Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to Guide Empiric Antibiotic Treatment for Ventilator-Associated Pneumonia in a Mexican Tertiary Care University Hospital

doi:10.21203/rs.3.rs-4714294/v1

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Research Article

Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to Guide Empiric Antibiotic Treatment for Ventilator-Associated Pneumonia in a Mexican Tertiary Care University Hospital

https://doi.org/10.21203/rs.3.rs-4714294/v1

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Background

Ventilator-associated pneumonia (VAP) is a prevalent and serious nosocomial infection among critically ill patients that leads to significant morbidity, mortality, and increased healthcare costs. The heterogeneity in local epidemiology and antibiotic resistance complicates the selection of effective empirical treatments. The weighted-incidence syndromic combination antibiogram (WISCA) tool has been proposed to optimize empirical antibiotic therapy by leveraging local microbiological data.

Objective

This study aimed to develop a WISCA tailored for VAP in a Mexican tertiary care university hospital. The objective is to improve empirical antibiotic coverage by considering the unique pathogen landscape and resistance patterns of the institution.

Methods

This research was conducted from June 2021 to June 2024 using clinical registries and microbiological data from a tertiary hospital in an upper middle-income Latin American country. Data, including demographic and clinical characteristics, were collected from patients who developed VAP. We employed a Bayesian hierarchical logistic regression model to estimate the coverage of various antibiotic regimens. We also analyzed the impact of initial inappropriate empiric treatment on in-hospital mortality and prolonged hospital stays in our population using multivariate logistic regression analysis.

Results

A total of 197 VAP episodes from 129 patients were analyzed. The median age was 44 years (IQR 35–56), and the median Charlson comorbidity index was 1 (IQR 0–2). The most common pathogens were Acinetobacter baumannii and Pseudomonas aeruginosa. Tigecycline-based combined regimens showed greater median coverage (+ 47.97%) than nontigecycline-based combined regimens, particularly against multidrug-resistant Acinetobacter baumannii. Inappropriate initial empirical treatment was associated with prolonged hospital stays but not directly with in-hospital mortality. However, inappropriate treatment during the entire VAP episode was significantly associated with increased mortality.

Conclusions

The tailored WISCA model provided robust coverage estimates. This study demonstrated the potential to optimize empirical antibiotic regimens, especially tigecycline-based combinations. This study highlights the importance of local epidemiological data in guiding empirical therapy and reducing the consequences of inappropriate antibiotic use.

Ventilator-associated pneumonia

weighted incidence syndromic combination antibiogram

Bayesian hierarchical model

antibiotic resistance

empirical therapy

Acinetobacter baumannii

tigecycline

Ventilator-associated pneumonia (VAP) is one of the most common nosocomial infections in critically ill patients and is associated with high morbidity, mortality, and healthcare costs. It affects 10–25% of hospitalized patients. (1–4) VAP primarily impacts the most vulnerable hospitalized patients, leading to extended hospital stays, and is a leading cause of mortality related to healthcare-associated infections. (5, 6) Adequate empirical treatment of VAP is crucial due to the rapid progression of the infection and the emergence of multidrug-resistant organisms (MDROs). (7) Heterogeneity in local epidemiology and bacterial resistance patterns further complicates the selection of appropriate empirical antibiotics, underscoring the need for customized tools based on local data. (8, 9)

Currently, antimicrobial resistance (AMR) is a global crisis. (10–12) AMR is correlated with increased morbidity, mortality, prolonged length of stay, and elevated healthcare costs. (10) Given its high prevalence, prompt recognition and etiologic diagnosis are essential to optimize antibiotic use, mitigate the emergence of AMR, and decrease the incidence of Clostridioides difficile infections. (13, 14) One of the most significant risk factors for the acquisition of multidrug-resistant bacteria at the population level is hospital ecology, particularly in low-income countries. (15–18)

The weighted-incidence syndromic combination antibody (WISCA) has emerged as an innovative tool to address this challenge. (19–20) The WISCA allows the evaluation of disease-specific empirical antibiotic regimens by considering local pathogen susceptibilities and weighting the incidence of each pathogen in the infectious syndrome under study. (19) Previous studies have demonstrated the efficacy of WISCA in various infections, including bloodstream infections, VAP, febrile neutropenia, neonatal sepsis, and urinary tract infections, by utilizing a Bayesian approach to estimate the coverage probabilities of different antibiotic combinations. (21–24)

This study aimed to develop and design a WISCA tailored for VAP treatment at a tertiary care university hospital in Mexico and to estimate the potential improvement in adequate empiric antibiotic coverage considering the unique pathogen landscape and resistance patterns within our institution. This approach not only seeks to optimize antibiotic use but also to provide a framework for other hospitals to develop their own WISCA models based on local data, fostering more effective and targeted empirical therapy for VAP.

Ethics

This study involving humans was approved by “Comité de ética en investigación en ciencias de la salud del Centro Universitario de Tlajomulco, Universidad de Guadalajara” (CUTLAJO/DS/CEICS/018/24) and conducted in accordance with the Helsinki Declaration. This study was conducted in accordance with the national legislation and institutional requirements. As the study was performed retrospectively and only deidentified data were used, informed consent was waived.

Population and Eligibility Criteria

In our university hospital, the Internal Medicine Department oversees a unit for patients on invasive mechanical ventilation (IMV), in addition to an intensive care unit managed by the Critical Care Medicine Department. Most patients with nonsurgical pathologies requiring IMV are located in this unit. We reviewed the medical records of patients admitted to the Internal Medicine Department with the International Classification of Diseases 10th Edition (ICD-10) code J95.851: Ventilator-associated pneumonia (VAP). Additionally, we reviewed cultures of respiratory samples, including endotracheal aspirates and aspirates through tracheostomy tubes, from the Internal Medicine Department. These records were obtained from patients admitted between June 2021 and June 2024.

We verified that the patients met the definition of VAP as established by the American Thoracic Society and the Infectious Diseases Society of America (IDSA/ATS) (25) and that they had positive respiratory cultures from the hospital’s microbiology laboratory. Patients younger than 15 years of age and those with more than 10% missing data from clinical records were excluded.

Demographic data, the Charlson Comorbidity Index (CCI), the Sequential Organ Failure Assessment (SOFA) score, and other clinical parameters were systematically collected from patient medical records. The data included variables such as the PaO2/FiO2 ratio, presence of acute respiratory distress syndrome (ARDS) based on the criteria of Matthay et al., (26) SARS-CoV-2 infection status, presence of septic shock (in accordance with the Third International Consensus Definitions for Sepsis and Septic Shock), duration of IMV until the onset of VAP, duration of IMV following VAP treatment, and length of hospital stay after VAP development.

Bacterial isolates were identified using standard methods, and their antibiotic susceptibility was tested with the VITEK 2 system by Biomerieux (Marcy l’Etoile, France). This involved the use of specific panels or disc diffusion techniques in accordance with Clinical and Laboratory Standards Institute (CLSI) guidelines. (27) The results were interpreted based on CLSI breakpoints, with extrapolations made for agents lacking established breakpoints or MIC values.

WISCA Development

The WISCA model was designed based on pathogens isolated from patients with VAP. A VAP event was considered if the patient met the IDSA/ATS definition and had a positive respiratory culture. If the isolated organism was susceptible to an antibiotic, it was reported to be susceptible to the empirical antimicrobial regimen employed. An intermediate susceptibility to a particular antimicrobial was reported as resistant for the model. If there was more than one culture of the same microorganism during an episode, the most resistant culture was selected. If there was more than one organism in a respiratory culture, both organisms were selected, and the patient was classified as having VAP associated with multiple organisms. For patients with multiple subsequent cultures, it was verified that these patients experienced new VAP events based on the clinical presentation and culture timing. Subsequent cultures not associated with a VAP event were excluded to avoid results influenced by pharmacological pressure. If they corresponded to a new VAP event, they were included in the analysis.

We studied the coverage of the most used regimens in our institution according to local and international guidelines and for which automated sensitivity testing is routinely performed. These were as follows: meropenem (MEM), piperacillin/tazobactam (TZP), cefepime (FEP), and ceftazidime (CAZ); combination therapy: ceftazidime + linezolid (CAZ + LNZ) or vancomycin (VA); meropenem + linezolid (MEM + LNZ) or vancomycin (VA); piperacillin/tazobactam + linezolid (TZP + LNZ) or vancomycin (VA); and cefepime + linezolid (FEP + LNZ) or vancomycin (VA). Additionally, based on our local epidemiology, we studied ceftazidime + tigecycline (CAZ + TGC), meropenem + tigecycline (MEM + TGC), cefepime + tigecycline (FEP + TGC), and piperacillin/tazobactam + tigecycline (TZP + TGC).

Statistical analysis

Demographic data are reported as simple relative frequencies. The normality of the data distribution was assessed using the Shapiro–Wilk test. Pearson’s chi-squared and Fisher’s exact tests were used to compare proportions as appropriate. For comparisons of quantitative variables, Student’s t tests and Wilcoxon-Mann–Whitney tests were used for normally and nonnormally distributed data, respectively.

In line with previous studies, a Bayesian hierarchical logistic regression model was used to estimate the coverage of antibiotic regimens against pathogens linked to VAP. The model featured a hierarchical structure with varying intercepts for pathogens and empirical regimens, ensuring stable and reliable coverage estimates, especially for regimens with limited sensitivity testing. The detailed methodology can be found in the supplementary material. The Hamiltonian Monte Carlo (HMC) algorithm, implemented via Stan software, was used for Bayesian inference. Parameters and coverage estimates were summarized using the median and 95% highest density intervals (HDIs), with wider HDIs indicating greater uncertainty. Covariates for age and sex were included to account for potential variability.

Finally, to identify the impact of an inadequate antibiotic regimen on clinical outcomes, a multivariate logistic regression model was used. Clinical outcomes were in-hospital mortality and prolonged hospital stay, defined as a stay exceeding the 90th percentile of our patient population, the latter of which was analyzed in surviving patients. Variables included in the model had a p value of < 0.1 and were considered to be biologically plausible. A stepwise method for the selection of variables in the model was used. The Hosmer–Lemeshow test was used to determine the goodness of fit, with values greater than 0.1 considered appropriate.

The statistical analysis was implemented using Python for data analysis. The Bayesian hierarchical model was fitted using the CmdStanPy package (version 0.9.76) with the underlying Stan framework (version 2.26.1). Model diagnostics and posterior analyses were conducted using the ArviZ package (version 0.11.2).

According to the specified criteria, we documented 197 episodes of VAP in 129 patients, as depicted in Fig. 1.

The median age of the patients included was 44 years (IQR 35–56), 31% were women (n = 40), and the median Charlson comorbidity index was 1 (IQR 0–2). The median SOFA score was 5 (IQR 3–7), vasopressors were required for 40.60% of the VAP episodes (n = 80), and 26.39% of the episodes met the definition for ARDS (n = 52). The median hospital stay after VAP development was 12 days (IQR 6–21), the 90th percentile of days after VAP development was 29.4 days, and the in-hospital mortality rate for patients with VAP was 34.51% (n = 68). The remaining sociodemographic and clinical characteristics are shown in Table 1.

In terms of local epidemiology and antibiotic resistance, the most frequently isolated organisms were Acinetobacter baumannii (36.04%, n = 71), Enterobacterales (26.39%, n = 52), Pseudomonas aeruginosa (18.25%, n = 36), Staphylococcus aureus (10.15%, n = 20), and Stenotrophomonas maltophilia (8.12%, n = 16). In 41.12% of the episodes, more than one organism was isolated (n = 81). The remaining isolated organisms can be found in Table 1.

According to standardized international terminology for describing acquired resistance profiles, (28) 87.32% of Acinetobacter baumannii isolates were classified as XDR (extensively drug-resistant) Acinetobacter baumannii (XDR-AB) (n = 62). For Enterobacterales, 28.3% of the isolates were MDR (multidrug resistant) Enterobacteriaceae (n = 15), and 7.5% were carbapenem-resistant Enterobacteriaceae (n = 4). For Pseudomonas aeruginosa, 19.44% of the isolates were MDR, and 5.55% were XDR. Only 1% of the Staphylococcus aureus isolates were methicillin-resistant Staphylococcus aureus (MRSA) (n = 2). The rest of the resistance profiles can be found in Table 2.

The most frequently used initial empiric therapy regimens were meropenem (24.87%, n = 49), colistin + tigecycline (18.27%, n = 36), piperacillin/tazobactam (13.7%, n = 27), and meropenem + linezolid (7.61%, n = 15). Other regimens were used in 35.53% (n = 70) of the patients. The remaining antimicrobial regimens are listed in Table S1 of the supplementary material. Initial empiric therapy was inappropriate for 45.18% of VAP episodes (n = 89). However, in 85.79% of the events, patients eventually received an appropriate regimen during the entire VAP episode. The median number of days to correct the initial inappropriate empiric treatment was 3 days (IQR 1-5.75) (Table 1).

Eight antibiotics were tested as monotherapies, and thirteen combined regimens were studied in the WISCA model for the overall pool of pathogens. The median estimated coverages ranged from 4.99% (vancomycin, VA) to 87.23% (cefepime + tigecycline, FEP + TGC). Tigecycline-based combined regimens showed greater median coverage (+ 47.97%) than nontigecycline-based combined regimens. The complete set of medians of the posterior distribution and the associated 95% highest density intervals (HDIs) are shown in Fig. 2.

The algorithm used to sample from the parameters posterior distributions achieved optimal convergence, with R^ index values always near 1 and good mixing for all chains, as shown in Table S2 of the supplementary material. The Monte Carlo Markov chain (MCMC) trace plots of the WISCA model parameters and the density plots of the posterior distributions of the model parameters are depicted in Figures S1 and S2 of the supplementary material.

We found significant differences in the distributions of VAP-causing microorganisms according to the days of invasive mechanical ventilation (IMV) prior to the development of VAP. Specifically, Enterobacterales and Staphylococcus aureus were more frequently isolated within the first 5 days of IMV, whereas Acinetobacter baumannii was more frequently isolated after 5 days of IMV (p < 0.02), as shown in Fig. 3 and Table 2.

Due to these differences in the distributions of the isolated microorganisms, we calculated the posterior coverages of the thirteen most commonly used antibiotic regimens for both time periods, as shown in Fig. 4. The complete set of medians of the posterior distribution and the associated 95% HDIs for these antibiotic regimens during these time periods are shown in Table S3, as well as the R^ index values of the WISCA models parameters in Table S4 of the supplementary material.

According to the univariate analyses, the presence of a low PaO2/FiO2 ratio, inappropriate treatment for VAP episodes, a high SOFA score, the need for vasopressors, the presence of ARDS, and Acinetobacter baumannii infection were positively associated with mortality. The Charlson comorbidity index and days of IMV after VAP development were positively associated with an extended hospital stay. The remaining sociodemographic and clinical variables can be found in Table S5 of the supplementary material.

According to the multivariate regression analysis, inappropriate treatment for the entire VAP event, a higher SOFA score, the need for vasopressors, the presence of ARDS, and Acinetobacter baumannii infection were independently positively associated with mortality. Interestingly, an increase in days of IMV after VAP development was negatively associated with mortality. On the other hand, initial empiric inappropriate treatment and an increase in days of IMV after VAP development were positively associated with an extended hospital stay. The goodness of fit was adequate for both models (Hosmer‒Lemeshow test, p = 0.988 and 0.925, respectively). The complete set of variables for both models is depicted in Table 3.

In our study, we developed a WISCA tool that expands the framework of classic hospital combined antibiograms by providing weighted coverage estimates based on the frequency of the pathogens identified. Similar to previous WISCA designs, we specified a Bayesian hierarchical logistic regression with random effects structures on the pathogens and the treatment regimens. Our study contributes to the limited knowledge concerning WISCA design for VAP in middle-income countries. To our knowledge, this is the first WISCA design for VAP in a Latin American country; previous studies have been conducted in high-income countries, specifically in Europe. (21–24)

Our patient population with VAP at our university hospital has a particular profile. The Internal Medicine Department oversees a critical care area where patients requiring IMV are treated without underlying surgical conditions. Additionally, with the presence of a geriatric department, the upper age limit of the internal medicine department patient population is 65 years. These circumstances allow us to describe a relatively young VAP patient population, enabling us to study the effects of inadequate treatment, local epidemiology, and bacterial resistance patterns on various clinical outcomes in patients without underlying surgical disease.

From the WISCA design, we can infer that the most commonly used therapies in our hospital, for which automated sensitivity testing is routinely performed, have coverage rates ranging from 37.71% (piperacillin/tazobactam monotherapy) to 49.82% (meropenem + linezolid combination). Due to the high incidence of XDR-AB causing VAP in our institution, tigecycline-based regimens perform better in terms of coverage than non-tigecycline-based regimens. This particularity of our local microbiology also reflects the high use of colistin-based regimens, which are the second most commonly used initial therapy in our population. The presented WISCA model could support the use of tigecycline-based combinations over colistin-based combinations, potentially improving outcomes by reducing future resistance development and minimizing adverse effects associated with colistin therapy.

Although tigecycline therapy is not FDA-approved for the treatment of VAP, it is highly utilized in cases of VAP caused by XDR-AB. (29–31) Recent studies support the effectiveness of tigecycline in treating VAP. A 2018 randomized controlled trial in China demonstrated that high-dose cefoperazone-sulbactam combined with tigecycline improved antimicrobial activity against VAP caused by XDR-AB. (32) Another 2020 study in China on elderly patients (> 60 years) comparing high-dose versus standard-dose tigecycline in VAP found that the eradication rate in the high-dose group (loading dose 200 mg, followed by a maintenance dose of 100 mg intravenously twice daily) was significantly greater than that in the standard-dose group, although it did not lower crude mortality. (33) A recent systematic review and meta-analysis published in 2023 by Feng et al. reported that high-dose tigecycline treatment for VAP caused by multidrug-resistant bacteria improved clinical efficacy without increasing adverse effects, maintaining a high safety profile. (34)

In our study, the distribution of organisms causing pneumonia across the days of IMV was significantly different. Enterobacterales and Staphylococcus aureus were more frequently isolated in the first 2–5 days of IMV, while Acinetobacter baumannii was more frequently isolated after 5 days. The designed WISCA models reflect greater susceptibility to antibiotic regimens in the first 5 days of VAP, which could inform the decision to avoid colistin-based regimens in the initial days of IMV, considering the previously described consequences associated with colistin-based treatments. (35)

Interestingly, our study revealed that initial inappropriate empiric treatment was not associated with increased mortality. However, inappropriate treatment throughout the entire VAP event was related to increased mortality in our multivariate model. This may be due to the rapid correction of the antimicrobial regimen in most patients with initially inappropriate empiric treatment, with a median correction time of 3 days (IQR 1-5.75), a relatively young patient population (median age of 44 years, IQR 35–56), and a population with fewer comorbidities (median Charlson Comorbidity Index of 1, IQR 0–2). Notably, an increase in days of IMV after VAP development was associated with reduced mortality, possibly due to a slow weaning process combined with a reduction in the incidence of VAP as the number of days of IMV increased (Fig. 3). Other variables associated with mortality were the need for vasopressors, the presence of ARDS, and Acinetobacter baumannii infection, which is consistent with other reports. Finally, initial inappropriate empiric treatment was positively associated with an extended hospital stay in our multivariate model, as was an increase in days of IMV after VAP development, as expected.

In an observational study performed in the Netherlands from 2013 to 2015, (36) researchers evaluated the effect of delayed appropriate empiric therapy on early mortality in patients with bloodstream infections. The authors did not find a significant impact on mortality in low-risk bloodstream infection patients, suggesting its limited importance compared to other determinants. These results were replicated in a multicenter retrospective cohort study conducted in critically ill patients, where empiric treatment was not associated with increased mortality in bacteremic patients. In a retrospective cohort of patients with VAP, Randhawa et al. (23) reported that hospital survival was similar among patients receiving adequate treatment within 24 hours versus those who did not receive early adequate treatment. However, the percentage of patients who received appropriate treatment was greater than 85% at 72 hours after culture collection, indicating rapid improvement of the antimicrobial regimen. Conversely, other studies suggest that improper initial antibiotic therapy is associated with mortality in patients with VAP. (37–39) These differences may be due to the heterogeneity of populations, the availability of resources, and the specialized attention of Intensive Care Units (ICUs).

The limitations of our study are primarily due to its retrospective nature. First, during data collection, some variables were missing and had to be excluded from the analyses. Additionally, certain variables intended for inclusion were not available in the hospital records. At our institution, systematic susceptibility testing for colistin was not performed at the time of this report, and testing for combinations of new beta-lactams with new beta-lactamase inhibitors only began this year; therefore, these combinations were not included in the analysis. To ensure accurate and reliable results regarding the impact of inappropriate treatment on clinical outcomes, further studies using prospective cohorts or clinical trials in the same population and region are recommended. This approach will allow for the inclusion of more data and better control of variables. It should be noted that the findings of this study are limited to a single hospital center, which may hinder the generalization of the data. Additionally, more WISCA models should be developed to cover different infectious syndromes and support empiric therapy regimens based on the heterogeneity of local epidemiology.

This study has highlighted several important points. Our hospital unit is a reference center that provides care to the uninsured population in western Mexico. Consequently, the incidence of patients requiring IMV and subsequently developing VAP is greater than commonly reported. To standardize the data, we excluded those with a high percentage of missing data to ensure accurate analysis. A major strength of this study was the development of a WISCA model with a Bayesian hierarchical design to enable the rational use of empiric treatment regimens based on local epidemiology. This will promote a standardized tool for designing empiric antibiotic treatments in our population.

In conclusion, our WISCA models offered highly informative coverage estimates, surpassing the limitations of traditional hospital susceptibility reports. Additionally, they can aid in reevaluating first-line treatments according to local guidelines. In our population, tigecycline-based regimens performed better in terms of coverage than did the more commonly used regimens. Although initial inappropriate empiric treatment was not associated with increased mortality, it was linked to extended hospital stays, and inappropriate treatment during the entire VAP episode was associated with hospital mortality. This methodology can guide empiric therapy and elucidate the consequences of inappropriate treatment in each population.

AMR. Antimicrobial resistance.

ARDS. Acute respiratory distress syndrome

CAZ. Ceftazidime

CCI. Charlson Comorbidity Index.

CLSI. Clinical and Laboratory Standards Institute

FEP. Cefepime

HDIs. Highest density intervals

ICD-10. International Classification of Diseases 10th Edition.

ICUs. Intensive Care Units

IDSA/ATS. American Thoracic Society and the Infectious Diseases Society of America

IMV. Invasive mechanical ventilation.

LNZ. Linezolid

LNZ. Vancomycin

MCMC. Monte Carlo Markov chain

MDR. Multidrug resistant

MDROs. Multidrug-resistant organisms.

MEM. Meropenem

MRSA. Methicillin-resistant Staphylococcus aureus

PaO2/FiO2 ratio. Partial pressure of oxygen in arterial blood to Fraction of inspired oxygen ratio.

SOFA. Sequential Organ Failure Assessment.

TZP. Piperacillin/tazobactam

TGC. Tigecycline

VAP. Ventilator-associated pneumonia.

WISCA. Weighted-incidence syndromic combination antibiogram.

XDR. Extensively drug-resistant

XDR-AB. Extensively drug-resistant Acinetobacter baumannii

Ethics statement

Consent for publication

Not applicable

Availability of data and materials

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Authors´ contributions

Jaime Briseno-Ramírez MD: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Software, Writing–original draft.

Adolfo Gómez-Quiroz: Investigation, Data curation, Writing–original draft.

Brenda Berenice Avila-Cardenas Investigation, Data curation, Writing – original draft.

Judith Carolina De Arcos-Jiménez Investigation, Data curation, Writing – original draft.

Leonardo Perales-Guerrero Methodology, Investigation, Writing – original draft.

Jaime F. Andrade-Villanueva Investigation, Supervision, Project administration, Writing – review & editing.

Pedro Martínez-Ayala Investigation, Supervision, Project administration, Writing – review & editing, Validation.

Acknowledgments

The authors have no acknowledgments to report.

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Table 1. Sociodemographic and Clinical Characteristics of Patients with Ventilator-Associated Pneumonia (VAP) and Isolated Organisms.

Variable	Total VAP events (n=197)	Men (n=140)	Women (n=57)	p value
Age (median, IQR)	44.0 (35.0-56.0)	47.5 (36.0-56.5)	40.0 (30.0-54.0)	0.037
Charlson comorbidity Index (median, IQR)	1.0 (0.0.0-2.0)	1.0 (0.0-2.0)	1.5 (0.25-3.75)	0.145
Clinical variables
Initial Inappropriate Empiric Treatment - N (%)	89 (45.18%)	59 (42.14%)	30 (52.63%)	0.236
Days to Correct Empiric Treatment - Median (IQR)	3.0 (1.0 - 5.75)	3.0 (1.0 - 6.25)	3.0 (2.0 - 5.0)	0.063
Inappropriate Treatment for VAP- N (%)	28 (14.21%)	18 (12.86%)	10 (17.54%))	0.529
Days from IMV to VAP development (median, IQR)	8.0 (4.0-15.0)	10.0 (4.0-16.0)	6.0 (4.0-12.0)	0.049
Days of IMV after VAP development (median, IQR)	9.0 (3.0-14.0)	10.0 (3.75-15.0)	8.0 (3.0-13.0)	0.579
Need for vasopressors - N (%)	80 (40.60)	57 (40.71)	23 (40.35)	1
SOFA score (median, IQR)	5.0 (3.0-7.0)	5.0 (3.0-7.0)	4.0 (2.0-6.0)	0.047
PaO2/FiO2 ratio (median, IQR)	232.5 (176.5-303.0)	232.0 (185.5-283.0)	233.0 (168.0-356.0)	0.280
ARDS diagnosis - N (%)	52 (26.39)	35 (25.00)	17 (29.82)	0.593
SARS-CoV-2 infection - N (%)	21 (10.65)	16 (11.43)	5 (8.77)	0.695
Multiple organisms isolated in VAP event - N (%)	81 (41.11)	63 (45.00)	18 (31.58)	0.109
Days of hospitalization after VAP event (median, IQR)	12.0 (6.0-21.0)	12.0 (6.0-21.0)	12.0 (7.0-23.0)	0.980
Post-VAP hospital stays over 30 days - N (%)	28.0 (21.71) *	21 (23.08)	7 (18.42)	0.725
In-hospital mortality - N (%)	68 (34.51)	49 (35.00)	19 (33.33)	0.869
Isolated organisms - N (%)
Acinetobacter baumannii	71 (36.04)	43 (30.71)	28 (49.12)	0.021
Burkholderia gladioli	1 (0.51)	0 (0.00)	1 (1.75)	0.289
Enterobacter aerogenes	1 (0.51)	1 (0.71)	0 (0.00)	1
Enterobacter cloacae complex	10 (5.08)	8 (5.71)	2 (3.51)	0.726
Escherichia coli	2 (1.02)	2 (1.43)	0 (0.00)	1
Hafnia alvei	1 (0.51)	1 (0.71)	0 (0.00)	1
Klebsiella oxytoca	2 (1.02)	1 (0.71)	1 (1.75)	0.496
Klebsiella pneumoniae	32 (16.24)	25 (17.86)	7 (12.28)	0.399
Proteus mirabilis	1 (0.51)	1 (0.71)	0 (0.00)	1
Providencia stuartii	1 (0.51)	1 (0.71)	0 (0.00)	1
Pseudomonas aeruginosa	36 (18.27)	28 (20.00)	8 (14.04)	0.417
Serratia marcescens	3 (1.52)	3 (2.14)	0 (0.00)	0.558
Staphylococcus aureus	20 (10.15)	13 (9.29)	7 (12.28)	0.603
Stenotrophomonas maltophilia	16 (8.12)	13 (9.29)	3 (5.26)	0.565
SOFA, Sequential Organ Failure Assessment score. ARDS, Acute Respiratory Distress Syndrome. SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus 2. VAP, Ventilator-Associated Pneumonia. IMV, Invasive Mechanical Ventilation. Bold values are for statistically significant variables. * Calculated based on surviving patients

Table 2. Multivariate analysis regarding in-hospital mortality and extended hospital stays.

In-hospital mortality
Variable	OR	CI 95%	p value
Days of IMV Post-VAP development	0.88	0.81-0.94	0.001
Initial Inappropriate Empiric Treatment	0.37	0.14-1.18	0.068
Inappropriate Treatment for VAP	4.37	1.22-19.11	0.024
Sequential Organ Failure Assessment (SOFA) score	1.55	1.22-1.96	<0.001
Need of Vasopressors	3.06	1.01-9.25	0.047
Acute Respiratory Distress Syndrome (ARDS)	5.92	2.13-16.41	0.001
Acinetobacter baumannii infection	2.76	1.06-7.13	0.036
Hosmer‒Lemeshow p value= 0.988 Pseudo R2 = 0.441
Extended Hospital Stays
Charlson Index	0.91	0.56-1.46	0.106
Days to Correct Treatment	0.95	0.46-1.53	0.895
Days from IMV to VAP development	1.13	0.76-1.68	0.241
Days of IMV after VAP development	1.52	1.05-2.23	0.028
Initial Inappropriate Empiric Treatment	4.40	1.37-14.12	0.043
Acute Respiratory Distress Syndrome (ARDS)	0.01	0.00-1.68	0.270
Acinetobacter baumannii infection	1.16	0.59-1.23	0.239
Hosmer‒Lemeshow p value= 0.925 Pseudo R²: 0.431

No competing interests reported.

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Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to Guide Empiric Antibiotic Treatment for Ventilator-Associated Pneumonia in a Mexican Tertiary Care University Hospital

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