Time series analysis as a tool to predict the impact of antimicrobial restriction in antibiotic stewardship programs using the example of multidrug-resistant Pseudomonas aeruginosa

Leveraging Large Data, Statistics, and Machine Learning to Predict the Emergence of Resistant E. coli Infections

Drug-resistant Gram-negative bacterial infections, on average, increase the length of stay (LOS) in U.S. hospitals by 5 days, translating to approximately $15,000 per patient. We used statistical and machine-learning models to explore the relationship between antibiotic usage and antibiotic resistance over time and to predict the clinical and financial costs associated with resistant E. coli infections. We acquired data on antibiotic utilization and the resistance/sensitivity of 4776 microbial cultures at a Kaiser Permanente facility from April 2013 to December 2019. The ARIMA (autoregressive integrated moving average), neural networks, and random forest time series algorithms were employed to model antibiotic resistance trends. The models' performance was evaluated using mean absolute error (MAE) and root mean squared error (RMSE). The best performing model was then used to predict antibiotic resistance rates for the year 2020. The ARIMA model with cefazolin, followed by the one with cephalexin, provided the lowest RMSE and MAE values without signs of overfitting across training and test datasets. The study showed that reducing cefazolin usage could decrease the rate of resistant E. coli infections. Although piperacillin/tazobactam did not perform as well as cefazolin in our time series models, it performed reasonably well and, due to its broad spectrum, might be a practical target for interventions in antimicrobial stewardship programs (ASPs), at least for this particular facility. While a more generalized model could be developed with data from multiple facilities, this study acts as a framework for ASP clinicians to adopt statistical and machine-learning approaches, using region-specific data to make effective interventions.

Usefulness of dynamic regression time series models for studying the relationship between antimicrobial consumption and bacterial antimicrobial resistance in hospitals: a systematic review

BACKGROUNG

Antimicrobial resistance (AMR) is on the rise worldwide. Tools such as dynamic regression (DR) models can correlate antimicrobial consumption (AMC) with AMR and predict future trends to help implement antimicrobial stewardship programs (ASPs).

MAIN BODY

We carried out a systematic review of the literature up to 2023/05/31, searching in PubMed, ScienceDirect and Web of Science. We screened 641 articles and finally included 28 studies using a DR model to study the correlation between AMC and AMR at a hospital scale, published in English or French. Country, bacterial species, type of sampling, antimicrobials, study duration and correlations between AMC and AMR were collected. The use of β-lactams was correlated with cephalosporin resistance, especially in Pseudomonas aeruginosa and Enterobacterales. Carbapenem consumption was correlated with carbapenem resistance, particularly in Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii. Fluoroquinolone use was correlated with fluoroquinolone resistance in Gram-negative bacilli and methicillin resistance in Staphylococcus aureus. Multivariate DR models highlited that AMC explained from 19 to 96% of AMR variation, with a lag time between AMC and AMR variation of 2 to 4 months. Few studies have investigated the predictive capacity of DR models, which appear to be limited.

CONCLUSION

Despite their statistical robustness, DR models are not widely used. They confirmed the important role of fluoroquinolones, cephalosporins and carbapenems in the emergence of AMR. However, further studies are needed to assess their predictive capacity and usefulness for ASPs.

Systematic review of time lag between antibiotic use and rise of resistant pathogens among hospitalized adults in Europe

Background

Antimicrobial resistance (AMR) causes substantial health and economic burden to individuals, healthcare systems and societies globally. Understanding the temporal relationship between antibiotic consumption and antibiotic resistance in hospitalized patients can better inform antibiotic stewardship activities and the time frame for their evaluation.

Objectives

This systematic review examined the temporal relationship between antibiotic use and development of antibiotic resistance for 42 pre-defined antibiotic and pathogen combinations in hospitalized adults in Europe.

Methods

Searches in MEDLINE, Embase, Cochrane Library and NIHR Centre for Reviews and Dissemination were undertaken from 2000 to August 2021. Pathogens of interest were Escherichia coli, Klebsiella pneumoniae, Streptococcus pneumoniae, Staphylococcus aureus, Enterococcus faecium, CoNS, Pseudomonas aeruginosa and Acinetobacter baumannii complex.

Results

Twenty-eight ecological studies and one individual-level study were included. Ecological studies were predominantly retrospective in design (19 studies) and of reasonable (20 studies) to high (8 studies) methodological quality. Of the eight pathogens of interest, no relevant data were identified for S. pneumoniae and CoNS. Across all pathogens, the time-lag data from the 28 ecological studies showed a similar pattern, with the majority of studies reporting lags ranging from 0 to 6 months.

Conclusions

Development of antibiotic resistance for the investigated antibiotic/pathogen combinations tends to occur over 0 to 6 months following exposure within European hospitals. This information could inform planning of antibiotic stewardship activities in hospital settings.

Association of Antibiotic Use with the Resistance Epidemiology of Pseudomonas aeruginosa in a Hospital Setting: A Four-Year Retrospective Time Series Analysis

Background: Even though, Pseudomonas aeruginosa is a common cause of hospital-acquired infections, treatment is challenging because of decreasing rates of susceptibility to many broad-spectrum antibiotics. Methods: Consumption data of eight broad spectrum antimicrobial agents and resistance rates of P. aeruginosa were collected for 48 consecutive months. Autoregressive integrated moving average (ARIMA) and transfer functions models were used to develop relationships between antibiotic use and resistance. Results: Positive correlations between P. aeruginosa resistance and uses of ciprofloxacin (p < 0.001), meropenem (p < 0.001), and cefepime (p = 0.005) were identified. Transfer function models showed the quantified effect of each of these antibiotics on resistance. Regarding levofloxacin, ceftazidime, piperacillin/tazobactam and imipenem, no significant relationships were found. For ceftazidime and levofloxacin, this was probably due to their low consumption, while for imipenem the reason can possibly be ascribed to the already high established P. aeruginosa resistance in the hospital. Conclusion: In the hospital setting, the effect of antimicrobial agents’ consumption on the susceptibility epidemiology of P. aeruginosa differs significantly for each one of them. In this study, the role of precedent use of meropenem, cefepime and ciprofloxacin was quantified in the development of P. aeruginosa resistance.

Feature selection based multivariate time series forecasting: An application to antibiotic resistance outbreaks prediction

Antimicrobial resistance has become one of the most important health problems and global action plans have been proposed globally. Prevention plays a key role in these actions plan and, in this context, we propose the use of Artificial Intelligence, specifically Time Series Forecasting techniques, for predicting future outbreaks of Methicillin-resistant Staphylococcus aureus (MRSA). Infection incidence forecasting is approached as a Feature Selection based Time Series Forecasting problem using multivariate time series composed of incidence of Staphylococcus aureus Methicillin-sensible and MRSA infections, influenza incidence and total days of therapy of both of Levofloxacin and Oseltamivir antimicrobials. Data were collected from the University Hospital of Getafe (Spain) from January 2009 to January 2018, using months as time granularity. The main contributions of the work are the following: the applications of wrapper feature selection methods where the search strategy is based on multi-objective evolutionary algorithms (MOEA) along with evaluators based on the most powerful state-of-the-art regression algorithms. The performance of the feature selection methods has been measured using the root mean square error (RMSE) and mean absolute error (MAE) performance metrics. A novel multi-criteria decision-making process is proposed in order to select the most satisfactory forecasting model, using the metrics previously mentioned, as well as the slopes of model prediction lines in the 1, 2 and 3 steps-ahead predictions. The multi-criteria decision-making process is applied to the best models resulting from a ranking of databases and regression algorithms obtained through multiple statistical tests. Finally, to the best of our knowledge, this is the first time that a feature selection based multivariate time series methodology is proposed for antibiotic resistance forecasting. Final results show that the best model according to the proposed multi-criteria decision making process provides a RMSE = (0.1349, 0.1304, 0.1325) and a MAE = (0.1003, 0.096, 0.0987) for 1, 2, and 3 steps-ahead predictions.

Quantifying uncertainty about future antimicrobial resistance: Comparing structured expert judgment and statistical forecasting methods

The increase of multidrug resistance and resistance to last-line antibiotics is a major global public health threat. Although surveillance programs provide useful current and historical information on the scale of the problem, the future emergence and spread of antibiotic resistance is uncertain, and quantifying this uncertainty is crucial for guiding decisions about investment in antibiotics and resistance control strategies. Mathematical and statistical models capable of projecting future rates are challenged by the paucity of data and the complexity of the emergence and spread of resistance, but experts have relevant knowledge. We use the Classical Model of structured expert judgment to elicit projections with uncertainty bounds of resistance rates through 2026 for nine pathogen-antibiotic pairs in four European countries and empirically validate the assessments against data on a set of calibration questions. The performance-weighted combination of experts in France, Spain, and the United Kingdom projected that resistance for five pairs on the World Health Organization’s priority pathogens list (E. coli and K. pneumoniae resistant to third-generation cephalosporins and carbapenems and MRSA) would remain below 50% in 2026. In Italy, although upper bounds of 90% credible ranges exceed 50% resistance for some pairs, the medians suggest Italy will sustain or improve its current rates. We compare these expert projections to statistical forecasts based on historical data from the European Antimicrobial Resistance Surveillance Network (EARS-Net). Results from the statistical models differ from each other and from the judgmental forecasts in many cases. The judgmental forecasts include information from the experts about the impact of current and future shifts in infection control, antibiotic usage, and other factors that cannot be easily captured in statistical forecasts, demonstrating the potential of structured expert judgment as a tool for better understanding the uncertainty about future antibiotic resistance.

Semi-Automated Visualization and ANalysis of Trends: A "SAVANT" for Facilitating Antimicrobial Stewardship Using Antistaphylococcal Resistance and Consumption as a Prototype

Background

Governments and health care regulators now require hospitals and nursing homes to establish programs to monitor and report antimicrobial consumption and resistance. However, additional resources were not provided. We sought to develop an approach for monitoring antimicrobial resistance and consumption that health care systems can implement with minimal added costs or modifications to existing diagnostic and informatics infrastructure.

Methods

Using (1) the electronic laboratory information system of a nationwide managed care network, (2) the 3 most widely used commercial microbiology diagnostic platforms, and (3) Staphylococcus aureus, one of the most common causes of infections worldwide, as a prototype, we validated the approach dubbed "SAVANT" for Semi-Automated Visualization and ANalysis of Trends. SAVANT leverages 3 analytical methods (time series analysis, the autoregressive integrated moving average, and generalized linear regression) on either commercial or open source software to report trends in antistaphylococcal use and resistance.

Results

All laboratory results from January 2010 through December 2015 from an annual average of 9.2 million health care beneficiaries were queried. Inpatient and outpatient prescription rates were calculated for 8 key antistaphylococcal compounds. Trends and relationships of antistaphylococcal consumption and resistance among 81 840 unique S. aureus isolates from >6.5 million cultures were revealed.

Conclusions

Using existing or freely available resources, SAVANT was successfully implemented across a complex and geographically dispersed 280-hospital network, bridging a critical gap between medical informatics, large-scale data analytics, and mandatory reporting of health care quality metrics.

Impact of an infectious disease specialist on antifungal use: an interrupted time-series analysis in a tertiary hospital in Tokyo

BACKGROUND

Antimicrobial stewardship programmes are considered essential for optimizing antimicrobial use in order to improve patient outcomes, reduce the number of adverse sequelae, prevent resistance, and ensure cost-effective therapy.

AIM

To assess the efficacy and the limitations of antifungal antimicrobial stewardship programmes.

METHODS

A bundle to manage infectious diseases was implemented in our hospital in October 2010. Data regarding antimicrobial use density (AUD) from April 2006 to May 2016 were collected. Trends in AUD were assessed using an interrupted time-series model for three separate periods: the pre-bundle, the bundle implementation, and the long-term follow-up periods. The primary and secondary outcomes were AUD (defined daily dose (DDD) per 1000 patient-days) of intravenous antifungals and expenditure on antifungals per fiscal year, respectively.

FINDINGS

The AUD for all intravenous antifungals decreased from 26.1 in 2006 to 9.9 in 2015. Whereas the change in the trend during the pre-bundle period was not significant (slope: 0.062; 95% confidence interval (CI): -0.180 to 0.305), a significant decrease was observed in the bundle implementation period (slope: -0.535; 95% CI: -0.907 to -0.164). The trend slowed during the long-term follow-up period (slope: -0.040; 95% CI: -0.218 to 0.138). Total expenditure on antifungals decreased by 73%, from ¥52,354,411 in fiscal year 2006 to ¥14,073,099 in fiscal year 2015.

CONCLUSION

The bundle significantly reduced the use of antifungals and decreased costs over time, but this effect was limited in that it had stabilized within three years.

A proposed analytic framework for determining the impact of an antimicrobial resistance intervention

Antimicrobial use (AMU) is increasingly threatened by antimicrobial resistance (AMR). The FDA is implementing risk mitigation measures promoting prudent AMU in food animals. Their evaluation is crucial: the AMU/AMR relationship is complex; a suitable framework to analyze interventions is unavailable. Systems science analysis, depicting variables and their associations, would help integrate mathematics/epidemiology to evaluate the relationship. This would identify informative data and models to evaluate interventions. This National Institute for Mathematical and Biological Synthesis AMR Working Group's report proposes a system framework to address the methodological gap linking livestock AMU and AMR in foodborne bacteria. It could evaluate how AMU (and interventions) impact AMR. We will evaluate pharmacokinetic/dynamic modeling techniques for projecting AMR selection pressure on enteric bacteria. We study two methods to model phenotypic AMR changes in bacteria in the food supply and evolutionary genotypic analyses determining molecular changes in phenotypic AMR. Systems science analysis integrates the methods, showing how resistance in the food supply is explained by AMU and concurrent factors influencing the whole system. This process is updated with data and techniques to improve prediction and inform improvements for AMU/AMR surveillance. Our proposed framework reflects both the AMR system's complexity, and desire for simple, reliable conclusions.

The Impact of Reducing Antibiotics on the Transmission of Multidrug-Resistant Organisms

OBJECTIVE Antibiotic resistance is a major threat to public health. Resistance is largely driven by antibiotic usage, which in many cases is unnecessary and can be improved. The impact of decreasing overall antibiotic usage on resistance is unknown and difficult to assess using standard study designs. The objective of this study was to explore the potential impact of reducing antibiotic usage on the transmission of multidrug-resistant organisms (MDROs). DESIGN We used agent-based modeling to simulate interactions between patients and healthcare workers (HCWs) using model inputs informed by the literature. We modeled the effect of antibiotic usage as (1) a microbiome effect, for which antibiotic usage decreases competing bacteria and increases the MDRO transmission probability between patients and HCWs and (2) a mutation effect that designates a proportion of patients who receive antibiotics to subsequently develop a MDRO via genetic mutation. SETTING Intensive care unit INTERVENTIONS Absolute reduction in overall antibiotic usage by experimental values of 10% and 25% RESULTS Reducing antibiotic usage absolutely by 10% (from 75% to 65%) and 25% (from 75% to 50%) reduced acquisition rates of high-prevalence MDROs by 11.2% (P<.001) and 28.3% (P<.001), respectively. We observed similar effect sizes for low-prevalence MDROs. CONCLUSIONS In a critical care setting, where up to 50% of antibiotic courses may be inappropriate, even a moderate reduction in antibiotic usage can reduce MDRO transmission. Infect Control Hosp Epidemiol 2017;38:663-669.

Antibiotic Selection Pressure Determination through Sequence-Based Metagenomics

The human gut forms a dynamic reservoir of antibiotic resistance genes (ARGs). Treatment with antimicrobial agents has a significant impact on the intestinal resistome and leads to enhanced horizontal transfer and selection of resistance. We have monitored the development of intestinal ARGs over a 6-day course of ciprofloxacin (Cp) treatment in two healthy individuals by using sequenced-based metagenomics and different ARG quantification methods. Fixed- and random-effect models were applied to determine the change in ARG abundance per defined daily dose of Cp as an expression of the respective selection pressure. Among various shifts in the composition of the intestinal resistome, we found in one individual a strong positive selection for class D beta-lactamases which were partly located on a mobile genetic element. Furthermore, a trend to a negative selection has been observed with class A beta-lactamases (-2.66 hits per million sample reads/defined daily dose; P = 0.06). By 4 weeks after the end of treatment, the composition of ARGs returned toward their initial state but to a different degree in both subjects. We present here a novel analysis algorithm for the determination of antibiotic selection pressure which can be applied in clinical settings to compare therapeutic regimens regarding their effect on the intestinal resistome. This information is of critical importance for clinicians to choose antimicrobial agents with a low selective force on their patients' intestinal ARGs, likely resulting in a diminished spread of resistance and a reduced burden of hospital-acquired infections with multidrug-resistant pathogens.

Clinical and treatment-related risk factors for nosocomial colonisation with extensively drug-resistant Pseudomonas aeruginosa in a haematological patient population: a matched case control study

Background

This study aimed to investigate risk factors for colonisation with extensively drug-resistant P. aeruginosa (XDR-PA) in immunocompromised patients and to build a clinical risk score (CRS) based on these results.

Methods

We conducted a matched case–control study with 31 cases and 93 controls (1:3). Cases were colonised with XDR-PA during hospitalisation. Independent risk factors were determined using a three step conditional logistic regression procedure. A CRS was built with respect to the corresponding risk fraction of each risk factor, and its discriminatory power was estimated by receiver operating characteristic (ROC) analysis.

Results

The presence of a central venous catheter (OR 7.41, P = 0.0008), the presence of a urinary catheter (OR 21.04, P < 0.0001), CRP > 10 mg/dl (OR 7.36, P = 0.0015), and ciprofloxacin administration (OR 5.53, P = 0.025) were independent risk factors. The CRS exhibited a high discriminatory power, defining a high risk population with an approximately fourteen times greater risk for XDR-PA colonisation.

Conclusions

Unnecessary use of antibiotics, particularly ciprofloxacin should be avoided, and a high standard of infection control measures must be achieved when using medical devices. A CRS can be used for adaptation of the active screening culture policy to the local setting.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-014-0650-9) contains supplementary material, which is available to authorized users.

Mathematical model to quantify the effects of risk factors on carbapenem-resistant Acinetobacter baumannii

Carbapenem-resistant Acinetobacter baumannii (CRAB) infections are increasing, and they are associated with an increased risk of mortality in hospitalized patients. Linear regression is commonly used to identify concurrent trends, but it cannot quantify the relationship between risk factors and resistance. We developed a model to quantify the impact of antibiotic consumption on the prevalence of CRAB over time. Data were collected from January 2007 to June 2013 from our institution. Quarterly antibiotic consumption was expressed as defined daily dose/1,000 inpatient days. Six-month prevalence of CRAB was expressed as a percentage of all nonrepeat A. baumannii isolates tested. Individual trends were identified using linear regression. Antibiotic consumption from 2007 to 2011 was input as a step function in a relationship with CRAB. Model fit was evaluated by visual inspection and the residual sum of squares. The final model was validated using the best-fit (95% confidence interval) parameter estimates and antibiotic consumption to predict CRAB prevalence from January 2012 to June 2013. Cefepime, ertapenem, and piperacillin-tazobactam consumption and CRAB prevalence increased significantly over time. CRAB prevalence was best correlated to ertapenem (use sensitive; r2=0.76), and accounting for additional concurrent antibiotic use did not significantly improve model fit. Prospective validation with ertapenem consumption correlated well with CRAB observations, suggesting good predicting ability of the model. Our model provided the quantitative impact of antibiotic consumption on CRAB. We plan to further refine this model to account for multiple risk factors. Interventions should focus on controlling risk factors with the highest impact on resistance.

Evaluation of phenotypic detection methods for metallo-β-lactamases (MBLs) in clinical isolates of Pseudomonas aeruginosa

Metallo-beta-lactamase (MBL) production in Pseudomonas aeruginosa is a growing issue across the globe. Fast and reliable diagnostic tools are needed for appropriate implementation of infection control measures. In this study we evaluated the performance of three commercial combined disk tests, two EDTA based in-house combined disk tests and the Carba NP test in comparison to molecular detection of MBL genes on 133 meropenem non-susceptible non-duplicate P. aeruginosa clinical isolates. The meropenem/DPA based commercial KPC + MBL-confirm ID kit (Rosco Diagnostica, Denmark) and the MASTDISCS™ ID carbapenemase (Enterobacteriaceae) detection disc set (MAST Diagnostics, UK) showed sensitivities of 31.1 % and 28.8 % and specificities of 69.3 % and 79.6 %, respectively. The total MBL confirm kit (Rosco Diagnostica, Denmark) contains imipenem/DPA and imipenem/EDTA combination disks. Evaluation of the single disk combinations revealed 84.4 % sensitivity and 81.8 % specificity for the imipenem/DPA assay and 86.7 % sensitivity and 51.1 % specificity for the imipenem/EDTA test. Applying both tests simultaneously resulted in a slightly higher sensitivity of 88.9 % but a lower specificity of 48.9 % when compared to the single tests alone. The Carba NP test showed 93.3 % sensitivity and 96.6 % specificity. All phenotypic combined disk tests lacked either sensitivity or specificity for the detection of MBL in P. aeruginosa. The Carba NP test showed excellent test properties, but suffers from drawbacks in handling and high costs. The optimal diagnostic approach needs to be chosen depending on the epidemiological situation, laboratory resources and availability of molecular confirmation tests.

Rapid intrinsic fluorescence method for direct identification of pathogens in blood cultures

A positive blood culture is a critical result that requires prompt identification of the causative agent. This article describes a simple method to identify microorganisms from positive blood culture broth within the time taken to perform a Gram stain (<20 min). The method is based on intrinsic fluorescence spectroscopy (IFS) of whole cells and required development of a selective lysis buffer, aqueous density cushion, optical microcentrifuge tube, and reference database. A total of 1,121 monomicrobial-positive broth samples from 751 strains were analyzed to build a database representing 37 of the most commonly encountered species in bloodstream infections or present as contaminants. A multistage algorithm correctly classified 99.6% of unknown samples to the Gram level, 99.3% to the family level, and 96.5% to the species level. There were no incorrect results given at the Gram or family classification levels, while 0.8% of results were discordant at the species level. In 8/9 incorrect species results, the misidentified isolate was assigned to a species of the same genus. This unique combination of selective lysis, density centrifugation, and IFS can rapidly identify the most common microbial species present in positive blood cultures. Faster identification of the etiologic agent may benefit the clinical management of sepsis. Further evaluation is now warranted to determine the performance of the method using clinical blood culture specimens.

IMPORTANCE

Physicians often require the identity of the infective agent in order to make life-saving adjustments to empirical therapy or to switch to less expensive and/or more targeted antimicrobials. However, standard identification procedures take up to 2 days after a blood culture is signaled positive, and even most rapid molecular techniques take several hours to provide a result. Other techniques are faster (e.g., matrix-assisted laser desorption ionization-time of flight [MALDI-TOF] mass spectrometry) but require time-consuming manual processing steps and expensive equipment. There remains a clear need for a simple, inexpensive method to rapidly identify microorganisms directly from positive blood cultures. The promising new method described in this research article can identify microorganisms in minutes by optical spectroscopy, thus permitting the lab to simultaneously report the presence of a positive blood culture and the organism's identity.

Effect of metallo-β-lactamase production and multidrug resistance on clinical outcomes in patients with Pseudomonas aeruginosa bloodstream infection: a retrospective cohort study

Background

Blood stream infections (BSI) with Pseudomonas aeruginosa lead to poor clinical outcomes. The worldwide emergence and spread of metallo-β-lactamase (MBL) producing, often multidrug-resistant organisms may further aggravate this problem. Our study aimed to investigate the effect of MBL-producing P. aeruginosa (MBL-PA) and various other resistance phenotypes on clinical outcomes.

Methods

A retrospective cohort study was conducted in three German hospitals. Medical files from 2006 until 2012 were studied, and a number of 113 patients with P. aeruginosa BSI were included. The presence of VIM, IMP and NDM genes was detected using molecular techniques. Genetic relatedness was assessed through multilocus sequence typing (MLST). The effect of resistance patterns or MBL production on clinical outcomes was investigated by using multivariate Cox regression models.

Results

In-hospital mortality was significantly higher in patients with MBL-PA and multidrug-resistant P. aeruginosa. However, neither BSI with MBL-PA nor BSI with various resistance phenotypes of P. aeruginosa were independently associated with mortality or length of hospital stay. In multivariate models, the SAPS II score (HR 1.046), appropriate definitive treatment (HR range 0.25-0.26), and cardiovascular disease (HR range 0.44-0.46) were independent predictors of mortality. Concomitant infections were associated with an excess length of stay (HR < 1).

Conclusions

Medication with appropriate antimicrobial agents at any time during the course of infection remains the key for improving clinical outcomes in patients with P. aeruginosa BSI and should be combined with a strict implementation of routine infection control measures.

[Multiresistant gram-negative bacteria. A bacterial challenge of the twenty-first century]

The incidence of human-pathogenic microorganisms with resistance or even complete insensitivity to broad-spectrum antibiotics is increasing. This poses a serious challenge to infection control in hospitals and to hygiene strategies in clinical areas with critically ill patients, particularly intensive care or transplant units. These microorganisms create problems that are seemingly impossible to solve at present. The management of gram-positive pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) has been facilitated by evidence-based recommendations resulting in a measurable decrease in the incidence of infection and, where treatment is concerned, in a selection of reliably effective drugs for clinicians. However, in the more frequent cases of multiresistant gram-negative (MRGN) pathogens, the only option is the use of poorly defined regimens with older drugs, which carry the risk of serious side effects and organ toxicities. This article presents a comparative analysis of hospital hygiene management for MRSA and MRGN pathogens, pointing out both similarities and features which are unique to MRGN pathogens.