A prediction tool for nosocomial multi-drug Resistant Gram-Negative Bacilli infections in critically ill patients - prospective observational study

Prediction models for diagnosis and prognosis of the colonization or infection of multidrug-resistant organisms in adults: a systematic review, critical appraisal, and meta-analysis

BACKGROUND

Prediction models help to target patients at risk of multidrug-resistant organism (MDRO) colonization or infection and could serve as tools informing clinical practices to prevent MDRO transmission and inappropriate empiric antibiotic therapy. However, there is limited evidence to identify which among the available models are of low risk of bias and suitable for clinical application.

OBJECTIVES

To identify, describe, appraise, and summarise the performance of all prognostic and diagnostic models developed or validated for predicting MDRO colonization or infection.

DATA SOURCES

Six electronic literature databases and clinical registration databases were searched until April 2022.

STUDY ELIGIBILITY CRITERIA

Development and validation studies of any multivariable prognostic and diagnostic models to predict MDRO colonization or infection in adults.

PARTICIPANTS

Adults (≥ 18 years old) without MDRO colonization or infection (in prognostic models) or with unknown or suspected MDRO colonization or infection (in diagnostic models).

ASSESSMENT OF RISK OF BIAS

The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias. Evidence certainty was assessed using the Grading of Recommendations Assessment, Development, and Evaluation approach.

METHODS OF DATA SYNTHESIS

Meta-analyses were conducted to summarize the discrimination and calibration of the models' external validations conducted in at least two non-overlapping datasets.

RESULTS

We included 162 models (108 studies) developed for diagnosing (n = 135) and predicting (n = 27) MDRO colonization or infection. Models exhibited a high-risk of bias, especially in statistical analysis. High-frequency predictors were age, recent invasive procedures, antibiotic usage, and prior hospitalization. Less than 25% of the models underwent external validations, with only seven by independent teams. Meta-analyses for one diagnostic and two prognostic models only produced very low to low certainty of evidence.

CONCLUSIONS

The review comprehensively described the models for identifying patients at risk of MDRO colonization or infection. We cannot recommend which models are ready for application because of the high-risk of bias, limited validations, and low certainty of evidence from meta-analyses, indicating a clear need to improve the conducting and reporting of model development and external validation studies to facilitate clinical application.

Prediction of multidrug-resistant bacteria (MDR) hospital-acquired infection (HAI) and colonisation: A systematic review

BACKGROUND

Hospital-Acquired Infections (HAI) represent a public health priority in most countries worldwide. Our main objective was to systematically review the quality of the predictive modeling literature regarding multidrug-resistant gram-negative bacteria in Intensive Care Units (ICUs).

METHODS

We conducted and reported a Systematic Literature Review according to the recommendations of the PRISMA statement. We analysed the quality of the articles in terms of adherence to the TRIPOD checklist.

RESULTS

The initial search identified 1935 papers and 15 final articles were included in the review. Most studies analysed used traditional prediction models (logistic regression), and only three developed machine-learning techniques. We noted poor adherence to the main methodological issues recommended in the TRIPOD checklist to develop prediction models, such as handling missing data (20% adherence), model-building procedures (20% adherence), assessing model performance (47% adherence), and reporting performance measures (33% adherence).

CONCLUSIONS

Our review found few studies that use efficient alternatives to predict the acquisition of multidrug-resistant gram-negative bacteria in ICUs. Furthermore, we noted a lack of strategies for dealing with missing data, feature selection, and imbalanced datasets, a common problem in HAI studies.

Incidence of Carbapenem-Resistant Gram-Negative Bacterial Infections in Critically Ill Patients with COVID-19 as Compared to Non-COVID-19 Patients: A Prospective Case-Control Study

Introduction

Critically ill COVID-19 patients hospitalized in intensive care units (ICU) are immunosuppressed due to SARSCoV-2-related immunological effects and are administered immunomodulatory drugs. This study aimed to determine whether these patients carry an increased risk of multi-drug resistant (MDR) and especially carbapenem-resistant Gram-negative (CRGN) bacterial infections compared to other critically ill patients without COVID-19.

Materials and Methods

A prospective case-control study was conducted between January 2022 and August 2023. The ICU patients were divided into two groups (COVID-19 and non-COVID-19). Differences in the incidence of CRGN infections from Klebsiella pneumoniae, Acinetobacter spp., and Pseudomonas aeruginosa were investigated. In addition, an indicator of the infection rate of the patients during their ICU stay was calculated. Factors independently related to mortality risk were studied.

Results

Forty-two COVID-19 and 36 non-COVID-19 patients were analyzed. There was no statistically significant difference in the incidence of CRGN between COVID-19 and non-COVID-19 patients. The infection rate was similar in the two groups. Regarding the aetiological agents of CRGN infections, Pseudomonas aeruginosa was significantly more common in non-COVID-19 patients (p=0.007). COVID-19 patients had longer hospitalisation before ICU admission (p=0.003) and shorter ICU length of stay (LOS) (p=0.005). ICU COVID-19 patients had significantly higher mortality (p < 0.001) and sequential organ failure assessment (SOFA) score (p < 0.001) compared to non-COVID-19 patients. Μortality secondary to CRGN infections was also higher in COVID-19 patients compared to non-COVID-19 patients (p=0.033). Male gender, age, ICU LOS, and hospital LOS before ICU admission were independent risk factors for developing CRGN infections. Independent risk factors for patients' mortality were COVID-19 infection, obesity, SOFA score, total number of comorbidities, WBC count, and CRP, but not infection from CRGN pathogens.

Conclusions

The incidence of CRGN infections in critically ill COVID-19 patients is not different from that of non-COVID-19 ICU patients. The higher mortality of COVID-19 patients in the ICU is associated with higher disease severity scores, a higher incidence of obesity, and multiple underlying comorbidities, but not with CRGN infections.

A deep learning model for predicting multidrug-resistant organism infection in critically ill patients

BACKGROUND

This study aimed to apply the backpropagation neural network (BPNN) to develop a model for predicting multidrug-resistant organism (MDRO) infection in critically ill patients.

METHODS

This study collected patient information admitted to the intensive care unit (ICU) of the Affiliated Hospital of Qingdao University from August 2021 to January 2022. All patients enrolled were divided randomly into a training set (80%) and a test set (20%). The least absolute shrinkage and selection operator and stepwise regression analysis were used to determine the independent risk factors for MDRO infection. A BPNN model was constructed based on these factors. Then, we externally validated this model in patients from May 2022 to July 2022 over the same center. The model performance was evaluated by the calibration curve, the area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

In the primary cohort, 688 patients were enrolled, including 109 (15.84%) MDRO infection patients. Risk factors for MDRO infection, as determined by the primary cohort, included length of hospitalization, length of ICU stay, long-term bed rest, antibiotics use before ICU, acute physiology and chronic health evaluation II, invasive operation before ICU, quantity of antibiotics, chronic lung disease, and hypoproteinemia. There were 238 patients in the validation set, including 31 (13.03%) MDRO infection patients. This BPNN model yielded good calibration. The AUC of the training set, the test set and the validation set were 0.889 (95% CI 0.852-0.925), 0.919 (95% CI 0.856-0.983), and 0.811 (95% CI 0.731-0.891), respectively.

CONCLUSIONS

This study confirmed nine independent risk factors for MDRO infection. The BPNN model performed well and was potentially used to predict MDRO infection in ICU patients.

Establishment of a risk prediction model for multidrug-resistant bacteria in deceased organ donors: a retrospective cohort study in China

Background

Multidrug resistance in bacteria is a serious problem in organ transplantations. This study aimed to identify risk factors and establish a predictive model for screening deceased organ donors for multidrug-resistant (MDR) bacteria.

Methods

A retrospective cohort study was conducted at the First Affiliated Hospital of Zhejiang University School of Medicine from July 1, 2019 to December 31, 2022. The univariate and multivariate logistic regression analysis was used to determine independent risk factors associated with MDR bacteria in organ donors. A nomogram was established based on these risk factors. A calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to estimated the model.

Results

In 164 organ donors, the incidence of MDR bacteria in culture was 29.9%. The duration of antibiotic use ≥3 days (odds ratio [OR] 3.78, 95% confidence interval [CI] 1.62-8.81, p=0.002), length of intensive care unit (ICU) stay per day(OR 1.06, 95% CI 1.02-1.11, p=0.005) and neurosurgery (OR 3.31, 95% CI 1.44-7.58, p=0.005) were significant independent predictive factors for MDR bacteria. The nomogram constructed using these three predictors displayed good predictive ability, with an area under the ROC curve value of 0.79. The calibration curve showed a high consistency between the probabilities and observed values. DCA also revealed the potential clinical usefulness of this nomogram.

Conclusions

The duration of antibiotic use ≥3 days, length of ICU stay and neurosurgery are independent risk factors for MDR bacteria in organ donors. The nomogram can be used to monitor MDR bacteria acquisition risk in organ donors.

Tracking Epidemiological Characteristics and Risk Factors of Multi-Drug Resistant Bacteria in Intensive Care Units

Objectives

Multi-drug resistance (MDR) emerged as a serious threat in intensive care unit (ICU) settings. Our study aimed to investigate the major pathogens in ICU and identify the risk factors for MDR infection.

Methods

We performed a retrospective analysis of patients admitted to the ICU. Multivariate logistic regression was applied to identify the independent predictors, and then a nomogram to predict the probability of MDR infection.

Results

A total of 278 patients with 483 positive cultures were included. 249 (51.55%) had at least one MDR pathogen, including extensively drug-resistant (XDR) 77 (30.92%) and pan drug-resistant (PDR) 39 (15.66%), respectively. Klebsiella pneumonia was the most frequently isolated pathogen. We identified the number of bacteria (OR 2.91, 95% CI 1.97-4.29, P < 0.001), multiple invasive procedures (OR 2.23, 95% CI 1.37-3.63, P = 0.001), length of stay (LOS) (OR 1.01, 95% CI 1.00-1.02, P = 0.007), Hemoglobin (Hb) (OR 0.99, 95% CI 0.98-1.00, P = 0.01) were independent risk factors for MDR infection. Our nomogram displayed good discrimination with curve AUC was 0.75 (95% CI: 0.70-0.81). The decision curves also indicate the clinical utility of our nomogram. Additionally, the in-hospital mortality with MDR pathogens was independently associated with XDR (HR, 2.60; 95% CI: 1.08-6.25; P = 0.03) and total protein (TP) (HR, 0.95; 95% CI: 0.91-0.99; P = 0.03).

Conclusion

The number of bacteria, multiple invasive procedures, LOS, and Hb were the independent predictors associated with MDR pathogens. Our nomogram is potentially useful for predicting the occurrence of MDR infection. Besides, we also identify XDR and TP as the independent risk factors for in-hospital mortality with MDR infection. The current prevalence of MDR strains was also described. The results will contribute to the identification and preventive management of patients at increased risk of infection.

Quantifying Gram-Negative Resistance to Empiric Treatment After Repeat ExpoSure To AntimicRobial Therapy (RESTART)

Background

Antibiotic exposure is a primary predictor of subsequent antibiotic resistance; however, development of cross-resistance between antibiotic classes is also observed. The impact of changing to a different antibiotic from that of previous exposure is not established.

Methods

This was a retrospective, single-center cohort study of hospitalized adult patients previously exposed to an antipseudomonal β-lactam (APBL) for at least 48 hours in the 90 days prior to the index infection with a gram-negative bloodstream or respiratory infection. Susceptibility rates to empiric therapy were compared between patients receiving the same (repeat group) versus a different antibiotic from prior exposure (change group).

Results

A total of 197 patients were included (n = 94 [repeat group] and n = 103 [change group]). Pathogen susceptibility to empiric therapy was higher in the repeat group compared to the change group (76.6% vs 60.2%; P = .014). After multivariable logistic regression, repeat APBL was associated with an increased likelihood of pathogen susceptibility (adjusted odds ratio, 2.513; P = .012). In contrast, there was no difference in susceptibility rates between the repeat group and the subgroup of change patients who received an empiric APBL (76.6% vs 78.5%; P = .900). Longer APBL exposure duration (P = .012) and chronic kidney disease (P = .002) were associated with higher nonsusceptibility to the exposure APBL. In-hospital mortality was not significantly different between the repeat and change groups (18.1% vs 23.3%; P = .368).

Conclusions

The common practice of changing to a different APBL from that of recent exposure may not be warranted.

Prediction of Intracranial Infection in Patients under External Ventricular Drainage and Neurological Intensive Care: A Multicenter Retrospective Cohort Study

Objective: To generate an optimal prediction model along with identifying major contributors to intracranial infection among patients under external ventricular drainage and neurological intensive care. Methods: A retrospective cohort study was conducted among patients admitted into neurointensive care units between 1 January 2015 and 31 December 2020 who underwent external ventricular drainage due to traumatic brain injury, hydrocephalus, and nonaneurysmal spontaneous intracranial hemorrhage. Multivariate logistic regression in combination with the least absolute shrinkage and selection operator regression was applied to derive prediction models and optimize variable selections. Other machine-learning algorithms, including the support vector machine and K-nearest neighbor, were also applied to derive alternative prediction models. Five-fold cross-validation was used to train and validate each model. Model performance was assessed by calibration plots, receiver operating characteristic curves, and decision curves. A nomogram analysis was developed to explicate the weights of selected features for the optimal model. Results: Multivariate logistic regression showed the best performance among the three tested models with an area under curve of 0.846 ± 0.006. Six variables, including hemoglobin, albumin, length of operation time, American Society of Anesthesiologists grades, presence of traumatic subarachnoid hemorrhage, and a history of diabetes, were selected from 37 variable candidates as the top-weighted prediction features. The decision curve analysis showed that the nomogram could be applied clinically when the risk threshold is between 20% and 100%. Conclusions: The occurrence of external ventricular-drainage-associated intracranial infections could be predicted using optimal models and feature-selection approaches, which would be helpful for the prevention and treatment of this complication in neurointensive care units.

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.

Derivation and Validation of a Predictive Scoring Model of Infections Due to Acinetobacter baumannii in Patients with Hospital Acquired Pneumonia by Gram-Negative Bacilli

Background

The prognosis of ABA-HAP patients is very poor. This study aimed to develop a scoring model to predict ABA-HAP in patients with GNB-HAP.

Methods

A single center retrospective cohort study was performed among patients with HAP caused by GNB in our hospital during January 2019 to June 2019 (the derivation cohort, DC). The variables were assessed on the day when qualified respiratory specimens were obtained. A prediction score was formulated by using independent risk factors obtained from logistic regression analysis. It was prospectively validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019 (the validation cohort, VC).

Results

The final logistic regression model of DC included the following variables: transferred from other hospitals (3 points); blood purification (3 points); risk for aspiration (4 points); immunocompromised (3 points); pulmonary interstitial fibrosis (3 points); pleural effusion (1 points); heart failure (3 points); encephalitis (5 points); increased monocyte count (2 points); and increased neutrophils count (2 points). The AUROC of the scoring model was 0.845 (95% CI, 0.796 ~ 0.895) in DC and 0.807 (95% CI, 0.759 ~ 0.856) in VC. The scoring model clearly differentiated the low-risk patients (the score < 8 points), moderate-risk patients (8 ≤ the score < 12 points) and high-risk patients (the score ≥ 12 points), both in DC (P < 0.001) and in VC (P < 0.001).

Conclusion

This simple scoring model could predict ABA-HAP with high predictive value and help clinicians to choose appropriate empirical antibiotic therapy.

A Single-Center Evaluation of Extended Infusion Piperacillin/Tazobactam for Empiric Treatment in the Intensive Care Unit

Background: Piperacillin/tazobactam (PTZ) extended infusion (EI) is often used empirically in the intensive care unit (ICU). Gram-negative (GN) organisms with PTZ minimum inhibitory concentrations (MICs) >16/4 µg/mL are considered intermediate or resistant. Objective: The objective of this study was to evaluate MICs of GN isolates from the ICU to determine whether the hospital protocol for PTZ 3.375 g EI over 4 hours administered every 8 hours is an appropriate empiric regimen for ICU patients and to evaluate patient-specific risk factors associated with elevated MICs. Methods: All ICU patients admitted during 2017 with a confirmed GN organism from a non-urinary source were included for retrospective chart review. Patients with cystic fibrosis or cultures obtained >48 hours prior to ICU admission were excluded. Demographics, GN organism, culture source, risk factors for resistance, susceptibility profile, comorbidities, and creatinine clearance were collected. Appropriateness was defined as PTZ MIC ≤16/4 µg/mL in >80% of isolates. Results: Two hundred and thirty-one patients were included. The average patient was 56 years old. The majority of patients were white (64.1%) and male (69.7%). Pseudomonas aeruginosa (41%) was the most common organism isolated. Overall, 28% of GN isolates had MICs >16/4 µg/mL. Dialysis (P = .01), intravenous antibiotics within 90 days (P < .001), and presence of wounds/trauma (P = .01) were associated with elevated MICs. Conclusion: Current PTZ EI 3.375 g dosing regimens may not provide adequate empiric coverage for some GN organisms in ICU patients, especially for those who have previously received intravenous antibiotics, are on dialysis, or have wounds/trauma.

Recommendations for antibiotic selection for severe nosocomial infections

Severe infection and its evolution to sepsis are becoming more prevalent every day and are among the leading causes of critical illness and mortality. Proper management is crucial to improve prognosis. This document addresses three essential points that have a significant impact on this objective: a) early recognition of patients with sepsis criteria, b) identification of those patients who suffer from an infection and have a high risk of progressing to sepsis, and c) adequate selection and optimization of the initial antimicrobial treatment.

Risk factors and clinical impact of bacteremia due to carbapenem-nonsusceptible Enterobacteriaceae: A multicenter study in southern Taiwan

BACKGROUND

The emergence of carbapenem-non-susceptible Enterobacteriaceae (CnSE) infections is a public health threat. This study investigated the risk factors and clinical impact of bacteremia due to CnSE.

MATERIAL AND METHODS

The study was conducted at three hospitals in southern Taiwan between January 1, 2017, and October 31, 2019. Only the first episode of CnSE bacteremia from each adult was included. For one episode of CnSE bacteremia, two subsequent bacteremic episodes due to carbapenem-susceptible Enterobacteriaceae isolates in each hospital were included as the controls.

RESULTS

Among a total of 641 episodes of monomicrobial Enterobacteriaceae bacteremia were noted, 47 (7.3%) of which were of CnSE bacteremia. Ninety-four episodes of carbapenem-susceptible Enterobacteriaceae (CSE) bacteremia were selected as the controls for further analyses. In the multivariate analysis, hypertension (odds ratio [OR], 4.21; P = 0.005), Pitt bacteremia score (OR, 1.61; P = 0.002), and nosocomial bacteremia (OR, 3.30; P = 0.01) were associated with carbapenem nonsusceptibility among Enterobacteriaceae bacteremia. The most abundant CnSE isolate was Klebsiella pneumoniae (91.5%), followed by Klebsiella oxytoca (6.4%) and Escherichia coli (2.1%). Patients with CnSE bacteremia had a higher overall in-hospital mortality rate than those with CSE bacteremia (53.2% vs. 23.4%, P = 0.001). Moreover, in the multivariate analysis, the in-hospital mortality was significantly associated with higher Pitt bacteremia score (OR, 1.38; P = 0.02) and marginally associated with CnSE infections (OR, 2.44; P = 0.06).

CONCLUSION

Among adults with Enterobacteriaceae bacteremia, carbapenem nonsusceptibility, male sex, and the presence of hypertension or chronic kidney disease indicate a poor prognosis during hospitalization.

Clinical Impact and Risk Factors of Nonsusceptibility to Third-Generation Cephalosporins Among Hospitalized Adults with Monomicrobial Enterobacteriaceae Bacteremia in Southern Taiwan: A Multicenter Study

Background

Reducing the effectiveness of broad-spectrum cephalosporins against Enterobacteriaceae infections has been recognized. This study aimed to investigate risk factors and clinical significance of third-generation cephalosporin nonsusceptibility (3GC-NS) among the cases of monomicrobial Enterobacteriaceae bacteremia (mEB) at regional or district hospitals.

Methods

The study was conducted at three hospitals in southern Taiwan between Jan. 2017 and Oct. 2019. Only the first episode of mEB from each adult (aged ≥20 years) was included. The primary outcome was in-hospital crude mortality.

Results

Overall there were 499 episodes of adults with mEB included, and their mean age was 74.5 years. Female predominated, accounting for 53% of all patients. Escherichia coli (62%) and Klebsiella pneumoniae (21%) were two major causative species. The overall mortality rate was 15% (73/499), and patients infected by 3GC-NS isolates (34%, 172/499) had a higher mortality rate than those by 3GC-susceptible isolates (66%, 327/499) (21% vs 11%, P=0.005). By the multivariate analysis, 3GC-NS was the only independent prognostic determinant (adjusted odds ratio [AOR], 1.78; P=0.04). Of note, male (AOR 2.02, P=0.001), nosocomial-acquired bacteremia (AOR 2.77, P<0.001), and usage of nasogastric tube (AOR 2.01, P=0.002) were positively associated with 3GC-NS, but P. mirabilis bacteremia (AOR 0.28, P=0.01) and age (AOR 0.98, P=0.04) negatively with 3GC-NS.

Conclusion

For adults with Enterobacteriaceae bacteremia, 3GC-NS signifies a significant prognostic impact. Efforts to rapid identification of such antimicrobial resistance profiles should be incorporated into antimicrobial stewardship programs to achieve favorable outcomes.

Risk stratification for multidrug-resistant Gram-negative infections in ICU patients

PURPOSE OF REVIEW

Antimicrobial resistance among Gram-negative microorganisms has alarmingly increased in the past 10 years worldwide. Infections caused by these microorganisms are difficult to treat, especially in critically ill patients.The present review examines how to accurately predict which patients carry a greater risk of colonization or infection on which to base the timely choice of an effective empirical antibiotic treatment regimen and avoid antibiotic overuse.

RECENT FINDINGS

There are many risk factors for acquiring one of many multidrug-resistant Gram-negative microorganisms (MDR-GN); however, scores anticipating colonization, infection among those colonized, or mortality among those infected have a variable accuracy. Accuracy of scores anticipating colonization is low. Scores predicting infections among colonized patients are, in general, better, and ICU patients infected with MDR-GN have a worse prognosis than those infected by non-resistant microorganisms. Scores are, in general, better at excluding patients.

SUMMARY

Despite these limitations, scores continue to gain popularity including those by Giannella, Tumbarello, Johnson, or the scores INCREMENT carbapenem-producing Enterobacteriaceae score, Cano, Tartof, or CarbaSCORE.

Validating a prediction tool to determine the risk of nosocomial multidrug-resistant Gram-negative bacilli infection in critically ill patients: A retrospective case-control study

BACKGROUND

The Singapore GSDCS score was developed to enable clinicians predict the risk of nosocomial multidrug-resistant Gram-negative bacilli (RGNB) infection in critically ill patients. We aimed to validate this score in a UK setting.

METHOD

A retrospective case-control study was conducted including patients who stayed for more than 24h in intensive care units (ICUs) across two tertiary National Health Service hospitals in London, UK (April 2011-April 2016). Cases with RGNB and controls with sensitive Gram-negative bacilli (SGNB) infection were identified.

RESULTS

The derived GSDCS score was calculated from when there was a step change in antimicrobial therapy in response to clinical suspicion of infection as follows: prior Gram-negative organism, Surgery, Dialysis with end-stage renal disease, prior Carbapenem use and intensive care Stay of more than 5 days. A total of 110 patients with RGNB infection (cases) were matched 1:1 to 110 geotemporally chosen patients with SGNB infection (controls). The discriminatory ability of the prediction tool by receiver operating characteristic curve analysis in our validation cohort was 0.75 (95% confidence interval 0.65-0.81), which is comparable with the area under the curve of the derivation cohort (0.77). The GSDCS score differentiated between low- (0-1.3), medium- (1.4-2.3) and high-risk (2.4-4.3) patients for RGNB infection (P<0.001) in a UK setting.

CONCLUSION

A simple bedside clinical prediction tool may be used to identify and differentiate patients at low, medium and high risk of RGNB infection prior to initiation of prompt empirical antimicrobial therapy in the intensive care setting.

Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data

Background

Early detection of antimicrobial resistance in pathogens and prescription of more effective antibiotics is a fast-emerging need in clinical practice. High-throughput sequencing technology, such as whole genome sequencing (WGS), may have the capacity to rapidly guide the clinical decision-making process. The prediction of antimicrobial resistance in Gram-negative bacteria, often the cause of serious systemic infections, is more challenging as genotype-to-phenotype (drug resistance) relationship is more complex than for most Gram-positive organisms.

Methods and Findings

We have used NCBI BioSample database to train and cross-validate eight XGBoost-based machine learning models to predict drug resistance to cefepime, cefotaxime, ceftriaxone, ciprofloxacin, gentamicin, levofloxacin, meropenem, and tobramycin tested in Acinetobacter baumannii, Escherichia coli, Enterobacter cloacae, Klebsiella aerogenes, and Klebsiella pneumoniae. The input is the WGS data in terms of the coverage of known antibiotic resistance genes by shotgun sequencing reads. Models demonstrate high performance and robustness to class imbalanced datasets.

Conclusion

Whole genome sequencing enables the prediction of antimicrobial resistance in Gram-negative bacteria. We present a tool that provides an in silico antibiogram for eight drugs. Predictions are accompanied with a reliability index that may further facilitate the decision making process. The demo version of the tool with pre-processed samples is available at https://vancampn.shinyapps.io/wgs2amr/. The stand-alone version of the predictor is available at https://github.com/pieterjanvc/wgs2amr/.

Contamination of the Surfaces of a Health Care Environment by Multidrug-Resistant (MDR) Bacteria

Nosocomial infections (NIs) are known worldwide and remain a major problem despite scientific and technical advances in the field of health. The severity of the infection depends on the characteristics of the microorganisms involved and the high frequency of resistant pathogens in the hospital environment. The aim of this study is to determine the distribution of pathogenic bacteria (and their resistance to antibiotics) that spread on hospital surfaces, more specifically, on those of various departments in the Provincial Hospital Center (PHC) of Mohammedia, Morocco. A cross-sectional study was conducted from March 2017 to April 2018. Samples were collected by swabbing the hospital surfaces, and the isolated bacteria were checked for their susceptibility to antibiotics by the Kirby-Bauer disk diffusion method following the standards of the Clinical and Laboratory Standards Institute (CLSI). Among 200 swab samples, 176 (88%) showed bacterial growth. Gram-negative isolates were predominant at 51.5% (101/196), while the Gram-positives were at 48.5% (95/196). The main isolates are Enterobacteria weighted at 31.6% (62/196), Staphylococcus aureus reaching 24% (47/196), Pseudomonas aeruginosa at 9.2% (18/196), and Acinetobacter spp. with 3.3% (6/196). Moreover, the antimicrobial susceptibility profile of the isolates showed that about 31.7% (32/101) of the Gram-negative isolates were found to be MDR. This resistance is also high among isolates of S. aureus of which 44.7% (20/47) were methicillin-resistant Staphylococcus aureus (MRSA). Contamination of hospital surfaces by MDR bacteria is a real danger to public health. The concept of environmental bacterial reservoir is a reality that requires strict compliance with current guidelines and recommendations for hand hygiene, cleaning, and disinfection of surfaces in hospitals.

Development of a bedside tool to predict the probability of drug-resistant pathogens among hospitalized adult patients with gram-negative infections

Background

We developed a clinical bedside tool to simultaneously estimate the probabilities of third-generation cephalosporin-resistant Enterobacteriaceae (3GC-R), carbapenem-resistant Enterobacteriaceae (CRE), and multidrug-resistant Pseudomonas aeruginosa (MDRP) among hospitalized adult patients with Gram-negative infections.

Methods

Data were obtained from a retrospective observational study of the Premier Hospital that included hospitalized adult patients with a complicated urinary tract infection (cUTI), complicated intra-abdominal infection (cIAI), hospital-acquired/ventilator-associated pneumonia (HAP/VAP), or bloodstream infection (BSI) due to Gram-negative bacteria between 2011 and 2015. Risk factors for 3GC-R, CRE, and MDRP were ascertained by multivariate logistic regression, and separate models were developed for patients with community-acquired versus hospital-acquired infections for each resistance phenotype (N = 6). Models were converted to a singular user-friendly interface to estimate the probabilities of a patient having an infection due to 3GC-R, CRE, or MDRP when ≥ 1 risk factor was present.

Results

Overall, 124,068 patients contributed to the dataset. Percentages of patients admitted for cUTI, cIAI, HAP/VAP, and BSI were 61.6, 4.6, 16.5, and 26.4%, respectively (some patients contributed > 1 infection type). Resistant infection rates were 1.90% for CRE, 12.09% for 3GC-R, and 3.91% for MDRP. A greater percentage of the resistant infections were community-acquired relative to hospital-acquired (CRE, 1.30% vs 0.62% of 1.90%; 3GC-R, 9.27% vs 3.42% of 12.09%; MDRP, 2.39% vs 1.59% of 3.91%). The most important predictors of having an 3GC-R, CRE or MDRP infection were prior number of antibiotics; infection site; infection during the previous 3 months; and hospital prevalence of 3GC-R, CRE, or MDRP. To enable application of the six predictive multivariate logistic regression models to real-world clinical practice, we developed a user-friendly interface that estimates the risk of 3GC-R, CRE, and MDRP simultaneously in a given patient with a Gram-negative infection based on their risk (Additional file 1).

Conclusions

We developed a clinical prediction tool to estimate the probabilities of 3GC-R, CRE, and MDRP among hospitalized adult patients with confirmed community- and hospital-acquired Gram-negative infections. Our predictive model has been implemented as a user-friendly bedside tool for use by clinicians/healthcare professionals to predict the probability of resistant infections in individual patients, to guide early appropriate therapy.

Electronic supplementary material

The online version of this article (10.1186/s12879-019-4363-y) contains supplementary material, which is available to authorized users.

Incidence and Risk Factors Associated with Multi-Drug-Resistant Pathogens in a Critically Ill Trauma Population: A Retrospective Cohort Study

Background: Multi-drug resistance is considered a serious health threat particularly in the intensive care unit (ICU) setting. Studies evaluating multi-drug-resistant (MDR) pathogens in critically ill trauma patients are limited. The objectives were to describe the incidence of MDR, extensive-drug-resistant (XDR), and pan-drug-resistant (PDR) organism growth in ICU patients admitted with traumatic injuries and to identify any risk factors associated with MDR growth. Patients and Methods: This was a retrospective single-center cohort study of all ICU adult patients identified via the institution's trauma registry from January 1, 2016 to August 31, 2017. Patients were included if they had positive culture growth with susceptibility data taken during the index hospitalization. Patients were excluded if their cultures were drawn within 48 hours of emergency department triage. Study groups were defined based on the presence of at least one MDR pathogen during the index hospitalization. Results: A total of 2,578 charts were reviewed and 95 patients (mean age, 60 years; 66 males [69%]) with 201 total cultures were included. The majority of positive cultures were from respiratory (69%) and urinary (16%) sources. Of the 201 positive cultures, the majority of species identified was Enterobacteriaceae (47%), Staphylococcus (32%), Enterococcus (7%), Acinetobacter (5%), and Pseudomonas (3%). Of the 95 patients with positive cultures, the incidence of MDR, XDR, and PDR organisms was found to be 31%, 17%, and 0%, respectively. Augmented renal clearance (ARC) was the only risk factor associated with an increased risk for MDR organism growth (adjusted odds ratio 9.78, 95% confidence interval [CI] 2.56-37.41; p = 0.001). Conclusions: In this cohort of critically ill trauma patients, the incidence of an MDR pathogen occurred in 31% of patients. This is the first study to find an association of ARC and multi-drug resistance, which should be further validated as a potential cause for MDR organisms.

Derivation and validation of a simple score to predict the presence of bacteria requiring carbapenem treatment in ICU-acquired bloodstream infection and pneumonia: CarbaSCORE

Background

The recommendations of learned societies mention risk factors for the presence of multidrug resistant bacteria in hospital-acquired infections, but they do not propose a scoring system to guide empiric antibiotic therapy. Our study was aimed at developing a simple score for predicting "the presence of bacteria requiring carbapenem treatment" in ICU-acquired bloodstream infection and pneumonia.

Methods

Between December 2011 and January 2015, we conducted a retrospective study using a prospectively collected French database of nosocomial infections in the polyvalent intensive care unit of a French university hospital. All patients with ICU-acquired bloodstream infection or pneumonia were included in the study. Bivariate and multivariate analyses were performed to develop the CarbaSCORE, and this score was internally validated.

Results

In total, 338 patients were analyzed, including 27 patients requiring carbapenem treatment. The CarbaSCORE was composed of four criteria: "presence of bloodstream infection" (as opposed to pneumonia) scored 2 points, "chronic hemodialysis" scored 4 points, "travel abroad in the last 6 months" scored 5 points, and "MDR-colonization or prior use of a β-lactam of class ≥ 3" scored 6 points. Internal validation by bootstrapping showed an area under the receiver operating characteristic curve of 0.81 [0.73-0.89]. Sensitivity was 96% at the 6-point threshold and specificity was 91% at the 9-point threshold.

Conclusions

The CarbaSCORE is a simple and efficient score for predicting the presence of bacteria requiring carbapenem treatment. Further studies are needed to test this score before it can be used in practice.

Influence of antibiotic pressure on multi-drug resistant Klebsiella pneumoniae colonisation in critically ill patients

Background

The aim of this study is to evaluate the risk factors for colonisation by multidrug resistant (MDR) K. pneumoniae in a critical care unit and the relationship between colonisation and the antibiotic pressure exerted by the antimicrobial treatments received by patients.

Methods

A prospective observational was designed. Patients admitted for more than 48 h to an intensive care unit were included. Samples for surveillance cultures were obtained from all the patients upon admission and once a week. The association between risk factors and colonisation by MDR K. pneumoniae was determined by logistic regression. A Cox regression model was used to evaluate the effect of the use of antimicrobials on the colonisation rate. An ARMIA model was used to investigate the association between the incidence of colonisation by MDR strains and the global consumption of antimicrobials in the unit.

Results

One thousand seven hundred twenty-five patients were included, from which 308 (17.9%) were positive for MDR K. pneumoniae. In the multivariate analysis, hospitalisation for longer than 7 days together with respiratory infection and administration of any antibiotic was associated with increased MR K. pneumoniae colonisation. Patients who received antibiotics for more than 48 h were colonised earlier than patients who did not receive antibiotic treatment [HR: 2.16 (95%CI:1.55–3.03)]. The ARIMA model found a significant association between the monthly colonisation rate for MR K. pneumoniae and the consumption of cephalosporins and carbapenems in the previous month.

Conclusion

Individual antibiotic administration and the global antibiotic pressure of cephalosporins and carbapenems are associated to an increased colonisation by MDR K. pneumoniae strains.

Disparity of the "screen-and-isolate" policy for multidrug-resistant organisms: A national survey in French adult ICUs

BACKGROUND

The prevalence of multidrug-resistant organisms (MDROs) has dramatically increased. The aim of this survey was to describe and analyze the different screening and isolation policies regarding MDROs in French adult intensive care units (ICUs).

MATERIALS AND METHODS

A multicenter online survey was performed among French ICUs, including 63 questions distributed into 4 parts: characteristics of the unit, MDRO screening policy, policy regarding contact precautions, and ecology of the unit.

RESULTS

From April 2015 to June 2016, 73 of 301 ICUs (24%) participated in the survey. MDRO screening was performed on admission in 96% of ICUs, for at least 1 MDRO (78%). MDRO screening was performed weekly during ICU stay in 83% of ICUs. Preemptive isolation was initiated on admission in 82% of ICUs, mostly in a targeted way (71%). Imported and acquired MDRO rates >10% were reported in 44% and 27% of ICUs, respectively. An MDRO outbreak had occurred within the past 3 years in 48% of cases.

CONCLUSION

French ICUs have variable screening and isolation approaches for MDROs, as up to 10 combinations were met. Discrepancies with the 2009 national guidelines were observed. Very few ICUs practice without some form of screening and isolation of patients upon admission.

Risk Factors for Colistin Resistance among Gram-Negative Rods and Klebsiella pneumoniae Isolates

Infections due to colistin-resistant (Col^r) Gram-negative rods (GNRs) and colistin-resistant Klebsiella pneumoniae isolates in particular result in high associated mortality and poor treatment options. To determine the risk factors for recovery on culture of Col^r GNRs and Col^rK. pneumoniae, analyses were chosen to aid decisions at two separate time points: the first when only Gram stain results are available without any bacterial species information (corresponding to the Col^r GNR model) and the second when organism identification is performed but prior to reporting of antimicrobial susceptibility testing results (corresponding to the Col^rK. pneumoniae model). Cases were retrospectively analyzed at a major academic hospital system from 2011 to 2016. After excluding bacteria that were intrinsically resistant to colistin, a total of 28,512 GNR isolates (4,557 K. pneumoniae isolates) were analyzed, 128 of which were Col^r (i.e., MIC > 2 μg/ml), including 68 of which that were Col^rK. pneumoniae In multivariate analysis, risk factors for Col^r GNRs were neurologic disease, residence in a skilled nursing facility prior to admission, receipt of carbapenems in the last 90 days, prior infection with a carbapenem-resistant organism, and receipt of ventilatory support (c-statistic = 0.81). Risk factors for Col^rK. pneumoniae specifically were neurologic disease, residence in a skilled nursing facility prior to admission, receipt of carbapenems in the last 90 days, receipt of an anti-methicillin-resistant Staphylococcus aureus antimicrobial in the last 90 days, and prior infection with a carbapenem-resistant organism (c-statistic = 0.89). A scoring system derived from these models can be applied by providers to guide empirical antimicrobial therapy in patients with infections with suspected Col^r GNR and Col^rK. pneumoniae isolates.

Differential characteristics of Acinetobacter baumannii colonization and infection: risk factors, clinical picture, and mortality

Objectives

The objectives of this study were to detect those characteristics that were specifically associated with infection or colonization by Acinetobacter baumannii, describe the clinical manifestations of those patients in whom the infection was detected in intensive care unit (ICU) or non-ICU wards, and analyze the prognosis-associated factors in patients from whom A. baumannii was isolated.

Patients and methods

A sample of 122 patients from whom A. baumannii was recovered during an endemic period in a teaching hospital was included. Only those cases in which A. baumannii was recovered as the unique microbe were considered. Demographic data; ward of admission; intrinsic and extrinsic risk factors for infection or colonization; chronic underlying condition severity, as evaluated by the McCabe classification or Charlson index and Acute Physiology and Chronic Health Evaluation (APACHE) II score; and clinical manifestations were analyzed to differentiate specific characteristics of colonized or infected patients. Factors independently associated with the mortality at 30 days were also analyzed by Cox regression.

Results

A total of 73 (60%) patients were colonized and 49 (40%) individuals were infected with A. baumannii. A non-fatal McCabe class (when compared to ultimately and rapidly fatal), days of hospitalization prior to isolation of A. baumannii, and present ICU admission were associated with the diagnosis of infection. The more frequent clinical picture was respiratory infection (tracheobronchitis, 16 [33%] cases; pneumonia, 27 [55%] cases). Mortality at 30 days was 24% (n=29). A non-fatal McCabe class (Exp[B] 2.44, 95% confidence interval [CI] 1.05–5.66, p=0.039) and the absence of infection (Exp[B] 2.75, 95% CI 1.18–6.38, p=0.019) were independently associated with survival.

Conclusion

Parameters associated with infection by A. baumannii in an endemic situation are the admission at ICU and the number of days of hospitalization. Mortality of patients from whom A. baumannii was isolated was independently influenced by the chronic underlying basal state and the presence of infection by A. baumannii.

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.

Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections

OBJECTIVES

Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia.

METHODS

We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%).

RESULTS

A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy.

CONCLUSIONS

Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation.

SUMMARY

Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models.

Emerging and re-emerging infectious disease in otorhinolaryngology

SUMMARY

Emerging and re-emerging infectious disease in otorhinolaryngology (ENT) are an area of growing epidemiological and clinical interest. The aim of this section is to comprehensively report on the epidemiology of key infectious disease in otorhinolaryngology, reporting on their burden at the national and international level, expanding of the need of promoting and implementing preventive interventions, and the rationale of applying evidence-based, effective and cost- effective diagnostic, curative and preventive approaches. In particular, we focus on i) ENT viral infections (HIV, Epstein-Barr virus, Human Papilloma virus), retrieving the available evidence on their oncogenic potential; ii) typical and atypical mycobacteria infections; iii) non-specific granulomatous lymphadenopathy; iv) emerging paediatric ENT infectious diseases and the prevention of their complications; v) the growing burden of antimicrobial resistance in ENT and the strategies for its control in different clinical settings. We conclude by outlining knowledge gaps and action needed in ENT infectious diseases research and clinical practice and we make references to economic analysis in the field of ENT infectious diseases prevention and care.

Risk factors for multidrug-resistant Gram-negative bacteria infection in intensive care units: A meta-analysis

AIMS

To identify the risk factors for multidrug-resistant Gram-negative bacteria systematically and provide suggestions and an evidence-base for clinical measures.

BACKGROUND

With the increase in the social population, changes in human behaviour and ecosystems, as well as economic development, bacteria have gradually produced drug resistance genes. These have swept through intensive care units causing high mortality.

METHODS

Relevant literature which included case-control and cohort studies published from January 1999 to March 2017 were searched in the Cochrane Library, PubMed, Web of Science, and Medline. Meta-analysis was performed by using StataSE version 12.0 software.

RESULTS

Eighteen studies of 235 publications were eligible. Male gender (OR 1.40, 95%CI 1.09, 1.80), having an operative procedure (OR 1.31, 95%CI 1.10, 1.56), a central venous catheter (OR 1.22, 95%CI 1.01, 1.48), mechanical ventilation (OR 1.25, 95%CI 1.07, 1.46), previous antibiotic therapy (OR 1.66, 95%CI 1.41, 1.96), length of ICU stay (weighted mean difference 8.18, 95%CI 0.27, 16.10), and types of health-associated infections were the identified risk factors for multidrug-resistant Gram-negative bacterial infection in intensive care units; moreover, diabetes mellitus was not.

CONCLUSION

Six risk factors were associated with multidrug-resistant Gram-negative bacterial infection in intensive care units. Antimicrobial stewardship, infection control, and medical staff prevention care are needed.

Chloramphenicol-borate/boronate complex for controlling infections by chloramphenicol-resistant bacteria

Increasing bacterial resistance to antibiotics is a pressing problem worldwide, with many health organisations prioritizing this issue. Whilst there is a desperate need for new effective antimicrobials, it is also important to understand the mechanisms and epidemiology of the resistant pathogens currently present in the community. Chloramphenicol is one such well known antibiotic which had lost its efficacy due to bacterial resistance. In this paper, we report the design, synthesis, and bio-studies of novel chloramphenicol-borate/boronate derivatives which showed the ability to control the infections caused by chloramphenicol-resistant bacteria. Activity profiling against P. aeruginosa strain EXR1 with catB gene indicated the inability of acetyl transferase to acetylate the chloramphenicol-borate/boronate complex, unlike chloramphenicol. Results obtained from the antimicrobial assays were further rationalized by molecular docking studies. The latter revealed that the probable reason for the enhanced antibacterial activity may be attributed to the change in the binding site of chloramphenicol-borate/boronate with chloramphenicol acetyl transferase (CAT) with respect to chloramphenicol itself. Hemolytic and genotoxic studies established the reduced toxicity of these synthetic derivatives with respect to chloramphenicol.

We report the design, synthesis, and bio-studies of novel chloramphenicol-borate/boronate derivatives which could control the infections caused by chloramphenicol-resistant bacteria.

Predicting Multidrug-Resistant Gram-Negative Bacterial Colonization and Associated Infection on Hospital Admission

OBJECTIVE

Isolation of multidrug-resistant gram-negative bacteria (MDR-GNB) from patients in the community has been increasingly observed. A prediction model for MDR-GNB colonization and infection risk stratification on hospital admission is needed to improve patient care.

METHODS

A 2-stage, prospective study was performed with 995 and 998 emergency department patients enrolled, respectively. MDR-GNB colonization was defined as isolates resistant to 3 or more classes of antibiotics, identified in either the surveillance or early (≤48 hours) clinical cultures.

RESULTS

A score-assigned MDR-GNB colonization prediction model was developed and validated using clinical and microbiological data from 995 patients enrolled in the first stage of the study; 122 of these patients (12.3%) were MDR-GNB colonized. We identified 5 independent predictors: age>70 years (odds ratio [OR], 1.84 [95% confidence interval (CI), 1.06–3.17]; 1 point), assigned point value in the model), residence in a long-term-care facility (OR, 3.64 [95% CI, 1.57–8.43); 3 points), history of cerebrovascular accidents (OR, 2.23 [95% CI, 1.24–4.01]; 2 points), hospitalization within 1 month (OR, 2.63 [95% CI, 1.39–4.96]; 2 points), and recent antibiotic exposure (OR, 2.18 [95% CI, 1.16–4.11]; 2 points). The model displayed good discrimination in the derivation and validation sets (area under ROC curve, 0.75 and 0.80, respectively) with the best cutoffs of<4 and ≥4 points for low- and high-risk MDR-GNB colonization, respectively. When applied to 998 patients in the second stage of the study, the model successfully stratified the risk of MDR-GNB infection during hospitalization between low- and high-risk groups (probability, 0.02 vs 0.12, respectively; log-rank test, P<.001).

CONCLUSION

A model was developed to optimize both the decision to initiate antimicrobial therapy and the infection control interventions to mitigate threats from MDR-GNB.

Infect Control Hosp Epidemiol 2017;38:1216–1225

Development and evaluation of the automated risk assessment system for multidrug-resistant organisms (autoRAS-MDRO)

BACKGROUND

A high proportion of infections acquired in hospitals are caused by multidrug-resistant organisms (MDROs). The priority in MDRO prevention is to detect high-risk patients and implement preventive intervention as soon as possible.

AIM

To develop an automated risk assessment system for MDROs (autoRAS-MDRO) to screen for patients at MDRO infection risk and evaluate the predictive validity of the autoRAS-MDRO.

METHODS

Data for 4200 variables were extracted from the electronic health records (EHRs) for constructing the MDRO risk-scoring algorithm, which was based on a logistic regression model. The autoRAS-MDRO was designed such that the MDRO risk classification (high, moderate, low risk) could be automatically displayed on the nursing Kardex screen in the EHRs system. For the development of the MDRO risk-scoring algorithm, 1000 patients with MDROs and 4000 patients without MDROs were selected; similarly, for the evaluation, 2173 and 8692 patients with and without MDROs, respectively, were selected.

FINDINGS

The predictive validity of the autoRAS-MDRO was as follows: (i) at the 6-month evaluation: sensitivity, 81%; specificity, 79%; positive predictive value (PPV), 49%; negative predictive value (NPV), 94%; and Youden index, 0.60; (ii) at the 12-month evaluation: sensitivity 79%, specificity 78%, PPV 47%, NPV 94%, and Youden index, 0.57.

CONCLUSION

The autoRAS-MDRO had moderate predictive validity. It could be useful in redirecting nurses' time and efforts required for MDRO risk assessment and implementation of infection control measures, and in reducing the incidence of MDRO infection in hospitals, thereby contributing to patient safety.

Modeling risk for developing drug resistant bacterial infections in an MDR-naive critically ill population

PURPOSE

To create a model predictive of an individual's risk of developing a de novo multidrug-resistant (MDR) infection while in the intensive care unit (ICU).

METHODS

This is a case-control study in which 189 ICU patients diagnosed with their first infection with an MDR organism were compared on the basis of demographic, past medical and clinical variables to randomly selected ICU patients without such an infection, era-matched in a 2:1 ratio. A prediction tool was derived using multivariate logistic regression.

RESULTS

Five features remained predictive of developing an infection with a drug-resistant pathogen: hospitalization within a year [adjusted odds ratio (OR) 2.14], chronic hemodialysis (3.86), underlying oxygen-dependent pulmonary disease (1.86), endotracheal intubation within 24 h (2.46) and reason for ICU admission (respiratory failure 2.89, non-respiratory failure, non-shock presentation 1.85). Using a scoring system (0-7 points) based on the adjusted OR, risk categories were derived (low: 0-2 points, intermediate: 3-4 points and high risk: 5-7 points). The negative predictive value at a score cutoff of 2 is excellent (88.9%).

CONCLUSIONS

A clinical prediction rule comprised of five easily measured ICU variables reasonably discriminates between patients who will develop their first MDR infection versus those who will not.

Universal versus targeted additional contact precautions for multidrug-resistant organism carriage for patients admitted to an intensive care unit

BACKGROUND

Although additional contact precautions (ACPs) are routinely used to reduce cross-transmission of multidrug-resistant organisms (MDROs), the relevance of isolation precautions remains debated. We hypothesized that the collection of recognized risk factors for MDRO carriage on intensive care unit (ICU) admission might be helpful to target ACPs without increasing MDRO acquisition during ICU stays, compared with universal ACPs.

MATERIALS AND METHODS

This is a sequential single-center observational study performed in consecutive patients admitted to a French medical and surgical ICU. During the first 6-month period, screening for MDRO carriage and ACPs were performed in all patients. During the second 6-month period, screening was maintained, but ACP use was guided by the presence of at least 1 defined risk factor for MDRO.

RESULTS

During both periods, 33 (10%) and 30 (10%) among 327 and 297 admissions were, respectively, associated with a positive admission MDRO carriage. During both periods, a second screening was performed in 147 (45%) and 127 (43%) patients. Altogether, the rate of acquired MDRO (positive screening or clinical specimen) was similar during both periods (10% [n = 15] and 11.8% [n = 15], respectively; P = .66).

CONCLUSIONS

The results of our study contribute to support the safety of an isolation-targeted screening policy on ICU admission compared with universal screening and isolation regarding the rate of ICU-acquired MDRO colonization or infection.

Prevalence and antimicrobial susceptibility of non-fermenting Gram-negative bacilli isolated from clinical samples at a tertiary care hospital

INTRODUCTION:

We compared the prevalence and antimicrobial susceptibility of non-fermenting gram-negative bacilli (NFGNB) isolated from clinical samples at a Brazilian tertiary care hospital in 2008 and 2013.

METHODS:

Collected data included patient's name, age, sex, inpatient unit, laboratory record number, type of biological material, culture test result, and antimicrobial susceptibility of isolated strains.

RESULTS:

Out of 19,112 culture tests analyzed, 926 (4.8%) were positive for NFGNB. Among these, 45.2% were metallo-beta-lactamase (MBL) producing strains.

CONCLUSION:

Between 2008 and 2013, the number of MBL-producing NFGNB isolates increased by 21.5%, which was accompanied by a consequent reduction in susceptibility to antimicrobials.

Microbiological sentinel events at a neurological hospital: a retrospective cohort study

The purpose of this study is to describe the epidemiological surveillance of microbiological sentinel events (SEs) carried out between 2012 and 2014 at the Neurological Hospital Carlo Besta, Milano, Italy. The setting is inpatient care with multidrug-resistant infections. The aim of the procedure is to formalize the management mode, reporting and transmission of SEs. Categorical variables were described by counts and percentages, as mean and sd or median and interquartile range. The incidence rates of SE were calculated per 1000 patient-days and for 100 admissions using Poisson distribution. The incidence rate of isolation for 1000 patient-days varies from a minimum of 0.52 (95 % confidence interval, 0.23-1.15) for the second quarter of 2014 to a maximum value of 4.16 (95 % confidence interval, 3.20-5.40) for the first quarter of 2013. A decrease followed from the third quarter of 2013 that remained constant in 2014, reaching values similar to those of 2012. Preventive actions and their effectiveness on Acinetobacterbaumannii, the primary cause in our division of multidrug-resistant infections in 2012, have ensured a reduction of the incidence of the same; preventive actions and their effectiveness allowed us to intercept microbiological SE and trigger appropriate precautionary behaviour and isolation. Surveillance of healthcare-associated infections is fundamental in understanding the sources that are contributing to the growing reservoir within hospital communities.

Molecular epidemiology of environmental and clinical carbapenemase-producing Gram-negative bacilli from hospitals in Guelma, Algeria: Multiple genetic lineages and first report of OXA-48 in Enterobacter cloacae

This study was designed to investigate environmental colonisation in Algerian hospitals by carbapenem-resistant Gram-negative bacilli (GNB), including molecular characterisation of their resistance, and to perform a comparative molecular analysis between clinical and environmental strains. GNB isolated from hospitalised patients and the hospital environment were identified using microbiological methods and matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS). Antibiotic susceptibility testing was performed by disk diffusion and Etest methods. Carbapenemase- and extended-spectrum β-lactamase (ESBL)-encoding genes were searched for using PCR and sequencing. Clonality of the environmental and clinical strains was assessed by multilocus sequencing typing (MLST). A total of 32 carbapenem-resistant GNB were isolated, including 16 (29%) of 56 multidrug-resistant (MDR) GNB from clinical specimens and 16 (48%) of 33 MDR-GNB from inanimate surfaces. Of the 32 carbapenem-resistant isolates, 14 produced a carbapenemase. The bla_OXA-48 gene was detected both in clinical and surface isolates of Klebsiella pneumoniae (n=3) and Enterobacter cloacae (n=2). Clinical and surface isolates of Acinetobacter baumannii were found to produce the carbapenemases NDM-1 (7 isolates) and OXA-23 (2 isolates). MLST revealed clonal diversity and a relationship between environmental and clinical strains with identical sequence types. Here we report the first description of an OXA-48-producing E. cloacae isolate in Algeria. We also highlight the important role of inanimate surfaces in the spread of carbapenem-resistant bacteria and the emergence of nosocomial infections.

Carbapenem-Resistant Non-Glucose-Fermenting Gram-Negative Bacilli: the Missing Piece to the Puzzle

The non-glucose-fermenting Gram-negative bacilli Pseudomonas aeruginosa and Acinetobacter baumannii are increasingly acquiring carbapenem resistance. Given their intrinsic antibiotic resistance, this can cause extremely difficult-to-treat infections. Additionally, resistance gene transfer can occur between Gram-negative species, regardless of their ability to ferment glucose. Thus, the acquisition of carbapenemase genes by these organisms increases the risk of carbapenemase spread in general. Ultimately, infection control practitioners and clinical microbiologists need to work together to determine the risk carried by carbapenem-resistant non-glucose-fermenting Gram-negative bacilli (CR-NF) in their institution and what methods should be considered for surveillance and detection of CR-NF.

The costs of nosocomial resistant gram negative intensive care unit infections among patients with the systemic inflammatory response syndrome- a propensity matched case control study

Background

Infections due to multi-drug resistant gram negative bacilli (RGNB) in critically ill patients have been reported to be associated with increased morbidity and costs and only a few studies have been done in Asia. We examined the financial impact of nosocomial RGNB infections among critically ill patients in Singapore.

Methods

A nested case control study was done for patients at medical and surgical ICUs of a tertiary university hospital (August 2007-December 2011) matched by propensity scores. Two groups of propensity-matched controls were selected for each case patient with nosocomial drug resistant gram negative infection: at-risk patients with no gram negative infection or colonization (Control A) and patients with ICU acquired susceptible gram negative infection (SGNB) (Control B). The costs of the hospital stay, laboratory tests and antibiotics prescribed as well as length of stay were compared using the Wilcoxon matched-pairs signed rank test.

Results

Of the 1539 patients included in the analysis, 76 and 65 patients had ICU acquired RGNB and SGNB infection respectively. The median(range) total hospital bill per day for patients with RGNB infection was 1.5 times higher than at-risk patients without GNB infection [Singapore dollars 2637.8 (458.7-20610.3) vs. 1757.4 (179.9-6107.4), p0.0001]. The same trend was observed when compared with SGNB infected patients. The median costs per day of antibiotics and laboratory investigations were also found to be significantly higher for patients with RGNB infection. The length of stay post infection was not found to be different between those infected with RGNB and SGNB.

Conclusion

The economic burden of RGNB infections to the patients and the hospital is considerable. Efforts need to be taken to prevent their occurrence by cost effective infection control practices.