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Rapid Molecular Diagnostics, Antibiotic Treatment Decisions, and Developing Approaches to Inform Empiric Therapy: PRIMERS I and II.

ABSTRACT: Rapid molecular diagnostic (RMD) platforms may lead to better antibiotic use. Our objective was to develop analytical strategies to enhance the interpretation of RMDs for clinicians.We compared the performance characteristics of 4 RMD platforms for detecting resistance against ?-lactams in 72 highly resistant isolates of Escherichia coli and Klebsiella pneumoniae (PRIMERS I). Subsequently, 2 platforms were used in a blinded study in which a heterogeneous collection of 196 isolates of E. coli and K. pneumoniae (PRIMERS II) were examined. We evaluated the genotypic results as predictors of resistance or susceptibility against ?-lactam antibiotics. We designed analytical strategies and graphical representations of platform performance, including discrimination summary plots and susceptibility and resistance predictive values, that are readily interpretable by practitioners to inform decision-making.In PRIMERS I, the 4 RMD platforms detected ?-lactamase (bla) genes and identified susceptibility or resistance in >95% of cases. In PRIMERS II, the 2 platforms identified susceptibility against extended-spectrum cephalosporins and carbapenems in >90% of cases; however, against piperacillin/tazobactam, susceptibility was identified in <80% of cases. Applying the analytical strategies to a population with 15% prevalence of ceftazidime-resistance and 5% imipenem-resistance, RMD platforms predicted susceptibility in >95% of cases, while prediction of resistance was 69%-73% for ceftazidime and 41%-50% for imipenem.RMD platforms can help inform empiric ?-lactam therapy in cases where bla genes are not detected and the prevalence of resistance is known. Our analysis is a first step in bridging the gap between RMDs and empiric treatment decisions.

SUBMITTER: Evans SR

PROVIDER: S-EPMC4690483 | biostudies-other | 2016 Jan

REPOSITORIES: biostudies-other

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Rapid Molecular Diagnostics, Antibiotic Treatment Decisions, and Developing Approaches to Inform Empiric Therapy: PRIMERS I and II.

Evans Scott R SR Hujer Andrea M AM Jiang Hongyu H Hujer Kristine M KM Hall Thomas T Marzan Christine C Jacobs Michael R MR Sampath Rangarajan R Ecker David J DJ Manca Claudia C Chavda Kalyan K Zhang Pan P Fernandez Helen H Chen Liang L Mediavilla Jose R JR Hill Carol B CB Perez Federico F Caliendo Angela M AM Fowler Vance G VG Chambers Henry F HF Kreiswirth Barry N BN Bonomo Robert A RA

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20150925 2

<h4>Background</h4>Rapid molecular diagnostic (RMD) platforms may lead to better antibiotic use. Our objective was to develop analytical strategies to enhance the interpretation of RMDs for clinicians.<h4>Methods</h4>We compared the performance characteristics of 4 RMD platforms for detecting resistance against β-lactams in 72 highly resistant isolates of Escherichia coli and Klebsiella pneumoniae (PRIMERS I). Subsequently, 2 platforms were used in a blinded study in which a heterogeneous collec ...[more]

PMID: 26409063

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Project description:IntroductionClinicians commonly escalate empiric antibiotic therapy due to poor clinical progress without microbiology guidance. When escalating, they should take account of how resistance to an initial antibiotic affects the probability of resistance to subsequent options. The term "escalation antibiogram" (EA) has been coined to describe this concept. One difficulty when applying the EA concept to clinical practice is understanding the uncertainty in results and how this changes for specific patient subgroups.MethodsA Bayesian model was developed to estimate antibiotic resistance rates in Gram-negative bloodstream infections based on phenotypic resistance data. The model generates a series of "credible" curves to fit the resistance data, each with the same probability of representing the true rate given the inherent uncertainty. To avoid overfitting, an integrated penalisation term adaptively smooths the curves given the level of evidence.ResultsRates of resistance to empiric first-choice and potential escalation antibiotics were calculated for the whole hospitalised population based on 10,486 individual bloodstream infections, and for a range of specific patient groups, including ICU (intensive care unit), haematolo-oncology, and paediatric patients. The model generated an expected value (posterior mean) with 95% credible interval to illustrate uncertainty, based on the size of the patient subgroup. For example, the posterior means of piperacillin/tazobactam resistance rates in Gram-negative bloodstream infection are different between patients on ICU and the general hospital population: 27.3% (95% CI 18.1-37.2 vs. 13.4% 95% CI 11.0-16.1) respectively. The model can also estimate the probability of inferiority between two antibiotics for a specific patient population. Differences in optimal escalation antibiotic options between specific patient groups were noted.ConclusionsEA analysis informed by our Bayesian model is a useful tool to support empiric antibiotic switches, providing an estimate of local resistance rates, and a comparison of antibiotic options with a measure of the uncertainty in the data. We demonstrate that EAs calculated for the whole hospital population cannot be assumed to apply to specific patient group.

Project description:BackgroundEmpiric antibiotics for community acquired bacterial pneumonia (CABP) are often prescribed to patients with COVID-19, despite a low reported incidence of co-infections. Stewardship interventions targeted at facilitating appropriate antibiotic prescribing for CABP among COVID-19 patients are needed. We developed a guideline for antibiotic initiation and discontinuation for CABP in COVID-19 patients. The purpose of this study was to assess the impact of this intervention on the duration of empiric CABP antibiotic therapy among patients with COVID-19.MethodsThis was a single-center, retrospective, quasi-experimental study of adult patients admitted between 3/1/2020 to 4/25/2020 with COVID-19 pneumonia, who were initiated on empiric CABP antibiotics. Patients were excluded if they were initiated on antibiotics > 48 h following admission or if another source of infection was identified. The primary outcome was the duration of antibiotic therapy (DOT) prior to the guideline (March 1 to March27, 2020) and after guideline implementation (March 28 to April 25, 2020). We also evaluated the clinical outcomes (mortality, readmissions, length of stay) among those initiated on empiric CABP antibiotics.ResultsA total of 506 patients with COVID-19 were evaluated, 102 pre-intervention and 404 post-intervention. Prior to the intervention, 74.5% (n = 76) of patients with COVID-19 received empiric antibiotics compared to only 42% of patients post-intervention (n = 170), p < 0.001. The median DOT in the post-intervention group was 1.3 days shorter (p < 0.001) than the pre-intervention group, and antibiotics directed at atypical bacteria DOT was reduced by 2.8 days (p < 0.001). More patients in the post-intervention group were initiated on antibiotics based on criteria consistent with our guideline (68% versus 87%, p = 0.001). There were no differences between groups in terms of clinical outcomes.ConclusionFollowing the implementation of a guideline outlining recommendations for initiating and discontinuing antibiotics for CABP among COVID-19 inpatients, we observed a reduction in antibiotic prescribing and DOT. The guideline also resulted in a significant increase in the rate of guideline-congruent empiric antibiotic initiation.

Project description:Quantifying the benefit of early antibiotic treatment is crucial for decision making and can be assessed only in observational studies. We performed a systematic review of prospective studies reporting the effect of appropriate empirical antibiotic treatment on all-cause mortality among adult inpatients with sepsis. Two reviewers independently extracted data. Risk of bias was assessed using the Newcastle-Ottawa score. We calculated unadjusted odds ratios (ORs) with 95% confidence intervals for each study and extracted adjusted ORs, with variance, methods, and covariates being used for adjustment. ORs were pooled using random-effects meta-analysis. We examined the effects of methodological and clinical confounders on results through subgroup analysis or mixed-effect meta-regression. Seventy studies were included, of which 48 provided an adjusted OR for inappropriate empirical antibiotic treatment. Inappropriate empirical antibiotic treatment was associated with significantly higher mortality in the unadjusted and adjusted comparisons, with considerable heterogeneity occurring in both analyses (I(2) > 70%). Study design, time of mortality assessment, the reporting methods of the multivariable models, and the covariates used for adjustment were significantly associated with effect size. Septic shock was the only clinical variable significantly affecting results (it was associated with higher ORs). Studies adjusting for background conditions and sepsis severity reported a pooled adjusted OR of 1.60 (95% confidence interval = 1.37 to 1.86; 26 studies; number needed to treat to prevent one fatal outcome, 10 patients [95% confidence interval = 8 to 15]; I(2) = 46.3%) given 34% mortality with inappropriate empirical treatment. Appropriate empirical antibiotic treatment is associated with a significant reduction in all-cause mortality. However, the methods used in the observational studies significantly affect the effect size reported. Methods of observational studies assessing the effects of antibiotic treatment should be improved and standardized.

Dataset Information

Rapid Molecular Diagnostics, Antibiotic Treatment Decisions, and Developing Approaches to Inform Empiric Therapy: PRIMERS I and II.

Publications

Rapid Molecular Diagnostics, Antibiotic Treatment Decisions, and Developing Approaches to Inform Empiric Therapy: PRIMERS I and II.

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