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

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

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

Crossroads of Antimicrobial and Diagnostic Stewardship: Assessing Risks to Develop Clinical Decision Support to Combat Multidrug-Resistant Pseudomonas

Background

Early detection of multidrug-resistant Pseudomonas aeruginosa (MDRP) remains challenging. Existing risk prediction tools are difficult to translate to bedside application. The goal of this study was to develop a simple electronic medical record (EMR)-integrated tool for prediction of MDRP infection.

Methods

This was a mixed-methods study. We conducted a split-sample cohort study of adult critical care patients with P aeruginosa infections. Two previously published tools were validated using c-statistic. A subset of variables based on strength of association and ease of EMR extraction was selected for further evaluation. A simplified tool was developed using multivariable logistic regression. Both c-statistic and theoretical trade-off of over- versus underprescribing of broad-spectrum MDRP therapy were assessed in the validation cohort. A qualitative survey of frontline clinicians assessed understanding of risks for MDRP and potential usability of an EMR-integrated tool to predict MDRP.

Results

The 2 previous risk prediction tools demonstrated similar accuracy in the derivation cohort (c-statistic of 0.76 [95% confidence interval {CI}, .69-.83] and 0.73 [95% CI, .66-.8]). A simplified tool based on 4 variables demonstrated reasonable accuracy (c-statistic of 0.71 [95% CI, .57-.85]) without significant overprescribing in the validation cohort. The risk factors were prior MDRP infection, ≥4 antibiotics prior to culture, infection >3 days after admission, and dialysis. Fourteen clinicians completed the survey. An alert providing context regarding individual patient risk factors for MDRP was preferred.

Conclusions

These results can be used to develop a local EMR-integrated tool to improve timeliness of effective therapy in those at risk of MDRP infections.

Predicting Extended-Spectrum Beta-Lactamase and Carbapenem Resistance in Enterobacteriaceae Bacteremia: A Diagnostic Model Systematic Review and Meta-Analysis

Enterobacteriaceae bacteremia, particularly when associated with antimicrobial resistance, can result in increased mortality, emphasizing the need for timely effective therapy. Clinical risk prediction models are promising tools, stratifying patients based on their risk of resistance due to ESBL and carbapenemase-producing Enterobacteriaceae in bloodstream infections (BSIs) and, thereby, improving therapeutic decisions. This systematic review and meta-analysis synthesized the literature on the performance of these models. Searches of PubMed and EMBASE led to the identification of 10 relevant studies with 6106 unique patient encounters. Nine studies concerned ESBL prediction, and one focused on the prediction of carbapenemases. For the two ESBL model derivation studies, the discrimination performance showed sensitivities of 53-85% and specificities of 93-95%. Among the four ESBL model derivation and validation studies, the sensitivities were 43-88%, and the specificities were 77-99%. The sensitivity and specificity for the subsequent external validation studies were 7-37% and 88-96%, respectively. For the three external validation studies, only two models were evaluated across multiple studies, with a pooled AUROC of 65-71%, with one study omitting the sensitivity/specificity. Only two studies measured clinical utility through hypothetical therapy assessments. Given the limited evidence on their interventional application, it would be beneficial to further assess these or future models, to better understand their clinical utility and ensure their safe and impactful implementation.

Antimicrobial Resistance in Ventilator-Associated Pneumonia: Predictive Microbiology and Evidence-Based Therapy

Ventilator-associated pneumonia (VAP) is a serious intensive care unit (ICU)-related infection in mechanically ventilated patients that is frequent, as more than half of antibiotics prescriptions in ICU are due to VAP. Various risk factors and diagnostic criteria for VAP have been referred to in different settings. The estimated attributable mortality of VAP can go up to 50%, which is higher in cases of antimicrobial-resistant VAP. When the diagnosis of pneumonia in a mechanically ventilated patient is made, initiation of effective antimicrobial therapy must be prompt. Microbiological diagnosis of VAP is required to optimize timely therapy since effective early treatment is fundamental for better outcomes, with controversy continuing regarding optimal sampling and testing. Understanding the role of antimicrobial resistance in the context of VAP is crucial in the era of continuously evolving antimicrobial-resistant clones that represent an urgent threat to global health. This review is focused on the risk factors for antimicrobial resistance in adult VAP and its novel microbiological tools. It aims to summarize the current evidence-based knowledge about the mechanisms of resistance in VAP caused by multidrug-resistant bacteria in clinical settings with focus on Gram-negative pathogens. It highlights the evidence-based antimicrobial management and prevention of drug-resistant VAP. It also addresses emerging concepts related to predictive microbiology in VAP and sheds lights on VAP in the context of coronavirus disease 2019 (COVID-19).

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.

Korean Guidelines for Use of Antibiotics for Intra-abdominal Infections in Adults

The guidelines are intended to provide practical information for the correct use of antibiotics for intra-abdominal infections in Korea. With the aim of realizing evidence-based treatment, these guidelines for the use of antibiotics were written to help clinicians find answers to key clinical questions that arise in the course of patient care, using the latest research results based on systematic literature review. The guidelines were prepared in consideration of the data on the causative pathogens of intra-abdominal infections in Korea, the antibiotic susceptibility of the causative pathogens, and the antibiotics available in Korea.

A multicenter analysis of trends in resistance in urinary Enterobacterales isolates from ambulatory patients in the United States: 2011-2020

Background

Urinary tract infections (UTIs), which are usually caused by bacteria in the Enterobacterales family, are a common reason for outpatient visits. Appropriate empiric therapy for UTIs requires an understanding of antibiotic resistance in the community. In this nationwide study, we examined trends in antibiotic resistance in urinary Enterobacterales isolates from ambulatory patients in the United States (US).

Methods

We analyzed the antimicrobial susceptibility profiles (extended-spectrum beta-lactamase [ESBL]-producing phenotype and not susceptible [NS] to beta-lactams, trimethoprim/sulfamethoxazole [TMP/SMX], fluoroquinolones [FQ], or nitrofurantoin [NFT]) of 30-day non-duplicate Enterobacterales isolates from urine cultures tested at ambulatory centers in the BD Insights Research Database (2011–2020). The outcome of interest was the percentage of resistant isolates by pathogen and year. Multi-variable generalized estimating equation models were used to assess trends in resistance over time and by additional covariates.

Results

A total of 338 US facilities provided data for > 2.2 million urinary Enterobacterales isolates during the 10-year study. Almost three-quarters (72.8%) of Enterobacterales isolates were Escherichia coli. Overall unadjusted resistance rates in Enterobacterales isolates were 57.5%, 23.1%, 20.6%, and 20.2% for beta-lactams, TMP/SMX, FQ, and NFT, respectively, and 6.9% had an ESBL-producing phenotype. Resistance to two or more antibiotic classes occurred in 16.4% of isolates and 5.5% were resistant to three or more classes. Among isolates with an ESBL-producing phenotype, 70.1%, 59.9%, and 33.5% were NS to FQ, TMP/SMX, and NFT, respectively. In multivariable models, ESBL-producing and NFT NS Enterobacterales isolates increased significantly (both P < 0.001), while other categories of resistance decreased. High rates (≥ 50%) of beta-lactam and NFT resistance were observed in Klebsiella isolates and in non-E. coli, non-Klebsiella Enterobacterales isolates.

Conclusions

Antimicrobial resistance was common in urinary Enterobacterales isolates. Isolates with an ESBL-producing phenotype increased by about 30% between 2011 and 2020, and significant increases were also observed in NFT NS Enterobacterales isolates. Resistance rates for all four antibiotic classes were higher than thresholds recommended for use as empiric therapy. Non-E. coli Enterobacterales isolates showed high levels of resistance to commonly used empiric antibiotics, including NFT. These data may help inform empiric therapy choices for outpatients with UTIs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07167-y.

Developing a model to predict individualised treatment for gonorrhoea: a modelling study

OBJECTIVE

To develop a tool predicting individualised treatment for gonorrhoea, enabling treatment with previously recommended antibiotics, to reduce use of last-line treatment ceftriaxone.

DESIGN

A modelling study.

SETTING

England and Wales.

PARTICIPANTS

Individuals accessing sentinel health services.

INTERVENTION

Developing an Excel model which uses participants' demographic, behavioural and clinical characteristics to predict susceptibility to legacy antibiotics. Model parameters were calculated using data for 2015-2017 from the Gonococcal Resistance to Antimicrobials Surveillance Programme.

MAIN OUTCOME MEASURES

Estimated number of doses of ceftriaxone saved, and number of people delayed effective treatment, by model use in clinical practice. Model outputs are the predicted risk of resistance to ciprofloxacin, azithromycin, penicillin and cefixime, in groups of individuals with different combinations of characteristics (gender, sexual orientation, number of recent sexual partners, age, ethnicity), and a treatment recommendation.

RESULTS

Between 2015 and 2017, 8013 isolates were collected: 64% from men who have sex with men, 18% from heterosexual men and 18% from women. Across participant subgroups, stratified by all predictors, resistance prevalence was high for ciprofloxacin (range: 11%-51%) and penicillin (range: 6%-33%). Resistance prevalence for azithromycin and cefixime ranged from 0% to 13% and for ceftriaxone it was 0%. Simulating model use, 88% of individuals could be given cefixime and 10% azithromycin, saving 97% of ceftriaxone doses, with 1% of individuals delayed effective treatment.

CONCLUSIONS

Using demographic and behavioural characteristics, we could not reliably identify a participant subset in which ciprofloxacin or penicillin would be effective. Cefixime resistance was almost universally low; however, substituting ceftriaxone for near-uniform treatment with cefixime risks re-emergence of resistance to cefixime and ceftriaxone. Several subgroups had low azithromycin resistance, but widespread azithromycin monotherapy risks resistance at population level. However, this dataset had limitations; further exploration of individual characteristics to predict resistance to a wider range of legacy antibiotics may still be appropriate.

Burden of illness in US hospitals due to carbapenem-resistant Gram-negative urinary tract infections in patients with or without bacteraemia

Background

Urinary tract infections (UTIs) are the most common infections caused by Gram-negative bacteria and represent a major healthcare burden. Carbapenem-resistant (CR) strains of Enterobacterales and non-lactose fermenting pathogens further complicate treatment approaches.

Methods

We conducted a retrospective analysis of the US Premier Healthcare Database (2014–2019) in hospitalised adults with a UTI to estimate the healthcare burden of Gram-negative CR UTIs among patients with or without concurrent bacteraemia.

Results

Among the 47,496 patients with UTI analysed, CR infections were present in 2076 (4.4%). Bacteraemia was present in 24.5% of all UTI patients, and 1.7% of these were caused by a CR pathogen. The most frequent CR pathogens were Pseudomonas aeruginosa (49.4%) and Klebsiella pneumoniae (14.2%). Patients with CR infections had a significantly longer hospital length of stay (LOS) (median [range] 8 [5–12] days vs 6 [4–10] days, P < 0.001), were less likely to be discharged home (38.4% vs 51.0%, P < 0.001), had a higher readmission rate (22.6% vs 13.5%, P < 0.001), and had greater LOS-associated charges (mean US$ 91,752 vs US$ 66,011, P < 0.001) than patients with carbapenem-susceptible (CS) infections, respectively. The impact of CR pathogens was greater in patients with bacteraemia (or urosepsis) and these CR urosepsis patients had a significantly higher rate of mortality than those with CS urosepsis (10.5% vs 6.0%, P < 0.001).

Conclusions

Among hospitalised patients with UTIs, the presence of a CR organism and bacteraemia increased the burden of disease, with worse outcomes and higher hospitalisation charges than disease associated with CS pathogens and those without bacteraemia.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06229-x.

Restricted antimicrobial prescribing in an area of highly prevalent antimicrobial resistance

OBJECTIVE

To assess the predictive value for infection with multidrug-resistant organisms (MDROs) of reasons for empirical prescription of restricted antibiotics (rABX), in a setting with high resistance rates.

METHODS

We prospectively studied all rABX prescriptions in a 550-bed tertiary teaching hospital from April 15 to June 14, 2018 and from September 1 to October 30, 2018. Prescribing physicians had to justify their decision by choosing one or more prespecified reasons.

RESULTS

We reviewed 172 empirical prescriptions of rABX, which accounted for 67.2% of all rABX prescriptions. Stated reasons for empirical prescription of rABX were recent hospitalization (72.7%), escalation due to non-response to previous antimicrobials (47.7%), treatment for severe sepsis/septic shock (45.9%), escalation due to recurrence or deterioration (22.1%), prior MDRO infection (12.8%), and prior MDRO colonization (7.6%). Empirical treatment for septic shock or severe sepsis was the only significant predictor of MDRO isolation (OR=5.26, 95% CI: 1.5-18.4, P=0.009), while recent hospitalization had a high negative predictive value for MDRO (97.4%). Fourteen per cent of microbiologically documented infections were associated with MDROs resistant to the prescribed rABX.

CONCLUSIONS

Empirical treatment for severe sepsis or septic shock was the only independent predictor of MDRO isolation. Recent hospitalization had a high negative predictive value for MDRO infection. The isolation of pathogens resistant to the prescribed rABX suggests that in a setting with widespread antimicrobial resistance, it could be difficult to reduce the empirical use of rABX without risking inadequate treatment.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Best practice: antibiotic decision-making in ICUs

PURPOSE OF REVIEW

A major challenge in the ICU is optimization of antibiotic use. This review assesses current understanding of core best practices supporting and promoting astute antibiotic decision-making.

RECENT FINDINGS

Limiting exposure to the shortest effective duration is the cornerstone of antibiotic decision-making. The decision to initiate antibiotics should include assessment of risk for resistance. This requires synthesis of patient-level data and environmental factors to determine whether delayed initiation could be considered in some patients with suspected sepsis until sensitivity data is available. Until improved stratification scores and clinically meaningful cut-off values to identify MDR are available and externally validated, decisions as to which empiric antibiotic is used should rely on syndromic antibiograms and institutional guidance. Optimization of initial and maintenance doses is another enabler of enhanced outcome. Stewardship practices must be streamlined by re-assessment to minimize negative effects, such as a potential increase in duration of therapy and increased risk of collateral damage from exposure to multiple, sequential antibiotics that may ensue from de-escalation.

SUMMARY

Multiple challenges and research priorities for antibiotic optimization remain; however, the best stewardship practices should be identified and entrenched in daily practice. Reducing unnecessary exposure remains a vital strategy to limit resistance development.

Approach to the Treatment of Patients with Serious Multidrug-Resistant Pseudomonas aeruginosa Infections

Multidrug resistance(MDR) among Pseudomonas aeruginosa (PSA) isolates presents a significant clinical challenge and can substantially complicate the approach to selection of optimal antibiotic therapy. This review addresses major considerations in antibiotic selection for patients with suspected or documented serious MDR-PSA infections. Common mechanisms contributing to MDR among clinical PSA isolates are summarized. Empiric and definitive therapy considerations are addressed including the potential role of combination therapy. Newer agents with in vitro activity against MDR-PSA (e.g., ceftolozane-tazobactam, ceftazidime-avibactam, imipenem-relebactam, and cefiderocol) and their potential roles in clinical settings are discussed. Although these newer agents are promising options for the treatment of MDR-PSA, clinical data remain generally limited. Future studies are needed to determine optimal agents for the empiric and definitive treatment of MDR-PSA.

Current Concepts in Community and Ventilator Associated Lower Respiratory Tract Infections in ICU Patients

It is widely known that pneumonia (either community acquired or hospital acquired, as like ventilator associated pneumonia (VAP)), is the most frequent type of severe infection and continues to pose a significant burden on healthcare services worldwide. Despite new diagnostic developments, most pneumonia cases continue to be difficult to diagnose clinically, partly due to acquired antibiotic resistance and the lack of a ‘gold standard’ method of diagnosis. In other words, the lack of a rapid, accurate diagnostic test, as well as the uncertainty of the initial etiologic diagnosis and the risk stratification, results in empirical antibiotic treatments. There are significant changes in the aetiology of patients with ventilator associated lower respiratory tract infections (VA-LRTI), which are characterised by a higher incidence of multi drug resistant organisms. Evidence suggests that when patients with VA-LRTI develop organ failure, the associated mortality can be exceptionally high with frequent complications, including acute respiratory distress syndrome, acute kidney injury, and septic shock. Appropriate antibiotic treatments must consider that the present cardiovascular failure seen in patients has a different association with the patient’s mortality. Unlike patients with less severe clinical presentations, who have a higher chance of survival when the appropriate antibiotics are administered promptly, for patients with a severe subtype of the disease, the appropriateness of antibiotic treatment will impact the patient’s outcome to a lesser extent. The present review highlights certain factors detectable at the time of admission that could indicate patients who are at a high risk of bacteraemia and who, therefore, merit more intense therapy and stratified care.

Derivation of a Model to Guide Empiric Therapy for Carbapenem-Resistant Klebsiella pneumoniae Bloodstream Infection in an Endemic Area

BACKGROUND

Appropriate therapy for carbapenem-resistant Klebsiella pneumoniae (CRKP) bloodstream infection (BSI) is often given late in the course of infection, and strategies for identifying CRKP BSI earlier are needed.

METHODS

A retrospective case-control study was performed at a tertiary care hospital, university hospital, and community hospital in Bronx, New York. All participants had a blood culture sent and received an antibiotic within 48 hours of the culture. The case group (n = 163) had a blood culture with CRKP. The control group (n = 178) had a blood culture with carbapenem-susceptible Klebsiella. Data were obtained by electronic or conventional medical record abstraction. A multiple logistic regression model was built to identify associated factors and develop a clinical model for CRKP BSI. Model performance characteristics were estimated using a 10-fold cross-validation analysis.

RESULTS

A prior nonblood culture with carbapenem-resistant Enterobacteriaceae, skilled nursing facility (SNF) residence, mechanical ventilation, and admission >3 days were strongly associated risk factors. A significant interaction led to development of separate clinical models for subjects admitted <3 days at the time of positive blood culture from those admitted at least 3 days. The derived models had a good ability to discriminate between subjects with and without CRKP BSI. A clinical classification rule to guide therapy can prioritize sensitivity or specificity.

CONCLUSIONS

Prior nonblood cultures showing resistance and exposure to SNF and health care settings are factors associated with carbapenem resistance. The clinical classification rules derived in this work should be validated for ability to guide therapy.