Antimicrobial Use and Antimicrobial Resistance: A Population Perspective

The need to stem the growing problem of antimicrobial resistance has prompted multiple, sometimes conflicting, calls for changes in the use of antimicrobial agents. One source of disagreement concerns the major mechanisms by which antibiotics select resistant strains. For infections like tuberculosis, in which resistance can emerge in treated hosts through mutation, prevention of antimicrobial resistance in individual hosts is a primary method of preventing the spread of resistant organisms in the community. By contrast, for many other important resistant pathogens, such as penicillin-resistant Streptococcus pneumoniae, methicillin-resistant Staphylococcus aureus, and vancomycin-resistant Enterococcus faecium resistance is mediated by the acquisition of genes or gene fragments by horizontal transfer; resistance in the treated host is a relatively rare event. For these organisms, indirect, population-level mechanisms of selection account for the increase in the prevalence of resistance. These mechanisms can operate even when treatment has a modest, or even negative, effect on an individual host’s colonization with resistant organisms

Antimicrobial Resistance Determinants and Future Control

At the beginning of the 21st century, antimicrobial resistance is common, has developed against every class of antimicrobial drug, and appears to be spreading into new clinical niches. We describe determinants likely to influence the future epidemiology and health impact of antimicrobial-resistant infections. Understanding these factors will ultimately optimize preventive strategies for an unpredictable future

Using the Electronic Medical Record to Identify Community-Acquired Pneumonia: Toward a Replicable Automated Strategy

Background: Timely information about disease severity can be central to the detection and management of outbreaks of acute respiratory infections (ARI), including influenza. We asked if two resources: 1) free text, and 2) structured data from an electronic medical record (EMR) could complement each other to identify patients with pneumonia, an ARI severity landmark. Methods: A manual EMR review of 2747 outpatient ARI visits with associated chest imaging identified x-ray reports that could support the diagnosis of pneumonia (kappa score = 0.88 (95% CI 0.82∶0.93)), along with attendant cases with Possible Pneumonia (adds either cough, sputum, fever/chills/night sweats, dyspnea or pleuritic chest pain) or with Pneumonia-in-Plan (adds pneumonia stated as a likely diagnosis by the provider). The x-ray reports served as a reference to develop a text classifier using machine-learning software that did not require custom coding. To identify pneumonia cases, the classifier was combined with EMR-based structured data and with text analyses aimed at ARI symptoms in clinical notes. Results: 370 reference cases with Possible Pneumonia and 250 with Pneumonia-in-Plan were identified. The x-ray report text classifier increased the positive predictive value of otherwise identical EMR-based case-detection algorithms by 20–70%, while retaining sensitivities of 58–75%. These performance gains were independent of the case definitions and of whether patients were admitted to the hospital or sent home. Text analyses seeking ARI symptoms in clinical notes did not add further value. Conclusion: Specialized software development is not required for automated text analyses to help identify pneumonia patients. These results begin to map an efficient, replicable strategy through which EMR data can be used to stratify ARI severity

Drugs of Last Resort? The Use of Polymyxins and Tigecycline at US Veterans Affairs Medical Centers, 2005–2010

Multidrug-resistant (MDR) and carbapenem-resistant (CR) Gram-negative pathogens are becoming increasingly prevalent around the globe. Polymyxins and tigecycline are among the few antibiotics available to treat infections with these bacteria but little is known about the frequency of their use. We therefore aimed to estimate the parenteral use of these two drugs in Veterans Affairs medical centers (VAMCs) and to describe the pathogens associated with their administration. For this purpose we retrospectively analyzed barcode medication administration data of parenteral administrations of polymyxins and tigecycline in 127 acute-care VAMCs between October 2005 and September 2010. Overall, polymyxin and tigecycline use were relatively low at 0.8 days of therapy (DOT)/1000 patient days (PD) and 1.6 DOT/1000PD, respectively. Use varied widely across facilities, but increased overall during the study period. Eight facilities accounted for three-quarters of all polymyxin use. The same statistic for tigecycline use was twenty-six VAMCs. There were 1,081 MDR or CR isolates during 747 hospitalizations associated with polymyxin use (1.4/hospitalization). For tigecycline these number were slightly lower: 671 MDR or CR isolates during 500 hospitalizations (1.3/hospitalization) (p = 0.06). An ecological correlation between the two antibiotics and combined CR and MDR Gram-negative isolates per 1000PD during the study period was also observed (Pearson’s correlation coefficient r = 0.55 polymyxin, r = 0.19 tigecycline). In summary, while polymyxin and tigecycline use is low in most VAMCs, there has been an increase over the study period. Polymyxin use in particular is associated with the presence of MDR Gram-negative pathogens and may be useful as a surveillance measure in the future

Carriage of Methicillin-Resistant Staphylococcus Aureus at Hospital Admission

Abstract Objectives: To measure the prevalence of, and to establish predictors for, the nasal carriage of methicillin-resistant Staphylococcus aureus (MRSA) at hospital admission. To evaluate mannitol-salt agar with oxacillin for the simultaneous detection and identification of MRSA from nasal swabs. Design: Three-month prospective case-control survey, with data collected from interviews and computerized databases. The criterion standard for MRSA detection was culture on Mueller-Hinton agar with oxacillin 6 μg/mL (National Committee for Clinical Laboratory Standards method). Setting: 320-bed tertiary-care hospital. Patients: 387 patients screened within 24 hours after admission, including 10 MRSA carriers (cases), 291 patients with no S aureus, and 86 patients with methicillin-susceptible S aureus. Results: The prevalence of MRSA nasal carriage was 2.6%, whereas the prevalence of carriage was 3.1% when both nasal and wound cultures were performed. The significant predictors of carriage were a prior detection of MRSA, open wounds, diabetes mellitus, treatments by injection, prior nursing home stays, visits at home by a nurse, and prior antibiotic treatments. Cases had stayed for longer periods in hospitals and had received longer antibiotic treatments within a year. Eighty patients (including the 10 cases) had diabetes, had been exposed to healthcare facilities within a year, and had antibiotics within 6 months. The sensitivity and negative predictive value of nasal swabs on mannitol-salt agar with oxacillin were 60% and 71%, respectively. Conclusion: MRSA carriage on admission to the hospital may be an increasing and underestimated problem. Further studies are needed to develop and validate a sensitive and specific prediction rul

Identifying Complexity in Infectious Diseases Inpatient Settings: An Observation Study

Background Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design. Objective To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians. Method We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians. Results A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues \u3e 2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach’s Alpha = 0.87), urgency and acuity (6 items, 11%, Cronbach’s Alpha = 0.67), and psychosocial behavior (4 items, 10%, Cronbach’s alpha = 0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared = 0.13, p = 0.61). There were no physician effects on the rating of perceived complexity. Conclusion Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning

Drug-resistant Escherichia coli, Rural Idaho

Stool carriage of drug-resistant Escherichia coli in home-living residents of a rural community was examined. Carriage of nalidixic acid–resistant E. coli was associated with recent use of antimicrobial agents in the household. Household clustering of drug-resistant E. coli was observed. Most carriers of drug-resistant E. coli lacked conventional risk factors

Exploiting the UMLS Metathesaurus for extracting and categorizing concepts representing signs and symptoms to anatomically related organ systems

AbstractObjectiveTo develop a method to exploit the UMLS Metathesaurus for extracting and categorizing concepts found in clinical text representing signs and symptoms to anatomically related organ systems. The overarching goal is to classify patient reported symptoms to organ systems for population health and epidemiological analyses.Materials and methodsUsing the concepts’ semantic types and the inter-concept relationships as guidance, a selective portion of the concepts within the UMLS Metathesaurus was traversed starting from the concepts representing the highest level organ systems. The traversed concepts were chosen, filtered, and reviewed to obtain the concepts representing clinical signs and symptoms by blocking deviations, pruning superfluous concepts, and manual review. The mapping process was applied to signs and symptoms annotated in a corpus of 750 clinical notes.ResultsThe mapping process yielded a total of 91,000 UMLS concepts (with approximately 300,000 descriptions) possibly representing physical and mental signs and symptoms that were extracted and categorized to the anatomically related organ systems. Of 1864 distinct descriptions of signs and symptoms found in the 750 document corpus, 1635 of these (88%) were successfully mapped to the set of concepts extracted from the UMLS. Of 668 unique concepts mapped, 603 (90%) were correctly categorized to their organ systems.ConclusionWe present a process that facilitates mapping of signs and symptoms to their organ systems. By providing a smaller set of UMLS concepts to use for comparing and matching patient records, this method has the potential to increase efficiency of information extraction pipelines