Discriminating Inhalational Anthrax From Community-Acquired Pneumonia Using Chest Radiograph Findings and a Clinical Algorithm

Distinguishing Respiratory Features of Category A/B Potential Bioterrorism Agents from Community-Acquired Pneumonia

Differentiating between illness caused by community-acquired respiratory pathogens versus infection by biothreat agents is a challenge. This review highlights respiratory and clinical features of category A and B potential biothreat agents that have respiratory features as their primary presenting signs and symptoms. Recent world events make such a reminder that the possibility of rare diseases and unlikely events can occur timely for clinicians, policymakers, and public health authorities. Despite some distinguishing features, nothing can replace good clinical acumen and a strong index of suspicion in the diagnosis of uncommon infectious diseases.

Identifying Meningitis During an Anthrax Mass Casualty Incident: Systematic Review of Systemic Anthrax Since 1880

BACKGROUND

Bacillus anthracis, the causative agent of anthrax, is a potential bioterrorism agent. Anthrax meningitis is a common manifestation of B. anthracis infection, has high mortality, and requires more aggressive treatment than anthrax without meningitis. Its rapid identification and treatment are essential for successful management of an anthrax mass casualty incident.

METHODS

Three hundred six published reports from 1880 through 2013 met predefined inclusion criteria. We calculated descriptive statistics for abstracted cases and conducted multivariable regression on separate derivation and validation cohorts to identify clinical diagnostic and prognostic factors for anthrax meningitis.

RESULTS

One hundred thirty-two of 363 (36%) cases with systemic anthrax met anthrax meningitis criteria. Severe headache, altered mental status, meningeal signs, and other neurological signs at presentation independently predicted meningitis in the derivation cohort and were tested as a 4-item assessment tool for use during anthrax mass casualty incidents. Presence of any 1 factor on admission had a sensitivity for finding anthrax meningitis of 89% (83%) in the adult (pediatric) validation cohorts. Anthrax meningitis was unlikely in the absence of any of these signs or symptoms (likelihood ratio [LR]- = 0.12 [0.19] for adult [pediatric] cohorts), while presence of 2 or more made meningitis very likely (LR+ = 26.5 [30.0]). Survival of anthrax meningitis was predicted by treatment with a bactericidal agent (P = .005) and use of multiple antimicrobials (P = .01).

CONCLUSIONS

We developed an evidence-based assessment tool for screening patients for meningitis during an anthrax mass casualty incident. Its use could improve both patient outcomes and resource allocation in such an event.

Cost-effectiveness comparison of response strategies to a large-scale anthrax attack on the chicago metropolitan area: impact of timing and surge capacity

Rapid public health response to a large-scale anthrax attack would reduce overall morbidity and mortality. However, there is uncertainty about the optimal cost-effective response strategy based on timing of intervention, public health resources, and critical care facilities. We conducted a decision analytic study to compare response strategies to a theoretical large-scale anthrax attack on the Chicago metropolitan area beginning either Day 2 or Day 5 after the attack. These strategies correspond to the policy options set forth by the Anthrax Modeling Working Group for population-wide responses to a large-scale anthrax attack: (1) postattack antibiotic prophylaxis, (2) postattack antibiotic prophylaxis and vaccination, (3) preattack vaccination with postattack antibiotic prophylaxis, and (4) preattack vaccination with postattack antibiotic prophylaxis and vaccination. Outcomes were measured in costs, lives saved, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). We estimated that postattack antibiotic prophylaxis of all 1,390,000 anthrax-exposed people beginning on Day 2 after attack would result in 205,835 infected victims, 35,049 fulminant victims, and 28,612 deaths. Only 6,437 (18.5%) of the fulminant victims could be saved with the existing critical care facilities in the Chicago metropolitan area. Mortality would increase to 69,136 if the response strategy began on Day 5. Including postattack vaccination with antibiotic prophylaxis of all exposed people reduces mortality and is cost-effective for both Day 2 (ICER=$182/QALY) and Day 5 (ICER=$1,088/QALY) response strategies. Increasing ICU bed availability significantly reduces mortality for all response strategies. We conclude that postattack antibiotic prophylaxis and vaccination of all exposed people is the optimal cost-effective response strategy for a large-scale anthrax attack. Our findings support the US government's plan to provide antibiotic prophylaxis and vaccination for all exposed people within 48 hours of the recognition of a large-scale anthrax attack. Future policies should consider expanding critical care capacity to allow for the rescue of more victims.

AHRQ White Paper: Use of clinical decision rules for point-of-care decision support

Translation of research into clinical practice remains a barrier, with inconsistent adoption of effective treatments and useful tests. Clinical decision rules (CDRs) integrate information from several clinical or laboratory findings to provide quantitative estimates of risk for a diagnosis or clinical outcome. They are increasingly reported in the literature and have the potential to provide a bridge that helps translate findings from original research studies into clinical practice. Unlike formal aids for shared decision making, they are pragmatic solutions that provide discrete quantitative data to aid clinicians and patients in decision making. These quantitative data can help inform the informal episodes of shared decision making that frequently take place at the point of care. Methods used to develop CDRs include expert opinion, multivariate models, point scores, and classification and regression trees Desirable CDRs are valid (make accurate predictions of risk), relevant (have been shown to improve patient-oriented outcomes), are easy to use at the point of care, are acceptable (with good face validity and transparency of recommendations), and are situated in the clinical context. The latter means that the rule places patients in risk groups that are clinically useful (i.e., below the test threshold or above the treatment threshold) and does so in adequate numbers to make use of the CDR a worthwhile investment in time. CDRs meeting these criteria should be integrated with electronic health records, populating the point score or decision tree with individual patient data and performing calculations automatically to streamline decision making.

Differentiation of gram-negative bacterial aerosol exposure using detected markers in bronchial-alveolar lavage fluid

The identification of biosignatures of aerosol exposure to pathogens has the potential to provide useful diagnostic information. In particular, markers of exposure to different types of respiratory pathogens may yield diverse sets of markers that can be used to differentiate exposure. We examine a mouse model of aerosol exposure to known Gram negative bacterial pathogens, Francisella tularensis novicida and Pseudomonas aeruginosa. Mice were subjected to either a pathogen or control exposure and bronchial alveolar lavage fluid (BALF) was collected at four and twenty four hours post exposure. Small protein and peptide markers within the BALF were detected by matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) and analyzed using both exploratory and predictive data analysis methods; principle component analysis and degree of association. The markers detected were successfully used to accurately identify the four hour exposed samples from the control samples. This report demonstrates the potential for small protein and peptide marker profiles to identify aerosol exposure in a short post-exposure time frame.

Biological terrorism

A biological terrorism event could have a large impact on the general population and health care system. The impact of an infectious disaster will most likely be great to emergency departments, and the collaboration between emergency and infectious disease specialists will be critical in developing an effective response. A bioterrorism event is a disaster that requires specific preparations beyond the usual medical disaster planning. An effective response would include attention to infection control issues and plans for large-scale vaccination or antimicrobial prophylaxis. This article addresses some general issues related to preparing an effective response to a biological terrorism event. It will also review organisms and toxins that could be used in biological terrorism, including clinical features, management, diagnostic testing, and infection control.

Developing Indicators of Inpatient Adverse Drug Events Through Nonlinear Analysis Using Administrative Data

BACKGROUND

Because of uniform availability, hospital administrative data are appealing for surveillance of adverse drug events (ADEs). Expert-generated surveillance rules that rely on the presence of International Classification of Diseases, 9th Revision Clinical Modification (ICD-9-CM) codes have limited accuracy. Rules based on nonlinear associations among all types of available administrative data may be more accurate.

OBJECTIVES

By applying hierarchically optimal classification tree analysis (HOCTA) to administrative data, derive and validate surveillance rules for bleeding/anticoagulation problems and delirium/psychosis.

RESEARCH DESIGN

Retrospective cohort design.

SUBJECTS

A random sample of 3987 admissions drawn from all 41 Utah acute-care hospitals in 2001 and 2003.

MEASURES

Professional nurse reviewers identified ADEs using implicit chart review. Pharmacists assigned Medical Dictionary for Regulatory Activities codes to ADE descriptions for identification of clinical groups of events. Hospitals provided patient demographic, admission, and ICD9-CM data.

RESULTS

Incidence proportions were 0.8% for drug-induced bleeding/anticoagulation problems and 1.0% for drug-induced delirium/psychosis. The model for bleeding had very good discrimination and sensitivity at 0.87 and 86% and fair positive predictive value (PPV) at 12%. The model for delirium had excellent sensitivity at 94%, good discrimination at 0.83, but low PPV at 3%. Poisoning and adverse event codes designed for the targeted ADEs had low sensitivities and, when forced in, degraded model accuracy.

CONCLUSIONS

Hierarchically optimal classification tree analysis is a promising method for rapidly developing clinically meaningful surveillance rules for administrative data. The resultant model for drug-induced bleeding and anticoagulation problems may be useful for retrospective ADE screening and rate estimation.