Infections Caused by Extended-Spectrum β-Lactamase Producing Escherichia Coli in Systemic Lupus Erythematosus Patients: Prevalence, Risk Factors, and Predictive Model

Clinical Risk Scores to Predict Nonsusceptibility to Trimethoprim-Sulfamethoxazole, Fluoroquinolone, Nitrofurantoin, and Third-Generation Cephalosporin Among Adult Outpatient Episodes of Complicated Urinary Tract Infection

Background

Clinical risk scores were developed to estimate the risk of adult outpatients having a complicated urinary tract infection (cUTI) that was nonsusceptible to trimethoprim-sulfamethoxazole (TMP-SMX), fluoroquinolone, nitrofurantoin, or third-generation cephalosporin (3-GC) based on variables available on clinical presentation.

Methods

A retrospective cohort study (1 December 2017-31 December 2020) was performed among adult members of Kaiser Permanente Southern California with an outpatient cUTI. Separate risk scores were developed for TMP-SMX, fluoroquinolone, nitrofurantoin, and 3-GC. The models were translated into risk scores to quantify the likelihood of nonsusceptibility based on the presence of final model covariates in a given cUTI outpatient.

Results

A total of 30 450 cUTIs (26 326 patients) met the study criteria. Rates of nonsusceptibility to TMP-SMX, fluoroquinolone, nitrofurantoin, and 3-GC were 37%, 20%, 27%, and 24%, respectively. Receipt of prior antibiotics was the most important predictor across all models. The risk of nonsusceptibility in the TMP-SMX model exceeded 20% in the absence of any risk factors, suggesting that empiric use of TMP-SMX may not be advisable. For fluoroquinolone, nitrofurantoin, and 3-GC, clinical risk scores of 10, 7, and 11 predicted a ≥20% estimated probability of nonsusceptibility in the models that included cumulative number of prior antibiotics at model entry. This finding suggests that caution should be used when considering these agents empirically in patients who have several risk factors present in a given model at presentation.

Conclusions

We developed high-performing parsimonious risk scores to facilitate empiric treatment selection for adult outpatients with cUTIs in the critical period between infection presentation and availability of susceptibility results.

A Framework to Overcome Challenges in the Management of Infections in Patients with Systemic Lupus Erythematosus

Infections remain one of the leading causes of death in systemic lupus erythematosus (SLE), despite awareness of factors contributing to increased susceptibility to infectious diseases in SLE. Clinicians report challenges and barriers when encountering infection in SLE as certain infections may mimic a lupus flare. There are no evidence-based practice guidelines in the management of fever in SLE, with suboptimal implementations of evidence-based benefits related to infectious disease control and/or prevention strategies in SLE. Vigilance in identifying an opportunistic infection must be stressed when confronted by a diagnostic challenge during a presentation with a febrile illness in SLE. A balanced approach must focus on management of infections in SLE, and reduction in the glucocorticoids dose, given the need to control lupus disease activity to avoid lupus related organ damage and mortality. Clinical judgement and application of biomarkers of lupus flares could reduce false positives and overdiagnosis and improve differentiation of infections from lupus flares. Further precision-based risk and screening measures must identify individuals who would benefit most from low dose immunosuppressive therapy, targeted immune therapy, and vaccination programs.

Using Prior Culture Results to Improve Initial Empiric Antibiotic Prescribing: An Evaluation of a Simple Clinical Heuristic

BACKGROUND

A patient's prior cultures can inform the subsequent risk of infection from resistant organisms, yet prescribers often fail to incorporate these results into their empiric antibiotic selection. Given that timely initiation of adequate antibiotics has been associated with improved outcomes, there is an urgent need to address this gap.

METHODS

In order to better incorporate prior culture results in the selection of empiric antibiotics, we performed a pragmatic, prospective, hospital-wide intervention: (1) empiric antibiotic prescriptions were assessed for clinically significant discordance with the most recent methicillin-resistant Staphylococcus aureus (MRSA) surveillance swab, previous cultures for extended-spectrum beta-lactamases (ESBLs), and the most recent culture for a Gram-negative (GN) organism; and (2) if discordant, an antimicrobial stewardship pharmacist provided recommendations for alternative therapy. The impact was analyzed using a quasi-experimental design comparing two 9-month periods (pre- and postintervention) at a large academic, tertiary care institution.

RESULTS

Clinically significant discordance was identified 99 times in the preintervention period and 86 times in the intervention period. The proportion of patients that received concordant therapy increased from 73% (72/99) in the control group to 88% (76/86) in the intervention group (P = .01). The median time to concordant therapy was shorter in the intervention group than the control group (25 vs 55 hrs, respectively; P < .001; adjusted hazard ratio = 1.95 [95% confidence interval {CI}, 1.37-2.77; P < .001]). The median duration of unnecessary vancomycin therapy was reduced by 1.1 days (95% CI, .5-1.6 days; P < .001).

CONCLUSIONS

This intervention improved prescribing, with a shorter time to concordant therapy and an increased proportion of patients receiving empiric therapy concordant with prior culture results. The use of unnecessary vancomycin was also reduced.

Intra-abdominal infection in acute pancreatitis in eastern China: microbiological features and a prediction model

Background

This study aimed to investigate the microbiol distribution of intra-abdominal infection in patients with acute pancreatitis, and to develop a reliable prediction model to guide the use of antibiotics.

Methods

Inpatient with acute pancreatitis between January 2015 and June 2020 were enrolled in the study. Participants were divided into the intra-abdominal infection group and non-infection group. Isolated pathogens and antibiotic susceptibility were documented. Characteristics parameters, laboratory results, and outcomes were also compared. Least absolute shrinkage and selection operator (LASSO) regression model was used to select the risk factors associated with intra-abdominal infection in patients with acute pancreatitis. Logistic regression analysis, random forest model, and artificial neural network were also used to validate the performance of the selected predictors in intra-abdominal infection prediction. A novel nomogram based on selected predictors was established to provide individualized risk of developing intra-abdominal infection in patients with acute pancreatitis.

Results

A total amount of 711 participants were enrolled in the study, and of these, 182 (25.6%) had intra-abdominal infection. Of the 247 isolated pathogens, 45 (18.2%) were multidrug-resistant bacteria, and antibiotic susceptibility was lower than that of China Antimicrobial Surveillance Network 2020. The LASSO method identified 5 independent predictors [intra-abdominal pressure (IAP), acute physiology and chronic health evaluation II (APACHE II), computed tomography severity index (CTSI), the severity of pancreatitis, and intensive care unit (ICU) admission] of intra-abdominal infection, which were validated by three different models. The area under the curve was >0.95 for all 5 predictors. A clinically useful nomogram based on these predictors was successfully established.

Conclusions

Multidrug-resistant bacteria were quite common in intra-abdominal infection. IAP, APACHE II, CTSI, the severity of pancreatitis, and ICU admission were identified as risk factors and the new nomogram based on these could help clinicians estimate the risk of intra-abdominal infection and optimize antimicrobial prescription for acute pancreatitis patients.

Bacterial infections in lupus: Roles in promoting immune activation and in pathogenesis of the disease

BACKGROUND

Bacterial infections of the lung, skin, bloodstream and other tissues are common in patients with systemic lupus erythematosus (lupus) and are often more severe and invasive than similar infections in control populations. A variety of studies have explored the changes in bacterial abundance in lupus patients, the rates of infection and the influence of particular bacterial species on disease progression, using both human patient samples and mouse models of lupus.

OBJECTIVE

The aim of this review is to summarize human and mouse studies that describe changes in the bacterial microbiome in lupus, the role of a leaky gut in stimulating inflammation, identification of specific bacterial species associated with lupus, and the potential roles of certain common bacterial infections in promoting lupus progression.

METHODS

Information was collected using searches of the Pubmed database for articles relevant to bacterial infections in lupus and to microbiome changes associated with lupus.

RESULTS

The reviewed studies demonstrate significant changes in the bacterial microbiome of lupus patients as compared to control subjects and in lupus-prone mice compared to control mice. Furthermore, there is evidence supporting the existence of a leaky gut in lupus patients and in lupus-prone mice. This leaky gut may allow live bacteria or bacterial components to enter the circulation and cause inflammation. Invasive bacterial infections are more common and often more severe in lupus patients. These include infections caused by Staphylococcus aureus, Salmonella enterica, Escherichia coli, Streptococcus pneumoniae and mycobacteria. These bacterial infections can trigger increased immune activation and inflammation, potentially stimulating activation of autoreactive lymphocytes and leading to worsening of lupus symptoms.

CONCLUSIONS

Together, the evidence suggests that lupus predisposes to infection, while infection may trigger worsening lupus, leading to a feedback loop that may reinforce autoimmune symptoms.

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.