacterial 
pathogens of public health importance, to guide research, development, and strategies 
to prevent and control antimicrobial resistance. World Health Organization; 2024. 
64. ​ Sawa T, Kooguchi K, Moriyama K. Molecular diversity of extended-spectrum 
β-lactamases and carbapenemases, and antimicrobial resistance. Journal of Intensive 
Care. 2020;8: 1–13. 
65. ​ Martínez-Martínez L, González-López JJ. Carbapenemases in Enterobacteriaceae: 
types and molecular epidemiology. Enferm Infecc Microbiol Clin. 2014;32 Suppl 4: 4–9. 
66. ​ Yong D, Toleman MA, Giske CG, Cho HS, Sundman K, Lee K, et al. Characterization of 
a New Metallo-β-Lactamase Gene, blaNDM-1, and a Novel Erythromycin Esterase 
Gene Carried on a Unique Genetic Structure in Klebsiella pneumoniae Sequence Type 
14 from India. Antimicrobial Agents and Chemotherapy. 2009;53: 5046. 
67. ​ Lauretti L, Riccio ML, Mazzariol A, Cornaglia G, Amicosante G, Fontana R, et al. 
Cloning and characterization of blaVIM, a new integron-borne metallo-beta-lactamase 
gene from a Pseudomonas aeruginosa clinical isolate. Antimicrobial agents and 
chemotherapy. 1999;43. doi:10.1128/AAC.43.7.1584 
68. ​ Osano E, Arakawa Y, Wacharotayankun R, Ohta M, Horii T, Ito H, et al. Molecular 
characterization of an enterobacterial metallo beta-lactamase found in a clinical isolate 
of Serratia marcescens that shows imipenem resistance. Antimicrobial agents and 
chemotherapy. 1994;38. doi:10.1128/AAC.38.1.71 
69. ​ Tamma PD, Doi Y, Bonomo RA, Johnson JK, Simner PJ, Antibacterial Resistance 
Leadership Group. A primer on AmpC β-lactamases: Necessary knowledge for an 
increasingly multidrug-resistant world. Clin Infect Dis. 2019;69: 1446–1455. 
70. ​ Livermore DM, Nicolau DP, Hopkins KL, Meunier D. Carbapenem-Resistant 
Enterobacterales, Carbapenem Resistant Organisms, Carbapenemase-Producing 
Enterobacterales, and Carbapenemase-Producing Organisms: Terminology Past its 
“Sell-By Date” in an Era of New Antibiotics and Regional Carbapenemase 
31 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
Epidemiology. Clin Infect Dis. 2020;71: 1776–1782. 
71. ​ Potter RF, D’Souza AW, Dantas G. The rapid spread of carbapenem-resistant 
Enterobacteriaceae. Drug Resist Updat. 2016;29: 30–46. 
72. ​ Thomas GR, Corso A, Pasterán F, Shal J, Sosa A, Pillonetto M, et al. Increased 
Detection of Carbapenemase-Producing Enterobacterales Bacteria in Latin America 
and the Caribbean during the COVID-19 Pandemic. Emerg Infect Dis. 2022;28: 1–8. 
73. ​ Geographic patterns of global isolates of carbapenem-resistant Klebsiella pneumoniae 
and the activity of ceftazidime/avibactam, meropenem/vaborbactam, and comparators 
against these isolates: Results from the Antimicrobial Testing Leadership and 
Surveillance (ATLAS) program, 2020. Int J Antimicrob Agents. 2022;60: 106679. 
74. ​ Echegorry M, Marchetti P, Sanchez C, Olivieri L, Faccone D, Martino F, et al. National 
Multicenter Study on the Prevalence of Carbapenemase-Producing Enterobacteriaceae 
in the Post-COVID-19 Era in Argentina: The RECAPT-AR Study. Antibiotics (Basel). 
2024;13. doi:10.3390/antibiotics13121139 
75. ​ Bermejo V, Spadaccini L, Elbert GR, Duarte AIE, Erbin M, Cahn P. Prevalencia de 
Staphylococcus aureus resistente a meticilina en infecciones de piel y partes blandas 
en pacientes ambulatorios. Medicina (B Aires). 2012;72: 283–286. 
76. ​ Tubaro C, Dominguez R, Hinojosa M, Mosca S, De Leo G, Cabrera RR. Prevalencia de 
Staphylococcus aureus y su sensibilidad antibiótica en aislamientos en infecciones de 
piel y partes blandas en pacientes ambulatorios. Actualizaciones en Sida e Infectología. 
2024 [cited 8 Mar 2025]. doi:10.52226/revista.v32i114.186 
77. ​ INFECCIONES DE PIEL Y PARTES BLANDAS Y PREVALENCIA DE SAMR EN 
PACIENTES AMBULATORIOS EN UN HOSPITAL GENERAL DE CABA. [cited 8 Mar 
2025]. Available: 
https://infectologia.info/abstracts/infecciones-de-piel-y-partes-blandas-y-prevalencia-de-
samr-en-pacientes-ambulatorios-en-un-hospital-general-de-caba/ 
78. ​ [No title]. [cited 8 Mar 2025]. Available: 
http://antimicrobianos.com.ar/wp-content/uploads/2023/10/Mapas-de-Resistencia-Antim
icrobiana-Red-WHONET-Argentina-2022.pdf 
79. ​ Pea F, Viale P. The antimicrobial therapy puzzle: could 
pharmacokinetic-pharmacodynamic relationships be helpful in addressing the issue of 
appropriate pneumonia treatment in critically ill patients? Clin Infect Dis. 2006;42: 
1764–1771. 
80. ​ De Waele JJ, Boelens J, Leroux-Roels I. Multidrug-resistant bacteria in ICU: fact or 
myth. Curr Opin Anaesthesiol. 2020;33: 156–161. 
81. ​ Nov. INFORME RESISTENCIA 2023 – ARGENTINA. [cited 24 Mar 2025]. Available: 
http://antimicrobianos.com.ar/2024/11/informe-resistencia-2023-argentina/ 
82. ​ Bassetti M, Righi E, Vena A, Graziano E, Russo A, Peghin M. Risk stratification and 
treatment of ICU-acquired pneumonia caused by multidrug- resistant/extensively 
drug-resistant/pandrug-resistant bacteria. Curr Opin Crit Care. 2018;24: 385–393. 
83. ​ Blot S, Antonelli M, Arvaniti K, Blot K, Creagh-Brown B, de Lange D, et al. 
Epidemiology of intra-abdominal infection and sepsis in critically ill patients: “AbSeS”, a 
32 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
multinational observational cohort study and ESICM Trials Group Project. Intensive 
Care Medicine. 2019;45: 1703–1717. 
84. ​ Tumbarello M, Trecarichi EM, Bassetti M, De Rosa FG, Spanu T, Di Meco E, et al. 
Identifying patients harboring extended-spectrum-beta-lactamase-producing 
Enterobacteriaceae on hospital admission: derivation and validation of a scoring 
system. Antimicrob Agents Chemother. 2011;55: 3485–3490. 
85. ​ Schlebusch S, Graham RMA, Jennison AV, Lassig-Smith MM, Harris PNA, Lipman J, et 
al. Standard rectal swabs as a surrogate sample for gut microbiome monitoring in 
intensive care. BMC Microbiology. 2022;22: 1–14. 
86. ​ Blagojevic C, Brown KA, Diong C, Fridman DJ, Johnstone J, Langford BJ, et al. 
Long-term Risk of Infection Among Patients Colonized With Antimicrobial-Resistant 
Pathogens: A Population-wide Cohort Study. Open Forum Infect Dis. 2024;11: ofae712. 
87. ​ Temkin E, Solter E, Lugassy C, Chen D, Cohen A, Schwaber MJ, et al. The Natural 
History of Carbapenemase-Producing Enterobacterales: Progression From Carriage of 
Various Carbapenemases to Bloodstream Infection. Clinical infectious diseases : an 
official publication of the Infectious Diseases Society of America. 2024;79. 
doi:10.1093/cid/ciae110 
88. ​ Cano Á, Gutiérrez-Gutiérrez B, Machuca I, Torre-Giménez J, Gracia-Ahufinger I, Natera 
AM, et al. Association between Timing of Colonization and Risk of Developing 
Klebsiella pneumoniae Carbapenemase-Producing K. pneumoniae Infection in 
Hospitalized Patients. Microbiology Spectrum. 2022 [cited 17 Feb 2025]. 
doi:10.1128/spectrum.01970-21 
89. ​ Giannella M, Trecarichi EM, De Rosa FG, Del Bono V, Bassetti M, Lewis RE, et al. Risk 
factors for carbapenem-resistant Klebsiella pneumoniae bloodstream infection among 
rectal carriers: a prospective observational multicentre study. Clin Microbiol Infect. 
2014;20: 1357–1362. 
90. ​ Mertz D, Frei R, Periat N, Zimmerli M, Battegay M, Flückiger U, et al. Exclusive 
Staphylococcus aureus throat carriage: at-risk populations. Arch Intern Med. 2009;169: 
172–178. 
91. ​ Fosch S, Yones C, Trossero M, Grosso O, Nepote A. Portación nasal de 
Staphylococcus aureus en individuos de la comunidad: factores epidemiológicos. Acta 
bioquím clín latinoam. 2012;46: 59–68. 
92. ​ SADI. Recomendaciones Intersociedades para el Manejo de Infecciones de Piel y 
Partes Blandas. SADI - Sociedad Argentina de Infectología; 19 Sep 2010 [cited 9 Mar 
2025]. Available: 
https://www.sadi.org.ar/documentos/guias-recomendaciones-y-consensos/item/44-reco
mendaciones-intersociedades-para-el-manejo-de-infecciones-de-piel-y-partes-blandas 
93. ​ Antibiotic Timing and Progression to Septic Shock Among Patients in the ED With 
Suspected Infection. CHEST. 2022;161: 112–120. 
94. ​ Peiffer-Smadja N, Dellière S, Rodriguez C, Birgand G, Lescure F-X, Fourati S, et al. 
Machine learning in the clinical microbiology laboratory: has the time come for routine 
practice? Clin Microbiol Infect. 2020;26: 1300–1309. 
95. ​ Kherabi Y, Thy M, Bouzid D, Antcliffe DB, Rawson TM, Peiffer-Smadja N. Machine 
33 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
learning to predict antimicrobial resistance: future applications in clinical practice? Infect 
Dis Now. 2024;54: 104864. 
96. ​ Cornistein W, Santonato D, Novau PA, Fabbro LG, Jorge MF, Malvicini MA, et al. 
Synergy between infection control and antimicrobial stewardship programs to control 
carbapenem-resistant Enterobacterales. Antimicrobial stewardship & healthcare 
epidemiology : ASHE. 2023;3. doi:10.1017/ash.2023.439 
97. ​ THE 17 GOALS. [cited 13 Apr 2025]. Available: https://sdgs.un.org/goals 
98. ​ Goal 3. [cited 13 Apr 2025]. Available: https://sdgs.un.org/goals/goal3 
99. ​ Objetivo 9. [cited 14 Apr 2025]. Available: https://sdgs.un.org/es/goals/goal9 
100. ​ Goal 10. [cited 13 Apr 2025]. Available: https://sdgs.un.org/goals/goal10 
101. ​ Rose N, Matthäus-Krämer C, Schwarzkopf D, Scherag A, Born S, Reinhart K, et al. 
Association between sepsis incidence and regional socioeconomic deprivation and 
health care capacity in Germany – an ecological study. BMC Public Health. 2021;21: 
1–11. 
102. ​ Ratti MFG, Martinez B. Reestructuración de la Central de Emergencias durante la 
pandemia. Rev Hosp Ital BAires. 2022;42: 46–48. 
103. ​ [No title]. [cited 16 Apr 2025]. Available: 
https://hiba.hospitalitaliano.org.ar/archivos/noticias_archivos/74/archivos/Plan%20de%2
0Accion%20Central%20de%20Emergencias%20-%20Covid19.pdf 
104. ​ [No title]. [cited 16 Apr 2025]. Available: 
https://faso.org.ar/imagenes/covid/protocolo_italiano-17-4.pdf 
105. ​ Website. Available: 
https://trovare.hospitalitaliano.org.ar/greenstone/collect/instit/index/assoc/D1828.dir/intr-
memoria-hiba-2022.pdf 
106. ​ Centro Nacional de Terminología en Salud. In: Argentina.gob.ar [Internet]. 19 Jul 
2021 [cited 12 Nov 2023]. Available: https://www.argentina.gob.ar/salud/terminologia 
107. ​ Taylor SP, Kowalkowski MA, Skewes S, Chou S-H. Real-World Implications of 
Updated Surviving Sepsis Campaign Antibiotic Timing Recommendations. Crit Care 
Med. 2024;52: 1002–1006. 
108. ​ Im Y, Kang D, Ko R-E, Lee YJ, Lim SY, Park S, et al. Time-to-antibiotics and clinical 
outcomes in patients with sepsis and septic shock: a prospective nationwide multicenter 
cohort study. Critical Care. 2022;26: 1–10. 
109. ​ [No title]. [cited 16 Apr 2025]. Available: 
https://www.hospitalitaliano.org.ar/multimedia/archivos/noticias_archivos/109/guias_par
a_medicos/109_sepsis.pdf 
110. ​[No title]. [cited 16 Apr 2025]. Available: 
https://hiba.hospitalitaliano.org.ar/archivos/noticias_archivos/109/archivos/Sepsis%20sit
io%20infecto%20final-converted.pdf 
111. ​El Gobierno Nacional decretó el aislamiento social preventivo y obligatorio. In: 
34 

Version 2 Protocol: Predictive Model Project for Antimicrobial Resistance (AMR) in Septic Patients Attending the 
Emergency Care Center (CEA). August 2025 
Argentina.gob.ar [Internet]. 20 Mar 2020 [cited 14 Apr 2025]. Available: 
https://www.argentina.gob.ar/noticias/el-gobierno-nacional-decreto-el-aislamiento-social
-preventivo-y-obligatorio 
112. ​Sep. AÑO 2023. [cited 13 Apr 2025]. Available: 
http://antimicrobianos.com.ar/2024/09/2023/ 
113. ​[No title]. [cited 13 Apr 2025]. Available: 
http://antimicrobianos.com.ar/wp-content/uploads/2024/12/Mapas-de-Resistencia-Antim
icrobiana-Red-WHONET-Argentina-2023.pdf 
114. ​Immunocompromised Travelers. [cited 18 Feb 2025]. Available: 
https://wwwnc.cdc.gov/travel/yellowbook/2024/additional-considerations/immunocompr
omised-travelers 
115. ​Rubin LG, Levin MJ, Ljungman P, Davies EG, Avery R, Tomblyn M, et al. 2013 IDSA 
clinical practice guideline for vaccination of the immunocompromised host. Clinical 
infectious diseases : an official publication of the Infectious Diseases Society of 
America. 2014;58. doi:10.1093/cid/cit816 
116. ​[No title]. [cited 11 Feb 2024]. Available: 
https://www.sah.org.ar/revistasah/numeros/vol21/extra3/36-vol21-extra_noviembre.pdf 
117. ​Website. Available: Sauerbrei, Willi. “The Use of Resampling Methods to Simplify 
Regression Models in Medical Statistics.” Journal of the Royal Statistical Society. Series 
C (Applied Statistics) 48, no. 3 (1999): 313–29. http://www.jstor.org/stable/2680827. 
118. ​Jung K, Kashyap S, Avati A, Harman S, Shaw H, Li R, et al. A framework for making 
predictive models useful in practice. J Am Med Inform Assoc. 2021;28: 1149–1158. 
 
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