Predicting Resistance to Piperacillin-Tazobactam, Cefepime and Meropenem in Septic Patients With Bloodstream Infection Due to Gram-Negative Bacteria

From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review

The emergence of drug-resistant bacteria poses a significant challenge to modern medicine. In response, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have emerged as powerful tools for combating antimicrobial resistance (AMR). This review aims to explore the role of AI/ML in AMR management, with a focus on identifying pathogens, understanding resistance patterns, predicting treatment outcomes, and discovering new antibiotic agents. Recent advancements in AI/ML have enabled the efficient analysis of large datasets, facilitating the reliable prediction of AMR trends and treatment responses with minimal human intervention. ML algorithms can analyze genomic data to identify genetic markers associated with antibiotic resistance, enabling the development of targeted treatment strategies. Additionally, AI/ML techniques show promise in optimizing drug administration and developing alternatives to traditional antibiotics. By analyzing patient data and clinical outcomes, these technologies can assist healthcare providers in diagnosing infections, evaluating their severity, and selecting appropriate antimicrobial therapies. While integration of AI/ML in clinical settings is still in its infancy, advancements in data quality and algorithm development suggest that widespread clinical adoption is forthcoming. In conclusion, AI/ML holds significant promise for improving AMR management and treatment outcome.

Machine learning-based antibiotic resistance prediction models: An updated systematic review and meta-analysis

BACKGROUND

The widespread use of antibiotics has led to a gradual adaptation of bacteria to these drugs, diminishing the effectiveness of treatments.

OBJECTIVE

To comprehensively assess the research progress of antibiotic resistance prediction models based on machine learning (ML) algorithms, providing the latest quantitative analysis and methodological evaluation.

METHODS

Relevant literature was systematically retrieved from databases, including PubMed, Embase and the Cochrane Library, from inception up to December 2023. Studies meeting predefined criteria were selected for inclusion. The prediction model risk of bias assessment tool was employed for methodological quality assessment, and a random-effects model was utilised for meta-analysis.

RESULTS

The systematic review included a total of 22 studies with a combined sample size of 43,628; 10 studies were ultimately included in the meta-analysis. Commonly used ML algorithms included random forest, decision trees and neural networks. Frequently utilised predictive variables encompassed demographics, drug use history and underlying diseases. The overall sensitivity was 0.57 (95% CI: 0.42-0.70; p< 0.001; I2= 99.7%), the specificity was 0.95 (95% CI: 0.79-0.99; p< 0.001; I2 = 99.9%), the positive likelihood ratio was 10.7 (95% CI: 2.9-39.5), the negative likelihood ratio was 0.46 (95% CI: 0.34-0.61), the diagnostic odds ratio was 23 (95% CI: 7-81) and the area under the receiver operating characteristic curve was 0.78 (95% CI: 0.74-0.81; p< 0.001), indicating a good discriminative ability of ML models for antibiotic resistance. However, methodological assessment and funnel plots suggested a high risk of bias and publication bias in the included studies.

CONCLUSION

This meta-analysis provides a current and comprehensive evaluation of ML models for predicting antibiotic resistance, emphasising their potential application in clinical practice. Nevertheless, stringent research design and reporting are warranted to enhance the quality and credibility of future studies. Future research should focus on methodological innovation and incorporate more high-quality studies to further advance this field.

Machine learning to predict antimicrobial resistance: future applications in clinical practice?

INTRODUCTION

Machine learning (ML) is increasingly being used to predict antimicrobial resistance (AMR). This review aims to provide physicians with an overview of the literature on ML as a means of AMR prediction.

METHODS

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, ACM Digital Library, and IEEE Xplore Digital Library up to December 2023.

RESULTS

Thirty-six studies were included in this review. Thirty-two studies (32/36, 89 %) were based on hospital data and four (4/36, 11 %) on outpatient data. The vast majority of them were conducted in high-resource settings (33/36, 92 %). Twenty-four (24/36, 67 %) studies developed systems to predict drug resistance in infected patients, eight (8/36, 22 %) tested the performances of ML-assisted antibiotic prescription, two (2/36, 6 %) assessed ML performances in predicting colonization with carbapenem-resistant bacteria and, finally, two assessed national and international AMR trends. The most common inputs were demographic characteristics (25/36, 70 %), previous antibiotic susceptibility testing (19/36, 53 %) and prior antibiotic exposure (15/36, 42 %). Thirty-three (92 %) studies targeted prediction of Gram-negative bacteria (GNB) resistance as an output (92 %). The studies included showed moderate to high performances, with AUROC ranging from 0.56 to 0.93.

CONCLUSION

ML can potentially provide valuable assistance in AMR prediction. Although the literature on this topic is growing, future studies are needed to design, implement, and evaluate the use and impact of ML decision support systems.

State of the Management of Infections Caused by Multidrug-Resistant Gram-Negative Organisms

In the past decade, the prevalence of multidrug-resistant gram-negative (MDR-GN) bacterial infections has increased significantly, leading to higher rates of morbidity and mortality. Treating these infections poses numerous challenges, particularly when selecting appropriate empiric therapy for critically ill patients for whom the margin for error is low. Fortunately, the availability of new therapies has improved the treatment landscape, offering safer and more effective options. However, there remains a need to establish and implement optimal clinical and therapeutic approaches for managing these infections. Here, we review strategies for identifying patients at risk for MDR-GN infections, propose a framework for the choice of empiric and definitive treatment, and explore effective multidisciplinary approaches to managing patients in the hospital while ensuring a safe transition to outpatient settings.

Machine learning model for predicting ciprofloxacin resistance and presence of ESBL in patients with UTI in the ED

Increasing antimicrobial resistance in uropathogens is a clinical challenge to emergency physicians as antibiotics should be selected before an infecting pathogen or its antibiotic resistance profile is confirmed. We created a predictive model for antibiotic resistance of uropathogens, using machine learning (ML) algorithms. This single-center retrospective study evaluated patients diagnosed with urinary tract infection (UTI) in the emergency department (ED) between January 2020 and June 2021. Thirty-nine variables were used to train the model to predict resistance to ciprofloxacin and the presence of urinary pathogens' extended-spectrum beta-lactamases. The model was built with Gradient-Boosted Decision Tree (GBDT) with performance evaluation. Also, we visualized feature importance using SHapely Additive exPlanations. After two-step customization of threshold adjustment and feature selection, the final model was compared with that of the original prescribers in the emergency department (ED) according to the ineffectiveness of the antibiotic selected. The probability of using ineffective antibiotics in the ED was significantly lowered by 20% in our GBDT model through customization of the decision threshold. Moreover, we could narrow the number of predictors down to twenty and five variables with high importance while maintaining similar model performance. An ML model is potentially useful for predicting antibiotic resistance improving the effectiveness of empirical antimicrobial treatment in patients with UTI in the ED. The model could be a point-of-care decision support tool to guide clinicians toward individualized antibiotic prescriptions.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem. Both supervised and unsupervised machine learning tools have been successfully used to predict early antibiotic resistance, and thus support clinicians in selecting appropriate therapy. In this paper, we reviewed the existing literature on machine learning and artificial intelligence (AI) in general in conjunction with antimicrobial resistance prediction. This is a narrative review, where we discuss the applications of ML methods in the field of AMR and their value as a complementary tool in the antibiotic stewardship practice, mainly from the clinician's point of view.

Rapid Detection of Piperacillin-Tazobactam Resistance in Klebsiella pneumoniae and Escherichia coli

Rapid determination of susceptibility to piperacillin-tazobactam (TZP) is very important since the development of antibiotic resistance and inadequate treatment could increase the risk of clinical failure in infected patients, especially if such resistance is unknown to the clinician. Therefore, based on color change from orange to yellow of phenol red due to glucose metabolism (bacterial growth) in the presence of an adequate concentration of TZP (10 mg/L piperacillin and 5 mg/L tazobactam), the RapidTZP test has been developed to detect TZP resistance in Escherichia coli and Klebsiella pneumoniae isolates in a maximum of 3 h. A total of 140 isolates, 43 of E. coli and 97 of K. pneumoniae, were used to evaluate the performance of the test, 60 being resistant to TZP. The sensitivity and specificity of the test were 98.24% and 100%, respectively. Additionally, the RapidTZP test was validated by a pellet obtained directly from blood culture bottles. A total of 37 positive blood cultures for E. coli and 43 for K. pneumoniae were used for validation, 8 of them resistant to TZP. The sensitivity and specificity shown in the evaluation were 100% for both parameters. This new test is easy, fast, and accurate, providing results in 3 h. IMPORTANCE TZP is an antibiotic widely used for the empirical treatment of severe infections such as bloodstream infections. However, resistance to TZP in K. pneumoniae and E. coli has been increasing in the last few years. Thus, rapid detection of TZP resistance is critical to optimize the empirical treatment of patients with severe infections. In this study, we developed and evaluated a rapid test (RapidTZP) for the detection of TZP resistance in K. pneumoniae and E. coli directly from positive hemocultures in just 3 h. This rapid test has been validated on 138 K. pneumoniae and E. coli clinical isolates directly from agar plates and 80 K. pneumoniae and E. coli isolates causing bloodstream infections. The results demonstrate that the RapidTZP test has great clinical potential to optimize the empirical treatment of patients with bloodstream infections.

Effects of empiric antibiotic treatment based on hospital cumulative antibiograms in patients with bacteraemic sepsis: a retrospective cohort study

OBJECTIVES

To assess the effects of empiric antibiotics with different degrees of appropriateness based on hospital cumulative antibiograms in patients with bacteraemic sepsis presenting to the emergency department (ED).

METHODS

This retrospective cohort study included adult patients with sepsis and positive blood culture reports in the ED from February 2016 to December 2018. Based on isolated pathogens and empiric antibiotics which the patients received, these patients were divided into two groups using a cut-off of 70% for overall antimicrobial susceptibility (OAS) on hospital cumulative antibiograms 6 months prior to ED admission. Multivariate regression and sensitivity analyses were performed.

RESULTS

In this study, 1055 patients were included. We used multivariate regression models which were adjusted for age, sex, co-morbidities, site of infection, organ dysfunction, and septic shock. Empiric antibiotics with OAS of ≥70% were associated with reduced in-hospital deaths (adjusted odds ratio, 0.46; 95% CI, 0.28-0.77) and 30-day mortality (adjusted odds ratio, 0.53; 95% CI, 0.33-0.86). They were more likely to result in a shortened length of intensive care unit stay by 1.60 days (95% CI, -3.00 to -0.20).

CONCLUSIONS

Treatment with empiric antibiotics with OAS of ≥70% based on hospital cumulative antibiograms is associated with lower mortality and shorter length of intensive care unit stay in patients with bacteraemic sepsis in the ED.

Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis

INTRODUCTION

Antimicrobial resistance (AMR) is a global health threat; rapid and timely identification of AMR improves patient prognosis and reduces inappropriate antibiotic use.

METHODS

Relevant literature in PubMed, Web of Science, Embase and Institute of Electrical and Electronics Engineers prior to 28 September 2021 was searched. Any study that deployed machine learning (ML) or a risk score as a tool to predict AMR was included in the final review; there were 25 studies that employed the ML algorithm to predict AMR.

RESULTS

Extended spectrum β-lactamases, methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem resistance were the most common outcomes in studies with a specific resistance pattern. The most common algorithms in ML prediction were logistic regression (n = 14 studies), decision tree (n = 14) and random forest (n = 7). The area under the curve (AUC) range for ML prediction was 0.48-0.93. The pooled AUC for ML prediction was 0.82 (0.78-0.85). Compared with risk score, higher specificity [87% (82-91) vs. 37% (25-51)] was indicated for ML prediction, but not sensitivity [67% (62-72) vs. 73% (67-79)].

CONCLUSIONS

Machine learning might be a potential technology for AMR prediction; however, retrospective methodology for model development, nonstandard data processing and scarcity of validation in a randomised controlled trial or real-world study limit the application of these models in clinical practice.

What are the risk factors for recurrent UTI with repeated ESBL-producing Enterobacteriaceae? A retrospective cohort study

INTRODUCTION

A previous study has shown that two-thirds of patients with urinary tract infections (UTIs) caused by extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae experience recurrence with the same bacteria on subsequent UTI episodes. However, little is known about which patients suffer from UTI due to ESBL-producing Enterobacteriaceae repeatedly. This study aimed to investigate the risk factors for recurrent UTI due to repeated ESBL-producing organism infections.

METHODS

This retrospective, single-center, observational cohort study screened all patients with UTI caused by ESBL-producing strains between January 2012 and April 2019. Among the patients who were followed up, patients who experienced UTI recurrence were enrolled and divided into two groups: ESBL recurrence group and non-ESBL recurrence group. Multivariable Cox proportional hazards regression analyses were performed to evaluate the association between patient characteristics and the development of recurrent UTI caused by ESBL-producing Enterobacteriaceae.

RESULTS

A total of 330 patients were followed up after the diagnosis of UTI caused by ESBL-producing organisms. Among the patients, 115 (34.8%) experienced UTI recurrence, and 71 (61.7%) of them experienced subsequent recurrent UTI due to ESBL-producing organisms. Patient's age (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.00-1.04; P = 0.046) and recurrent UTI history (HR, 1.69; 95% CI, 1.05-2.72; P = 0.031) were significantly associated with an increased risk of recurrence with ESBL-producing Enterobacteriaceae.

CONCLUSION

These findings showed that a history of previous frequent UTI recurrence is the risk factor for recurrence of UTI due to repeated ESBL producing Enterobacteriaceae infections.

Bloodstream Infections Caused by Carbapenem-Resistant Pathogens in Intensive Care Units: Risk Factors Analysis and Proposal of a Prognostic Score

Considering the growing prevalence of carbapenem-resistant Gram-negative bacteria (CR-GNB) bloodstream infection (BSI) in intensive care units (ICUs), the identification of specific risk factors and the development of a predictive model allowing for the early identification of patients at risk for CR-Klebsiella pneumoniae, Acinetobacter baumannii or Pseudomonas aeruginosa are essential. In this retrospective case–control study including all consecutive patients showing an episode of BSI in the ICUs of a university hospital in Italy in the period January–December 2016, patients with blood culture positive for CR-GNB pathogens and for any other bacteria were compared. A total of 106 patients and 158 episodes of BSI were identified. CR-GNBs induced BSI in 49 patients (46%) and 58 episodes (37%). Prognosis score and disease severity at admission, parenteral nutrition, cardiovascular surgery prior to admission to ICU, the presence of sepsis and septic shock, ventilation-associated pneumonia and colonization of the urinary or intestinal tract were statistically significant in the univariate analysis. The duration of ventilation and mortality at 28 days were significantly higher among CR-GNB cases. The prognostic model based on age, presence of sepsis, previous cardiovascular surgery, SAPS II, rectal colonization and invasive respiratory infection from the same pathogen showed a C-index of 89.6%. The identified risk factors are in line with the international literature. The proposal prognostic model seems easy to use and shows excellent performance but requires further studies to be validated.

Antibiotic Decision-Making in the ICU

It is well established that Intensive Care Units (ICUs) are a focal point in antimicrobial consumption with a major influence on the ecological consequences of antibiotic use. With the high prevalence and mortality of infections in critically ill patients, and the clinical challenges of treating patients with septic shock, the impact of real life clinical decisions made by intensivists becomes more significant. Both under- and over-treatment with unnecessarily broad spectrum antibiotics can lead to detrimental outcomes. Even though substantial progress has been made in developing rapid diagnostic tests that can help guide antibiotic use, there is still a time window when clinicians must decide the empiric antibiotic treatment with insufficient clinical data. The continuous streams of data available in the ICU environment make antimicrobial optimization an ongoing challenge for clinicians but at the same time can serve as the input for sophisticated models. In this review, we summarize the evidence to help guide antibiotic decision-making in the ICU. We focus on 1) deciding IF: to start antibiotics, 2) choosing the spectrum of the empiric agents to use, and 3) de-escalating the chosen empiric antibiotics. We provide a perspective on the role of machine learning and artificial intelligence models for clinical decision support systems that can be incorporated seamlessly into clinical practice in order to improve the antibiotic selection process and, more importantly, current and future patients' outcomes.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Semirapid Detection of Piperacillin/Tazobactam Resistance and Extended-Spectrum Resistance to β-Lactams/β-Lactamase Inhibitors in Clinical Isolates of Escherichia coli

Piperacillin/tazobactam (TZP) is a β-lactam/β-lactamase inhibitor (BL/BLI) recommended for the empirical treatment of severe infections. The excessive and indiscriminate use of TZP has promoted the emergence of TZP-resistant Escherichia coli isolates. Recently, we demonstrated that TZP may contribute to the development of extended-spectrum resistance to BL/BLI (ESRI) in E. coli isolates that are TZP susceptible but have low-level resistance to BL/BLI (resistance to amoxicillin/clavulanic acid [AMC] and/or ampicillin/sulbactam [SAM]). This raises the need for the development of rapid detection systems. Therefore, the objective of this study was to design and validate a method able to detect TZP resistance and ESRI in E. coli. A colorimetric assay based on β-lactam ring hydrolysis by β-lactamases was designed (ESRI test). A total of 114 E. coli isolates from bloodstream and intra-abdominal sources, characterized according to their susceptibility profiles to BL/BLI, were used. Detection of the three most frequent β-lactamases involved in BL/BLI resistance (blaTEM, blaOXA-1, and blaSHV) was performed by PCR. The ESRI test was able to detect all the TZP-intermediate/-resistant isolates, as well as all the TZP-susceptible isolates with a capacity for ESRI development. Their median times to results were 5 and 30 min, respectively. All the isolates without resistance to BL/BLI displayed a negative result in the ESRI test. blaTEM was the most frequent β-lactamase gene detected, follow by blaSHV and blaOXA-1. These results demonstrate the efficacy of the ESRI test, showing great clinical potential which could lead to reductions in health costs, ineffective treatments, and inappropriate use of BL/BLI. IMPORTANCE TZP is a BL/BLI recommended for the empirical treatment of severe infections. The excessive use of TZP has promoted the emergence of TZP-resistant Escherichia coli isolates. We recently reported that TZP may contribute to the development of ESRI in E. coli isolates that are TZP susceptible but have low-level resistance to BL/BLI. This raises the need for the development of rapid detection systems. Here, we demonstrated that the ESRI test was able to detect the TZP-intermediate or -resistant isolates and the TZP-susceptible isolates with the capacity for ESRI development. All the isolates without BL/BLI resistance were negative for the ESRI test and did not harbor β-lactamase genes. For ESRI developers and TZP-intermediate or -resistant isolates, blaTEM was the most frequent β-lactamase gene detected, follow by blaSHV and blaOXA-1. The sensitivity, specificity, and positive and negative predictive values were all 100%. These data demonstrate the efficacy of the ESRI test and show that it has great clinical potential.

Assessment of a PK/PD Target of Continuous Infusion Beta-Lactams Useful for Preventing Microbiological Failure and/or Resistance Development in Critically Ill Patients Affected by Documented Gram-Negative Infections

BACKGROUND

Emerging data suggest that more aggressive beta-lactam PK/PD targets could minimize the occurrence of microbiological failure and/or resistance development. This study aims to assess whether a PK/PD target threshold of continuous infusion (CI) beta-lactams may be useful in preventing microbiological failure and/or resistance development in critically ill patients affected by documented Gram-negative infections.

METHODS

Patients admitted to intensive care units from December 2020 to July 2021 receiving continuous infusion beta-lactams for documented Gram-negative infections and having at least one therapeutic drug monitoring in the first 72 h of treatment were included. A receiver operating characteristic (ROC) curve analysis was performed using the ratio between steady-state concentration and minimum inhibitory concentration (C_ss/MIC) ratio as the test variable and occurrence of microbiological failure as the state variable. Area under the curve (AUC) and 95% confidence interval (CI) were calculated. Independent risk factors for the occurrence of microbiological failure were investigated using logistic regression.

RESULTS

Overall, 116 patients were included. Microbiological failure occurred in 26 cases (22.4%). A C_ss/MIC ratio ≤ 5 was identified as PK/PD target cut-off with sensitivity of 80.8% (CI 60.6-93.4%) and specificity of 90.5% (CI 74.2-94.4%), and with an AUC of 0.868 (95%CI 0.793-0.924; p < 0.001). At multivariate regression, independent predictors of microbiological failure were C_ss/MIC ratio ≤ 5 (odds ratio [OR] 34.54; 95%CI 7.45-160.11; p < 0.001) and Pseudomonas aeruginosa infection (OR 4.79; 95%CI 1.11-20.79; p = 0.036).

CONCLUSIONS

Early targeting of CI beta-lactams at C_ss/MIC ratio > 5 during the treatment of documented Gram-negative infections may be helpful in preventing microbiological failure and/or resistance development in critically ill patients.

Towards personalized guidelines: using machine-learning algorithms to guide antimicrobial selection

BACKGROUND

Electronic decision support systems could reduce the use of inappropriate or ineffective empirical antibiotics. We assessed the accuracy of an open-source machine-learning algorithm trained in predicting antibiotic resistance for three Gram-negative bacterial species isolated from patients' blood and urine within 48 h of hospital admission.

METHODS

This retrospective, observational study used routine clinical information collected between January 2010 and October 2016 in Birmingham, UK. Patients from whose blood or urine cultures Escherichia coli, Klebsiella pneumoniae or Pseudomonas aeruginosa was isolated were identified. Their demographic, microbiology and prescribing data were used to train an open-source machine-learning algorithm-XGBoost-in predicting resistance to co-amoxiclav and piperacillin/tazobactam. Multivariate analysis was performed to identify predictors of resistance and create a point-scoring tool. The performance of both methods was compared with that of the original prescribers.

RESULTS

There were 15 695 admissions. The AUC of the receiver operating characteristic curve for the point-scoring tools ranged from 0.61 to 0.67, and performed no better than medical staff in the selection of appropriate antibiotics. The machine-learning system performed statistically but marginally better (AUC 0.70) and could have reduced the use of unnecessary broad-spectrum antibiotics by as much as 40% among those given co-amoxiclav, piperacillin/tazobactam or carbapenems. A validation study is required.

CONCLUSIONS

Machine-learning algorithms have the potential to help clinicians predict antimicrobial resistance in patients found to have a Gram-negative infection of blood or urine. Prospective studies are required to assess performance in an unselected patient cohort, understand the acceptability of such systems to clinicians and patients, and assess the impact on patient outcome.

Clinical Profile, Prognostic Factors, and Outcome Prediction in Hospitalized Patients With Bloodstream Infection: Results From a 10-Year Prospective Multicenter Study

Background: Bloodstream infection (BSI) is one of the most common serious bacterial infections worldwide and also a major contributor to in-hospital mortality. Determining the predictors of mortality is crucial for prevention and improving clinical prognosis in patients with nosocomial BSI. Methods: A nationwide prospective cohort study was conducted from 2007 until 2016 in 16 teaching hospitals across China. Microbiological results, clinical information, and patient outcomes were collected to investigate the pathogenic spectrum and mortality rate in patients with BSI and identify outcome predictors using multivariate regression, prediction model, and Kaplan-Meier analysis. Results: No significant change was observed in the causative pathogen distribution during the 10-year period and the overall in-hospital mortality was 12.83% (480/3,741). An increased trend was found in the mortality of patients infected with Pseudomonas aeruginosa or Acinetobacter baumannii, while a decreased mortality rate was noted in Staphylococcus aureus-related BSI. In multivariable-adjusted models, higher mortality rate was significantly associated with older age, cancer, sepsis diagnosis, ICU admission, and prolonged hospital stay prior to BSI onset, which were also determined using machine learning-based predictive model achieved by random forest algorithm with a satisfactory performance in outcome prediction. Conclusions: Our study described the clinical and microbiological characteristics and mortality predictive factors in patients with BSI. These informative predictors would inform clinical practice to adopt effective therapeutic strategies to improve patient outcomes.

Impact of Baseline Characteristics on Future Episodes of Bloodstream Infections: Multistate Model in Septic Patients With Bloodstream Infections

BACKGROUND

Looking only at the index infection, studies have described risk factors for infections caused by resistant bacteria. We hypothesized that septic patients with bloodstream infections may transition across states characterized by different microbiology and that their trajectory is not uniform. We also hypothesized that baseline risk factors may influence subsequent blood culture results.

METHODS

All adult septic patients with positive blood cultures over a 7-year period were included in the study. Baseline risk factors were recorded. We followed all survivors longitudinally and recorded subsequent blood culture results. We separated states into bacteremia caused by gram-positive cocci, susceptible gram-negative bacilli (sGNB), resistant GNB (rGNB), and Candida spp. Detrimental transitions were considered when transitioning to a culture with a higher mortality risk (rGNB and Candida spp.). A multistate Markov-like model was used to determine risk factors associated with detrimental transitions.

RESULTS

A total of 990 patients survived and experienced at least 1 transition, with a total of 4282 transitions. Inappropriate antibiotics, previous antibiotic exposure, and index bloodstream infection caused by either rGNB or Candida spp. were associated with detrimental transitions. Double antibiotic therapy (beta-lactam plus either an aminoglycoside or a fluoroquinolone) protected against detrimental transitions.

CONCLUSION

Baseline characteristics that include prescribed antibiotics can identify patients at risk for subsequent bloodstream infections caused by resistant bacteria. By altering the initial treatment, we could potentially influence future bacteremic states.

Extended-spectrum resistance to β-lactams/β-lactamase inhibitors (ESRI) evolved from low-level resistant Escherichia coli

OBJECTIVES

Escherichia coli is characterized by three resistance patterns to β-lactams/β-lactamase inhibitors (BLs/BLIs): (i) resistance to ampicillin/sulbactam and susceptibility to amoxicillin/clavulanic acid and piperacillin/tazobactam (RSS); (ii) resistance to ampicillin/sulbactam and amoxicillin/clavulanic acid, and susceptibility to piperacillin/tazobactam (RRS); and (iii) resistance to ampicillin/sulbactam, amoxicillin/clavulanic acid and piperacillin/tazobactam (RRR). These resistance patterns are acquired consecutively, indicating a potential risk of developing resistance to piperacillin/tazobactam, but the precise mechanism of this process is not completely understood.

METHODS

Clinical isolates incrementally pressured by piperacillin/tazobactam selection in vitro and in vivo were used. We determined the MIC of piperacillin/tazobactam in the presence and absence of piperacillin/tazobactam pressure. We deciphered the role of the blaTEM genes in the new concept of extended-spectrum resistance to BLs/BLIs (ESRI) using genomic analysis. The activity of β-lactamase was quantified in these isolates.

RESULTS

We show that piperacillin/tazobactam resistance is induced in E. coli carrying blaTEM genes. This resistance is due to the increase in copy numbers and transcription levels of the blaTEM gene, thus increasing β-lactamase activity and consequently increasing piperacillin/tazobactam MICs. Genome sequencing of two blaTEM-carrying representative isolates showed that piperacillin/tazobactam treatment produced two types of duplications of blaTEM (8 and 60 copies, respectively). In the clinical setting, piperacillin/tazobactam treatment of patients infected by E. coli carrying blaTEM is associated with a risk of therapeutic failure.

CONCLUSIONS

This study describes for the first time the ESRI in E. coli. This new concept is very important in the understanding of the mechanism involved in the acquisition of resistance to BLs/BLIs.

A "resistance calculator": Simple stewardship intervention for refining empiric practices of antimicrobials in acute-care hospitals

Objective:

In the era of widespread resistance, there are 2 time points at which most empiric prescription errors occur among hospitalized adults: (1) upon admission (UA) when treating patients at risk of multidrug-resistant organisms (MDROs) and (2) during hospitalization, when treating patients at risk of extensively drug-resistant organisms (XDROs). These errors adversely influence patient outcomes and the hospital’s ecology.

Design and setting:

Retrospective cohort study, Shamir Medical Center, Israel, 2016.

Patients:

Adult patients (aged >18 years) hospitalized with sepsis.

Methods:

Logistic regressions were used to develop predictive models for (1) MDRO UA and (2) nosocomial XDRO. Their performances on the derivation data sets, and on 7 other validation data sets, were assessed using the area under the receiver operating characteristic curve (ROC AUC).

Results:

In total, 4,114 patients were included: 2,472 patients with sepsis UA and 1,642 with nosocomial sepsis. The MDRO UA score included 10 parameters, and with a cutoff of ≥22 points, it had an ROC AUC of 0.85. The nosocomial XDRO score included 7 parameters, and with a cutoff of ≥36 points, it had an ROC AUC of 0.87. The range of ROC AUCs for the validation data sets was 0.7–0.88 for the MDRO UA score and was 0.66–0.75 for nosocomial XDRO score. We created a free web calculator (https://assafharofe.azurewebsites.net).

Conclusions:

A simple electronic calculator could aid with empiric prescription during an encounter with a septic patient. Future implementation studies are needed to evaluate its utility in improving patient outcomes and in reducing overall resistances.

Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records

BACKGROUND

Computerized decision support systems are becoming increasingly prevalent with advances in data collection and machine learning algorithms. However, they are scarcely used for empiric antibiotic therapy. Here we accurately predict the antibiotic resistance profiles of bacterial infections of hospitalized patients using machine learning algorithms applied to patients' electronic medical records (EMR).

METHODS

The data included antibiotic resistance results of bacterial cultures from hospitalized patients, alongside their electronic medical records. Five antibiotics were examined: Ceftazidime (n=2942), Gentamicin (n=4360), Imipenem (n=2235), Ofloxacin (n=3117) and Sulfamethoxazole-Trimethoprim (n=3544). We applied lasso logistic regression, neural networks, gradient boosted trees, and an ensemble combining all three algorithms, to predict antibiotic resistance. Variable influence was gauged by permutation tests and Shapely Additive Explanations analysis.

RESULTS

The ensemble model outperformed the separate models and produced accurate predictions on a test set data. When no knowledge regarding the infecting bacterial species was assumed, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73-0.79, for different antibiotics. Including information regarding the bacterial species improved the auROCs to 0.8-0.88. The effects of different variables on the predictions were assessed and found consistent with previously identified risk factors for antibiotic resistance.

CONCLUSIONS

Our study demonstrates the potential of machine learning models to accurately predict antibiotic resistance of bacterial infections of hospitalized patients. Moreover, we show that rapid information regarding the infecting bacterial species can improve predictions substantially. The implementation of such systems should be seriously considered by clinicians to aid correct empiric therapy and to potentially reduce antibiotic misuse.

Prevalence of Antibiotic-Resistant Pathogens in Culture-Proven Sepsis and Outcomes Associated With Inadequate and Broad-Spectrum Empiric Antibiotic Use

IMPORTANCE

Broad-spectrum antibiotics are recommended for all patients with suspected sepsis to minimize the risk of undertreatment. However, little is known regarding the net prevalence of antibiotic-resistant pathogens across all patients with community-onset sepsis or the outcomes associated with unnecessarily broad empiric treatment.

OBJECTIVE

To elucidate the epidemiology of antibiotic-resistant pathogens and the outcomes associated with both undertreatment and overtreatment in patients with culture-positive community-onset sepsis.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 17 430 adults admitted to 104 US hospitals between January 2009 and December 2015 with sepsis and positive clinical cultures within 2 days of admission. Data analysis took place from January 2018 to December 2019.

EXPOSURES

Inadequate empiric antibiotic therapy (ie, ≥1 pathogen nonsusceptible to all antibiotics administered on the first or second day of treatment) and unnecessarily broad empiric therapy (ie, active against methicillin-resistant Staphylococcus aureus [MRSA]; vancomycin-resistant Enterococcus [VRE]; ceftriaxone-resistant gram-negative [CTX-RO] organisms, including Pseudomonas aeruginosa; or extended-spectrum β-lactamase [ESBL] gram-negative organisms when none of these were isolated).

MAIN OUTCOMES AND MEASURES

Prevalence and empiric treatment rates for antibiotic-resistant organisms and associations of inadequate and unnecessarily broad empiric therapy with in-hospital mortality were assessed, adjusting for baseline characteristics and severity of illness.

RESULTS

Of 17 430 patients with culture-positive community-onset sepsis (median [interquartile range] age, 69 [57-81] years; 9737 [55.9%] women), 2865 (16.4%) died in the hospital. The most common culture-positive sites were urine (9077 [52.1%]), blood (6968 [40.0%]), and the respiratory tract (2912 [16.7%]). The most common pathogens were Escherichia coli (5873 [33.7%]), S aureus (3706 [21.3%]), and Streptococcus species (2361 [13.5%]). Among 15 183 cases in which all antibiotic-pathogen susceptibility combinations could be calculated, most (12 398 [81.6%]) received adequate empiric antibiotics. Empiric therapy targeted resistant organisms in 11 683 of 17 430 cases (67.0%; primarily vancomycin and anti-Pseudomonal β-lactams), but resistant organisms were uncommon (MRSA, 2045 [11.7%]; CTX-RO, 2278 [13.1%]; VRE, 360 [2.1%]; ESBLs, 133 [0.8%]). The net prevalence for at least 1 resistant gram-positive organism (ie, MRSA or VRE) was 13.6% (2376 patients), and for at least 1 resistant gram-negative organism (ie, CTX-RO, ESBL, or CRE), it was 13.2% (2297 patients). Both inadequate and unnecessarily broad empiric antibiotics were associated with higher mortality after detailed risk adjustment (inadequate empiric antibiotics: odds ratio, 1.19; 95% CI, 1.03-1.37; P = .02; unnecessarily broad empiric antibiotics: odds ratio, 1.22; 95% CI, 1.06-1.40; P = .007).

CONCLUSIONS AND RELEVANCE

In this study, most patients with community-onset sepsis did not have resistant pathogens, yet broad-spectrum antibiotics were frequently administered. Both inadequate and unnecessarily broad empiric antibiotics were associated with higher mortality. These findings underscore the need for better tests to rapidly identify patients with resistant pathogens and for more judicious use of broad-spectrum antibiotics for empiric sepsis treatment.

Microbial cause of ICU-acquired pneumonia: hospital-acquired pneumonia versus ventilator-associated pneumonia

PURPOSE OF REVIEW

Successful treatment of patients with hospital-acquired pneumonia (HAP) and ventilator-associated pneumonia (VAP) remains a difficult and complex undertaking. Better knowledge of the pathogens involved in that setting may allow reassessment of our current modalities of therapy and definition of better protocols.

RECENT FINDINGS

Microorganisms responsible for HAP/VAP differ according to geographic areas, ICU patients' specific characteristics, durations of hospital and ICU stays before onset of the disease, and risk factors for MDR pathogens. However, a number of studies have shown that Gram-negative bacilli (GNB) - particularly Pseudomonas aeruginosa and Enterobacteriaceae - cause many of the respiratory infections in this setting, with minimal differences between HAP and VAP, indicating that the cause depends more on the underlying clinical condition of patients rather than previous intubation.

SUMMARY

When selecting initial antimicrobial therapy in patients with HAP/VAP, more attention should be paid to individual risk factors for MDR pathogens, severity of the clinical situation, and the local epidemiology than to the type of pneumonia.

A Decision Tree Using Patient Characteristics to Predict Resistance to Commonly Used Broad-Spectrum Antibiotics in Children With Gram-Negative Bloodstream Infections

BACKGROUND

As rates of multidrug-resistant gram-negative infections rise, it is critical to recognize children at high risk of bloodstream infections with organisms resistant to commonly used empiric broad-spectrum antibiotics. The objective of the current study was to develop a user-friendly clinical decision aid to predict the risk of resistance to commonly prescribed broad-spectrum empiric antibiotics for children with gram-negative bloodstream infections.

METHODS

This was a longitudinal retrospective cohort study of children with gram-negative bacteria cared for at a tertiary care pediatric hospital from June 2009 to June 2015. The primary outcome was a bloodstream infection due to bacteria resistant to broad-spectrum antibiotics (ie, cefepime, piperacillin-tazobactam, meropenem, or imipenem-cilastatin). Recursive partitioning was used to develop the decision tree.

RESULTS

Of 689 episodes of gram-negative bloodstream infections included, 31% were resistant to broad-spectrum antibiotics. The decision tree stratified patients into high- or low-risk groups based on prior carbapenem treatment, a previous culture with a broad-spectrum antibiotic resistant gram-negative organism in the preceding 6 months, intestinal transplantation, age ≥3 years, and ≥7 prior episodes of gram-negative bloodstream infections. The sensitivity for classifying high-risk patients was 46%, and the specificity was 91%.

CONCLUSION

A decision tree offers a novel approach to individualize patients' risk of gram-negative bloodstream infections resistant to broad-spectrum antibiotics, distinguishing children who may warrant even broader antibiotic therapy (eg, combination therapy, newer β-lactam agents) from those for whom standard empiric antibiotic therapy is appropriate. The constructed tree needs to be validated more widely before incorporation into clinical practice.

MicroRNA-494-3p protects rat cardiomyocytes against septic shock via PTEN

The aim of the present study was to investigate the role of microRNA (miR)-494-3p in myocardial injury in patients with septic shock and the underlying mechanism. A total of 22 patients with sepsis and 17 patients with septic shock were included in the present study. In addition, 20 healthy subjects were recruited as the control group. Peripheral blood was collected from all subjects and a rat cardiomyocyte model of myocardial injury was constructed. Reverse transcription-quantitative polymerase chain reaction was used to measure miR-494-3p expression, while cell counting kit-8 assays were performed to assess cell proliferation. Flow cytometry was performed to investigate cell cycle distribution and apoptosis. Lactate dehydrogenase (LDH) assays were performed to measure LDH levels. ELISA was also performed to measure LDH, tumor necrosis factor (TNF)-α and interleukin (IL)-6 levels in cell culture supernatants. Western blotting was employed to detect phosphatase and tensin homolog (PTEN) protein expression and dual luciferase reporter assays were performed to identify the interaction between miR-494-3p and PTEN mRNA. Reduced miR-494-3p expression was correlated with myocardial damage in patients with septic shock. Sera from patients with septic shock downregulated miR-494-3p expression in rat cardiomyocytes. miR-494-3p overexpression inhibited rat cardiomyocyte injury induced by treatment with sera from patients with septic shock. Furthermore, miR-494-3p overexpression reduced the synthesis and release of TNF-α and IL-6 from rat cardiomyocytes. PTEN knockdown alleviated rat cardiomyocyte injury following treatment with serum from patients with septic shock. PTEN was demonstrated to induce the release of TNF-α and IL-6 from rat cardiomyocytes treated with septic shock serum, while miR-494-3p was demonstrated to bind to the 3′-untranslated seed region of PTEN mRNA to regulate its expression. The results of the present study suggest that miR-494-3p is downregulated in the peripheral blood of patients with septic shock and is negatively correlated with myocardial injury. The present study also indicates that miR-494-3p regulates PTEN expression, inhibits sepsis-induced myocardial injury and protects the function of cardiomyocytes. The protective effect and mechanism of action of miR-494-3p indicate that it has potential for use in the clinical diagnosis and therapy of myocardial damage.

Risk factors and prognosis of complicated urinary tract infections caused by Pseudomonas aeruginosa in hospitalized patients: a retrospective multicenter cohort study

Purpose

Complicated urinary tract infections (cUTIs) are among the most frequent health-care-associated infections. In patients with cUTI, Pseudomonas aeruginosa deserves special attention, since it can affect patients with serious underlying conditions. Our aim was to gain insight into the risk factors and prognosis of P. aeruginosa cUTIs in a scenario of increasing multidrug resistance (MDR).

Methods

This was a multinational, retrospective, observational study at 20 hospitals in south and southeastern Europe, Turkey, and Israel including consecutive patients with cUTI hospitalized between January 2013 and December 2014. A mixed-effect logistic regression model was performed to assess risk factors for P. aeruginosa and MDR P. aeruginosa cUTI.

Results

Of 1,007 episodes of cUTI, 97 (9.6%) were due to P. aeruginosa. Resistance rates of P. aeruginosa were: antipseudomonal cephalosporins 35 of 97 (36.1%), aminoglycosides 30 of 97 (30.9%), piperacillin-tazobactam 21 of 97 (21.6%), fluoroquinolones 43 of 97 (44.3%), and carbapenems 28 of 97 (28.8%). The MDR rate was 28 of 97 (28.8%). Independent risk factors for P. aeruginosa cUTI were male sex (OR 2.61, 95% CI 1.60-4.27), steroid therapy (OR 2.40, 95% CI 1.10-5.27), bedridden functional status (OR 1.79, 95% CI 0.99-3.25), antibiotic treatment within the previous 30 days (OR 2.34, 95% CI 1.38-3.94), indwelling urinary catheter (OR 2.41, 95% CI 1.43-4.08), and procedures that anatomically modified the urinary tract (OR 2.01, 95% CI 1.04-3.87). Independent risk factors for MDR P. aeruginosa cUTI were age (OR 0.96, 95% CI 0.93-0.99) and anatomical urinary tract modification (OR 4.75, 95% CI 1.06-21.26). Readmission was higher in P. aeruginosa cUTI patients than in other etiologies (23 of 97 [23.7%] vs 144 of 910 [15.8%], P=0.04), while 30-day mortality was not significantly different (seven of 97 [7.2%] vs 77 of 910 [8.5%], P=0.6).

Conclusion

Patients with P. aeruginosa cUTI had characteristically a serious baseline condition and manipulation of the urinary tract, although their mortality was not higher than that of patients with cUTI caused by other etiologies.

Predictive factors for multidrug-resistant gram-negative bacteria among hospitalised patients with complicated urinary tract infections

Background

Patients with complicated urinary tract infections (cUTIs) frequently receive broad-spectrum antibiotics. We aimed to determine the prevalence and predictive factors of multidrug-resistant gram-negative bacteria in patients with cUTI.

Methods

This is a multicenter, retrospective cohort study in south and eastern Europe, Turkey and Israel including consecutive patients with cUTIs hospitalised between January 2013 and December 2014. Multidrug-resistance was defined as non-susceptibility to at least one agent in three or more antimicrobial categories. A mixed-effects logistic regression model was used to determine predictive factors of multidrug-resistant gram-negative bacteria cUTI.

Results

From 948 patients and 1074 microbiological isolates, Escherichia coli was the most frequent microorganism (559/1074), showing a 14.5% multidrug-resistance rate. Klebsiella pneumoniae was second (168/1074) and exhibited the highest multidrug-resistance rate (54.2%), followed by Pseudomonas aeruginosa (97/1074) with a 38.1% multidrug-resistance rate. Predictors of multidrug-resistant gram-negative bacteria were male gender (odds ratio [OR], 1.66; 95% confidence interval [CI], 1.20–2.29), acquisition of cUTI in a medical care facility (OR, 2.59; 95%CI, 1.80–3.71), presence of indwelling urinary catheter (OR, 1.44; 95%CI, 0.99–2.10), having had urinary tract infection within the previous year (OR, 1.89; 95%CI, 1.28–2.79) and antibiotic treatment within the previous 30 days (OR, 1.68; 95%CI, 1.13–2.50).

Conclusions

The current high rate of multidrug-resistant gram-negative bacteria infections among hospitalised patients with cUTIs in the studied area is alarming. Our predictive model could be useful to avoid inappropriate antibiotic treatment and implement antibiotic stewardship policies that enhance the use of carbapenem-sparing regimens in patients at low risk of multidrug-resistance.

Electronic supplementary material

The online version of this article (10.1186/s13756-018-0401-6) contains supplementary material, which is available to authorized users.

Emergence of antimicrobial resistance to piperacillin/tazobactam or meropenem in the ICU: Intermittent versus continuous infusion. A retrospective cohort study

BACKGROUND

Prolonged infusion of beta-lactam antibiotics is broadly recognized as a strategy to optimize antibiotic therapy by achieving a higher percentage of time that concentrations remain above the minimal inhibitory concentration (% fT_>MIC), i.e. the pharmacokinetic/pharmacodynamic (PK/PD) index. However, %fT_>MIC may not be the PK/PD index of choice for inhibition of resistance emergence and it is therefore unsure what impact prolonged infusion of beta-lactam antibiotics may have on the emergence of resistance.

METHODS

A retrospective cohort study including 205 patients receiving either intermittent (101 patients) or continuous (104 patients) infusion of piperacillin/tazobactam or meropenem was conducted in the ICU of the Ghent University Hospital. Logistic regression analysis was used to develop a prediction model and to determine whether the mode of infusion was a predictor of emergence of antimicrobial resistance.

RESULTS

Resistant strains emerged in 24 out of the 205 patients (11.7%). The mode of infusion was no predictor of emergence of antimicrobial resistance. Infection with Pseudomonas aeruginosa was associated with a significantly higher risk for emergence of resistance.

CONCLUSIONS

In this retrospective cohort study, the emergence of antimicrobial resistance to piperacillin/tazobactam or meropenem was not related to the mode of infusion.

The Rapid Prediction of Carbapenem Resistance in Patients With Klebsiella pneumoniae Bacteremia Using Electronic Medical Record Data

BACKGROUND

The administration of active antibiotics is often delayed in cases of carbapenem-resistant gram-negative bacteremia. Using electronic medical record (EMR) data to rapidly predict carbapenem resistance in patients with Klebsiella pneumoniae bacteremia could help reduce the time to active therapy.

METHODS

All cases of Klebsiella pneumoniae bacteremia at Mount Sinai Hospital from September 2012 through September 2016 were included. Cases were randomly divided into a "training set" and a "testing set." EMR data from the training set cases were reviewed, and significant risk factors for carbapenem resistance were entered into a multiple logistic regression model. Performance was assessed by repeated K-fold cross-validation and by applying the training set model to the testing set. All cases were also reviewed to determine the time to effective antibiotic therapy.

RESULTS

A total of 613 cases of Klebsiella pneumoniae bacteremia were included, 61 (10%) of which were carbapenem-resistant. The training and testing sets consisted of 460 and 153 cases, respectively. The regression model derived from the training set correctly predicted 73% of carbapenem-resistant cases and 59% of carbapenem-susceptible cases in the testing set (sensitivity, 73%; specificity, 59%; positive predictive value, 16%; negative predictive value, 95%). The mean area under the receiver operator characteristic curve of the K-fold cross-validation repeats was 0.731. Patients with carbapenem-resistant infections received active antibiotics significantly later than those with susceptible infections (40.4 hours vs 9.6 hours, P < .0001).

CONCLUSIONS

A multiple logistic regression model using EMR data can generate rapid, sensitive predictions of carbapenem resistance in patients with Klebsiella pneumoniae bacteremia, which could help shorten the time to effective therapy in these cases.

Antinociceptive and Antibacterial Properties of Anthocyanins and Flavonols from Fruits of Black and Non-Black Mulberries

Anthocyanins and flavones are important pigments responsible for the coloration of fruits. Mulberry fruit is rich in anthocyanins and flavonols, which have multiple uses in traditional Chinese medicine. The antinociceptive and antibacterial activities of total flavonoids (TF) from black mulberry (MnTF, TF of Morus nigra) and non-black mulberry (MmTF, TF of Morus mongolica; and MazTF, TF of Morus alba 'Zhenzhubai') fruits were studied. MnTF was rich in anthocyanins (11.3 mg/g) and flavonols (0.7 mg/g) identified by ultra-performance liquid chromatography-tunable ultraviolet/mass single-quadrupole detection (UPLC-TUV/QDa). Comparatively, MmTF and MazTF had low flavonol contents and MazTF had no anthocyanins. MnTF showed significantly higher antinociceptive and antibacterial activities toward Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus than MmTF and MazTF. MnTF inhibited the expression of interleukin 6 (IL-6), inducible nitric oxide synthase (iNOS), phospho-p65 (p-p65) and phospho-IκBα (p-IκBα), and increased interleukin 10 (IL-10). Additionally, mice tests showed that cyanidin-3-O-glucoside (C3G), rutin (Ru) and isoquercetin (IQ) were the main active ingredients in the antinociceptive process. Stronger antinociceptive effect of MnTF was correlated with its high content of anthocyanins and flavonols and its inhibitory effects on proinflammatory cytokines, iNOS and nuclear factor-κB (NF-κB) pathway-related proteins.

Risk Factors and Outcomes for Ineffective Empiric Treatment of Sepsis Caused by Gram-Negative Pathogens: Stratification by Onset of Infection

Sepsis and septic shock remain serious consequences of infections, with reported mortality rates in excess of 40 percent. Timely antibiotic therapy in cases of sepsis and septic shock is recognized as an important determinant of outcome. However, the administration of ineffective empirical treatment (IET) (an initial antibiotic regimen that is not active against the identified pathogen[s] based on in vitro susceptibility testing results) is associated with excess mortality compared to effective empirical treatment (EET). We examined all hospitalized patients at Barnes-Jewish Hospital with a sterile site (blood or pleural, abdominal, cerebrospinal, synovial, and pericardial fluid) culture positive for Gram-negative (GN) bacteria combined with a primary or secondary ICD-9-CM code for severe sepsis (995.92) or septic shock (785.52) between January 2010 and October 2015. Variables significantly associated with early-onset (<48 h of hospitalization) IET of GN sterile site sepsis and septic shock included age, recent hospitalization, and prior intravenous antibiotics. Late-onset IET was associated with increasing numbers of hospitalization days before infection onset and prior intravenous antibiotic administration. For patients with early-onset infection, we found no difference in rates of survival between patients receiving IET and EET. However, patients in the late-onset infection group receiving IET had a statistically lower rate of survival than those receiving EET. These data suggest that risk factors and outcomes for IET can vary based on the time of onset of infection. Our results also highlight the importance of prior intravenous antibiotic exposure as a risk factor for IET in infections by GN bacteria regardless of the time of onset of infection.