Evidence-Based Infectious Diseases E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Evidence-Based Medicine
• Sprache: Englisch

Seitenzahl: 1020

Veröffentlichungsjahr: 2011

Beschreibung

The fully updated Second Edition of this case-led text provides an excellent introduction to evidence-based Clinical Practice for the major areas of infectious disease management. The new edition takes on a brand new design and focus, making the book more accessible to junior doctors in infectious diseases and microbiology and general internists. Edited and written by the world's leading infectious disease specialists, Evidence-Based Infectious Diseases contains thoroughly revised clinical chapters, reporting on all new major trials and is ideal for; trainees and clinical instructors in infectious diseases and microbiology, internal medicine physicians and public health physicians.

Leseprobe

Contents

Contributors

Preface to the First Edition

Preface to the Second Edition

CHAPTER 1 Introduction to evidence-based infectious diseases

What is evidence-based medicine?

Evidence-based infectious diseases

Posing a clinical question and finding an answer

Evidence-based diagnosis

Evidence-based treatment

Evidence-based assessment of prognosis

Summary

References

PART 1 Specific diseases

CHAPTER 2 Skin and soft-tissue infections

Impetigo

Cellulitis and erysipelas

Furuncles and carbuncles

Soft-tissue infections and MRSA infection

Necrotizing fasciitis

Diabetic foot infections

Animal bites

References

CHAPTER 3 Bone and joint infections

Introduction

Infectious arthritis

Prosthetic joint infection

Osteomyelitis in the diabetic foot

Conclusion

References

CHAPTER 4 Infective endocarditis

Diagnosis

Antimicrobial management

Surgical intervention

Prognosis

References

CHAPTER 5 Meningitis and encephalitis

Meningitis

Encephalitis

References

CHAPTER 6 Management of community-acquired pneumonia

Burden of illness/relevance to clinical practice

Clinical history and physical examination

Chest radiograph

Admission decision

Diagnostic tests

Antibiotic treatment

Prevention

References

CHAPTER 7 Tuberculosis

Epidemiology

Risk factors for infection and disease

Diagnosis

Treatment

Prevention of TB

References

CHAPTER 8 Diarrhea

Diagnosis

Treatment

Prognosis

Diagnosis

Treatment

Diagnosis

Treatment

Prognosis

Glossary

References

CHAPTER 9 Urinary tract infections

Diagnosis

Therapy for the ambulatory patient

Prognosis

Pathogenesis

Follow-up

Asymptomatic bacteriuria

Prevention

Urinary tract infections in men

Severe and complex urinary tract infections

References

CHAPTER 10 Sexually transmitted infections

Diagnosis of STI

Prevention of STI

Management of sexually transmitted infections

Strategies for control of STI in the community

References

CHAPTER 11 Human immunodeficiency virus

Primary HIV infection

Tuberculosis

Pneumocystis jiroveci (carinii) pneumonia

Antiretroviral regimen selection and adherence

Opportunistic infection prophylaxis

Genotypic resistance testing

Viral phenotyping

Therapeutic drug monitoring

Management of multiple resistance

Treatment and prophylaxis of opportunistic infections (continued)

Other AIDS-related illness

References

CHAPTER 12 Influenza

Diagnosis

Treatment

Prognosis

Prevention

References

CHAPTER 13 Critical care

Sepsis

Ventilator-associated pneumonia

Catheter-related bloodstream infections

References

PART 2 Special populations

CHAPTER 14 Infection control

Surgical site infections

Methicillin-resistant bacteria

References

CHAPTER 15 Infections in neutropenic hosts

The febrile neutropenic episode

Measures to prevent infection in the neutropenic host

Assessment and management of the febrile neutropenic episode

Spectrum of bacterial infections in neutropenic cancer patients

Selected infectious problems in the neutropenic host

References

CHAPTER 16 Infections in general surgery

Surgical site infections

Mesh infections after incisional hernia repair

Acute diverticulitis

Epidemiology

References

CHAPTER 17 Infections in the thermally injured patient

Bacteriology of burns patients

Diagnosis

Prevention of infection

Empiric antibiotic treatment

Infection control

The role of hydrotherapy in burn wound management

Prognosis

New preventive strategies: vaccines

References

CHAPTER 18 Infections in healthcare workers

Occupational bloodborne pathogen exposures

Varicella zoster virus infections

Influenza-like illness

Prevention of other infections in healthcare workers

References

CHAPTER 19 Infections in long-term care

Long-term care facilities

Infections in long-term care facilities

Diagnosis

Pneumonia

Urinary infection

Skin infections

Treatment

Prevention

References

Index

This edition first published 2009, © 2009 by Blackwell Publishing LtdPrevious editions: 2004

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Library of Congress Cataloging-in-Publication Data

Evidence-Based infectious diseases / edited by Mark Loeb, Fiona Smaill, Marek Smieja. — 2nd ed.p. ; cm.Includes bibliographical references and index.ISBN 978-1-4051-7026-01. Communicable diseases. 2. Evidence-Based medicine. I. Loeb, Mark. II. Smaill, Fiona. III. Smieja, Marek.[DNLM: 1. Communicable Diseases—diagnosis. 2. Communicable Diseases—therapy.3. Evidence-Based Medicine. WC 100 E93 2009]RC112.L637 2009616.9—dc222009009650

ISBN: 978-1-4051-7026-0

A catalogue record for this book is available from the British Library.

Set in 9.5/12pt Minion by Macmillan Publishing Solutions, Chennai, India

Printed and bound in Singapore

1 2009

Contributors

Elias Abrutyn, MD (deceased)

Drexel University College of Medicine, Philadelphia, PA, USA

Brian J. Angus BSc, MD

Reader in Infectious Diseases, University of Oxford, Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK

Douglas Austgarden MD

Division of Infectious Diseases, The Hospital for SickChildren, Toronto, ON, Canada

Alain Bouckenooghe MD

Clinical R & D, Head Asia Pacific, Sanofi Pasteur, Singapore

Eric J. Bow MD, MSc

Professor and Head, Section of Haematology/Oncology, Professor, Department of Medical Microbiology and Infectious Diseases, University of Manitoba; and Head, Department of Medical Oncology and Haematology, Director, Infection Control Services, CancerCare Manitoba, Winnipeg, MB, Canada

Robert E. Burrell MD

Department of Surgery, University of Alberta, Edmonton, Alberta, Canada

Peter Daley MD

Lecturer, Infectious Diseases Training and Research Center, Vellore, India

Guy De Bruyn MBBCh, BCh, MPH

Programme Director, HIV Prevention Studies, PerinatalHIV Research Unit, University of the Witwatersrand, Johannesburg, South Africa

Guilio DiDiodato MD

Division of Infectious Diseases, The Hospital for Sick Children, Toronto, ON, Canada

Thomas Fekete MD

Section Chief, Infectious Diseases, Professor of Medicine, Temple University School of Medicine, Philadelphia, PA, USA

David N. Fisman MD, MPH

The Research Institute of the Hospital for Sick Children, Toronto, ON, Canada; The University of Toronto, Toronto, ON, Canada

William J. Gillespie BSc, MB, ChB, ChM

Emeritus Professor of Medicine, Hull York Medical School, University of Hull, Hull, UK

Ravindra K. Gupta MD

Centre for Tropical Medicine, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Scott D. Halpern, MD, PhD

Assistant Professor of Medicine and Epidemiology, University of Pennsylvania, Philadelphia, PA, USA

Anthony D. Harris MD, MPH

Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

Joanne M. Langley MD, MSc

Professor of Pediatrics; Professor, Community Health and Epidemiology, Canadian Centre for Vaccinology, Halifax, Dalhousie University, Halifax, NS, Canada

Ebbing Lautenbach MD, MPH, MSCE

Associate Professor of Medicine and Epidemiology, Department of Biostatics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Center for Education and Research of Therapeutics, University of Pennsylvania, School of Medicine, Philadelphia, PA, USA

Christine H. Lee MD

Department of Pathology and Molecular Medicine, McMasterUniversity, St. Joseph’s Healthcare, Hamilton, ON, Canada

Mark Loeb BSc, MD, MSc

Professor, Department of Pathology and MolecularMedicine, Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

Sarvesh Logsetty MD

Department of Surgery, University of Alberta, Edmonton, Alberta, Canada

Kara B. Mascitti MD

Division of Infectious Diseases, University of Pennsylvania, School of Medicine, Philadelphia, PA, USA

Maureen O. Meade MD, MSc

Critical Care Consultant, Hamilton Health Sciences; Associate Professor of Medicine, McMaster University Hamilton, ON, Canada

Sharmistha Mishra MD

The Research Institute of the Hospital for Sick Children, Toronto, ON, Canada

Lindsay E. Nicolle MD

Professor of Internal Medicine and Medical Microbiology, University of Manitoba, Winnipeg, MB, Canada

Kaede Ota MD

The Research Institute of the Hospital for Sick Children, Toronto, ON, Canada

Eli N. Perencevich MD, MSc

Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Veterans Affairs Maryland Health Care System, Baltimore, MD, USA

Robert Rennie MD

Department of Surgery, University of Alberta, Edmonton, Alberta, Canada

David C. Rhew MD

Zynx Health Incorporated, Los Angeles, CA, USA; Associate Clinical Professor, UCLA David Geffen School of Medicine; Division of Infectious Diseases, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA

Ashley Roberts MD

Pediatric Infectious Diseases, Hospital for Sick Children, Toronto, ON, Canada

Gregory Rose MD

Infectious Prevention and Control Program, Division ofInfectious Diseases, The Ottawa Hospital, Ottawa, ON, Canada

Stuart J. Rosser MD, MPH

Assistant Professor, Department of Medicine, University ofAlberta, Edmonton, AB, Canada

Virginia R. Roth MD

Director, Infectious Prevention and Control Program, The Ottawa Hospital; Associate Professor of Medicine, Division of Infectious Diseases, University of Ottawa, Ottawa, ON, Canada

Fiona Smaill MB, ChB

Department Chair & Professor Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

Marek Smieja MD

Associate Professor, Department of Pathology and Molecular Medicine, Department of Clinical Epidemiology and Biosatistics, Department of Medicine, McMaster University, Hamilton, Ontano, Canada

Graham M. Snyder MD

Fellow, Division of Infectious Diseases, Beth IsraelDeaconess Medical Center, Boston, MA, USA

Jocelyn A. Srigley MD

Infectious Diseases and Medical Microbiology Fellow, McMaster University, Hamilton, ON, Canada

Brian L. Strom MD, MPH

George S. Pepper Professor of Public Health and PeventiveMedicine in Biostatistics and Epidemiology, University of Pennsylvania, School of Medicine; Vice Dean for Institutional Affairs, Philadelphia, PA, USA

Darrell H.S. Tan MD

The Research Institute of the Hospital for Sick Children, Toronto, ON, Canada

Edward E. Tredget MD, MSc

Department of Surgery, University of Alberta, Edmonton, Alberta, Canada

Preface to the First Edition

As busy academic physicians we are often approached about assuming new roles and responsibilities, and frankly are sometimes hesitant about placing yet another item on the “to do” list. However, when we were first approached about editing this book, our reaction was different. The idea of editing the first book about evidence-based infectious diseases was exciting. Although there are many standard textbooks on infectious diseases, none that we were aware of use an “evidence-based” approach.

We emphasize in this book both the methodological issues in assessing the quality of evidence, as well as the “best evidence” for practicing infectious diseases. We have divided the book into two parts. In Part I, we focus on specific infections, including skin and soft tissue infections, bone and joint infections, infective endocarditis, meningitis and encephalitis, community-acquired pneumonia, tuberculosis, diarrhea, urinary tract infections, sexually transmitted infections, and human immunodeficiency virus (HIV). In Part II, we focus on infections that occur in specific populations and settings. These include infection control, infections in the neutropenic host, surgical infections, the thermally injured patient, and infection in healthcare workers. We have asked chapter authors to begin with a clinical scenario, to help focus on relevant clinical questions, and then to briefly summarize the burden of illness or background epidemiology. The remainder of each chapter summarizes the best evidence with respect to diagnosis, prognosis, treatment, and prevention, with a focus, where possible, on systematic reviews.

As we discuss in the introductory chapter, we believe that important clinical questions that arise should be approached in a systematic fashion. The chapters in this book will never be as up to date as the information that you can derive by searching the most recent literature. This is particularly relevant when we are faced with new emerging infections, such as severe acute respiratory syndrome (SARS). However, browsing through these chapters will give a good context and will provide you with key evidence that you can update by conducting a search to see if there is any useful new information. While evidence from well-designed studies informs the decision-making process, it obviously does not replace it. The outcomes of a clinical trial, for example, may suggest a default antibiotic to use for pneumonia, but does not preclude our individualizing treatment based on patient allergies, the biology of the responsible organism, or the pharmacokinetics and pharmacodynamics of the drugs to be administered in that patient.

We hope that our approach will help to emphasize aspects of diagnosis, prognosis, treatment, or prevention in which there is already excellent evidence, while highlighting areas in which more compelling evidence is needed. In these latter areas in which our confidence is limited, the reader should be particularly careful to look for newer published data when faced with a similar clinical problem.

We are grateful to the chapter authors who made this book possible. We appreciate the guidance (and patience) of Christina Karaviotis and Mary Banks from BMJ Books. We thank our families (Andrea, Julia, and Nathalie Loeb; Cathy Marchetti and Daniel, Nicole, and Benjamin Smieja; Peter Seary) for their patience and support.

We hope you find this book informative and stimulating, and we shall certainly appreciate any feedback.

Mark LoebMarek SmiejaFiona SmaillHamilton, 2004

Preface to the Second Edition

Following the success of our original edition in 2004, we are privileged to have this opportunity to edit an updated version of Evidence-Based Infectious Diseases. We have targeted this book to general internists and to trainees in infectious diseases, as feedback from the first edition indicated that our textbook was particularly helpful to these groups.

We hope that this new edition will bring added value, while continuing to serve as an evidence-based resource for physicians who manage patients with infections. Along with major updates in chapters on HIV, febrile neutropenia, bone and joint infections, sexually transmitted infections, urinary tract infections, and tuberculosis, there are three brand new chapters in this edition: Influenza, Critical care, and Infections in long-term care.

We are grateful to the chapter authors for all of their hard work. We would like to thank Mirjana Misina, Heather Addison, Rob Blundell, Laura Quigley, Beckie Brand, Lauren Brindley, and Mary Banks for their assistance in preparing this updated edition. We thank our families Andrea, Julia, and Nathalie Loeb; Cathy Marchetti and Benjamin, Nicole, and Daniel Smieja; and Peter Seary for their support.

We hope that you will find this edition informative and we welcome any feedback.

Mark LoebMarek SmiejaFiona SmaillHamilton

CHAPTER 1

Introduction to evidence-based infectious diseases

Mark Loeb, Marek Smieja & Fiona Smaill

Our purpose in this chapter is to provide a brief overview of evidence-based infectious diseases practice and to set the context for the chapters which follow. We highlight evidence-based guidelines for assessing diagnosis, treatment, and prognosis, and discuss the application of evidence-based practice to infectious diseases, as well as identifying areas in which such application must be made with caution.

What is evidence-based medicine?

Evidence-based medicine was born in the writings of clinical epidemiologists at McMaster University, Yale, and elsewhere. Two series of guidelines for assessing the clinical literature articulated these, then revolutionary, ideas and found a wide audience of students, academics, and practitioners alike [1, 2]. These guidelines emphasized the randomized clinical trial (RCT) for assessing treatment, now a standard requirement for the licensing of new drugs or other therapies. David Sackett, the founding chair of the Department of Clinical Epidemiology and Biostatistics at McMaster University, defined evidence-based medicine as “the conscientious, explicit and judicious use of current best evidence in making decisions about the care of patients” [3].

These guidelines, which we summarize later in the chapter, were developed primarily to help medical students and practicing doctors find answers to clinical problems. The reader was guided in assessing the published literature in response to a given clinical scenario, to find relevant clinical articles, to assess the validity and understand the results of the identified papers, and to improve their clinical practice. Aided by computers, massive databases, and powerful search engines, these guidelines and the evidence-based movement empowered a new generation of practitioners and have had a profound impact on how studies are conducted, reported, and summarized. The massive proliferation of randomized clinical trials, the increasing numbers of systematic reviews and evidence-based guidelines, and the emphasis on appropriate methods of assessing diagnosis and prognosis, have affected how we practice medicine.

Evidence-based infectious diseases

The field of infectious diseases, or more accurately the importance of illness due to infections, played a major role in the development of epidemiological research in the 19th and early 20th centuries. Classical observational epidemiology was derived from studies of epidemics – infectious diseases such as cholera, smallpox, and tuberculosis. Classical epidemiology was nevertheless action-oriented. For example, John Snow’s observations regarding cholera led to his removal of the Broad Street pump handle in an attempt to reduce the incidence of cholera. Pasteur, on developing an animal vaccine for anthrax, vaccinated a number of animals with members of the media in attendance [4]. When unvaccinated animals subsequently died, while vaccinated animals did not, the results were immediately reported throughout Europe’s newspapers.

In the era of clinical epidemiology, it is notable that the first true randomized controlled trial is widely attributed to Sir Austin Bradford Hill’s 1947 study of streptomycin for tuberculosis [5]. In subsequent years, and long before the “large simple trial” was rediscovered by the cardiology community, large-scale trials were carried out for polio prevention, and tuberculosis prevention and treatment.

Having led the developments in both classical and clinical epidemiology, is current infectious diseases practice evidence-based? We believe the answer is “somewhat”. We have excellent evidence for the efficacy and side effects of many modern vaccines, while the acceptance of before-and-after data to prove the efficacy of antibiotics for treating bacterial meningitis is ethically appropriate. In the field of HIV medicine we have very strong data to support our methods of diagnosis, assessing prognosis and treatment, as well as very persuasive evidence supporting causation. However, in treating many common infectious syndromes – from sinusitis and cellulitis to pneumonia – we have many very basic diagnostic and therapeutic questions that have not been optimally answered. How do we reliably diagnose pneumonia? Which antibiotic is most effective and cost-effective? Can we improve on the impaired quality of life that often follows such infections as pneumonia?

While virtually any patient presenting with a myocardial infarction will benefit from aspirin and thrombolytic therapy, there may not be a single “best” antibiotic for pneumonia. Much of the “evidence” that guides therapy in the infectious diseases, particularly for bacterial diseases, may not be clinical, but exists in the form of a sound biologic rationale, the activity of the antimicrobial against the offending pathogen, and the penetration at the site of infection (pharmacodynamics and pharmacokinetics). Still, despite having a sound biologic basis for choice of therapy, there are many situations where better randomized controlled trials need to be conducted and where clinically important outcomes, such as symptom improvement and health-related quality, are measured.

How, then, can we define “evidence-based infectious diseases” (EBID)? Paraphrasing David Sackett, EBID may be defined as “the explicit, judicious and conscientious use of current best evidence from infection diseases research in making decisions about the prevention and treatment of infection of individuals and populations”. It is an attempt to bridge the gap between research evidence and the clinical practice of infectious diseases. Such an “evidence-based approach” may include critically appraising evidence for the efficacy of a vaccine or a particular antimicrobial treatment regimen. However, it may also involve finding the best evidence to support (or refute) use of a diagnostic test to detect a potential pathogen. Additionally, EBID refers to the use of the best evidence to estimate prognosis of an infection or risk factors for the development of infection. EBID therefore represents the application of research findings to help answer a specific clinical question. In so doing, it is a form of knowledge transfer, from the researcher to the clinician. It is important to remember that use of research evidence is only one component of good clinical decision-making. Experience and clinical skills are essential components. EBID serves to inform the decision-making process. For the field of infectious diseases, a sound knowledge of antimicrobials and microbiologic principles are also needed.

Posing a clinical question and finding an answer

The first step in practicing EBID is posing a clinically driven and clinically relevant question. To answer a question about diagnosis, therapy, prognosis, or causation, one can begin by framing the question [2]. The question usually includes a brief description of the patients, the intervention, the comparison, and the outcome (a useful acronym is “PICO”). For example, if asking about the efficacy of antimicrobial-impregnated catheters in intensive care units [6], the question can be framed as follows: “In critically ill patients, does use of antibiotic-impregnated catheters reduce central line infections?” After framing the question, the second step is to search the literature. There are increasingly a number of options for finding the best evidence. The first step might be to assess evidence-based synopses such as Evidence-Based Medicine or ACP Journal Club (we admit to bias – two of the editors [ML, FS] are associate editors for these journals). These journals regularly report on high-quality studies that can impact practice. The essential components of the studies are abstracted and the papers are reviewed in an accompanying commentary by knowledgeable clinicians. However, since these journals are geared to a general internal medicine audience, many questions faced by clinicians practicing infectious diseases may not be addressed.

The next approach that we would recommend is to search for systematic reviews. Systematic reviews can be considered as concise summaries of the best available evidence that address sharply defined clinical questions [7]. Increasingly, the Cochrane Collaboration is publishing high-quality infectious diseases systematic reviews (http://www.cochranelibrary.com). Another source of systematic reviews is the DataBase of Abstracts of Reviews of Effects (DARE) (http://www.crd.york.ac.uk/crdweb). To help find systematic reviews, MEDLINE can be searched using the systematic review clinical query option in PubMed (http://www.ncbi.nlm.nih.gov/pubmed/). If there are no synopses or systematic reviews that can answer the clinical question, the next step is search the literature itself by accessing MEDLINE through PUBMED. After finding the evidence the next step is to critically appraise it.

Evidence-based diagnosis

Let us consider the use of a rapid antigen detection test for group A streptococcal infection in throat swabs. The first question to ask is whether there was a blinded comparison against an accepted reference standard. By blinded, we mean that the measurements with the new test were done without knowledge of the results of the reference standard.

Next, we would assess the results. Traditionally, we are interested in the sensitivity (proportion of reference-standard positives correctly identified as positive by the new test) and specificity (the proportion of reference-standard negatives correctly identified as negative by the new test).

Ideally, we would also like to have a measure of the precision of this estimate, such as a 95% confidence interval on the sensitivity and specificity, although such measures are rarely reported in the infectious diseases literature.

Note, however, that while the sensitivity and specificity may help a laboratory to choose the best test to offer for routine testing, they do not necessarily help the clinician. Thus, faced with a positive test with known 95% sensitivity and specificity, we cannot infer that our patient with a positive test for group A streptococcal infection has a 95% likelihood of being infected. For this, we need a positive predictive value, which is calculated as the percentage of true positives among all those who test positive. If the positive predictive value is 90%, then a positive test would suggest a 90% likelihood that the person is truly infected. Similarly, the negative predictive value is the percentage of true negatives among all those who test negative. Both positive and negative predictive value change with the underlying prevalence of the disease, hence such numbers cannot be generalized to other settings.

A more sophisticated way to summarize diagnostic accuracy, which combines the advantages of positive and negative predictive values while solving the problem of varying prevalence, is to quantify the results using likelihood ratios. Like sensitivity and specificity, likelihood ratios are a constant characteristic of a diagnostic test, and independent of prevalence. However, to estimate the probability of a disease using likelihood ratios, we additionally need to estimate the probability of the target condition (based on prevalence or clinical signs). Diagnostic tests then help us to shift our suspicion (pretest probability) about a condition depending on the result. Likelihood ratios tell us how much we should increase the probability of a condition for a positive test (positive likelihood ratio) or reduce the probability for a negative test (negative likelihood ratio). More formally, likelihood ratio positive (LR+) and negative (LR−) are defined as:

A positive likelihood ratio is also defined as follows: sensitivity/(1 – specificity). Let us assume, hypothetically, that the sensitivity of the rapid antigen test is 80% and the specificity 90%. The positive likelihood ratio for the antigen test is (0.8/0.1) or 8. This would mean that a patient with a positive antigen test would have 8 times the odds of being positive compared with a patient without group A streptococcal infection. The tricky part in using likelihood ratios is to convert the pretest probability (say 20% based on our expected prevalence among patients with pharyngitis in our clinic) to odds: these represent 1:4 odds. After multiplying by 8, we have odds of 8:4, or a 67% post-test probability of disease. Thus, our patient probably has group A streptococcus, and it would be reasonable to treat with antibiotics.

The negative likelihood ratio, defined as (1 – s ensitivity)/specificity, tells us how much we should reduce the probability for disease given a negative test. In this case, the negative likelihood ratio is 0.22, which can be interpreted as follows: a patient with pharyngitis and a negative antigen test would have their odds of disease multiplied by 0.22. In this case, a pretest probability of 20% (odds 1:4) would fall to an odds of 0.22 to 4, or about 5%, following a negative test. Nomograms have been published to aid in the calculation of post-test probabilities for various likelihood ratios [8].

Having found that the results of the diagnostic test appear favorable for both diagnosing or ruling out disease, we ask whether the results of a study can be generalized to the type of patients we would be seeing. We might also call this “external validity” of the study. Here we are asking the question: “Am I likely to get the same good results as in this study in my own patients?” This includes such factors as the severity and spectrum of patients studied versus those we will encounter in our own practice, and technical issues in how the test is performed outside the research setting.

To summarize, to assess a study of a new diagnostic test, we identify a study in which the new test is compared with an independent reference standard; we examine its sensitivity, specificity, and positive and negative likelihood ratios; and we determine whether the spectrum of patients and technical details of the test can be generalized to our own setting.

In applying these guidelines in infectious diseases, there are some important caveats.

There may be no appropriate reference standard.The spectrum of illness may dramatically change the test characteristics, as may other co- interventions such as antibiotics.

For example, let us assume that we are interested in estimating the diagnostic accuracy of a new commercially available polymerase chain reaction (PCR) test for the rapid detection of Neisseria meningitidis in spinal fluid. The reference standard of culture may not be completely sensitive. Therefore, use of an expanded reference (“gold”) standard might be used. For example, the reference standard may be growth of N. meningitidis from the spinal fluid, demonstration of an elevated white blood cell count in the spinal fluid along with gram-negative bacilli with typical morphology on Gram stain, or elevated white blood cell count along with isolation of N. meningitidis in the blood.

It is also important to know in what type of patients the test was evaluated, such as the inclusion and exclusion criteria, as well as the spectrum of illness. Given that growth of microorganisms is usually progressive, test characteristics in infectious diseases can change depending when the tests are conducted. For example, PCR conducted in patients who are early in their course of meningitis may not be sensitive as compared to patients that presented with late-stage disease. This addresses the issue of spectrum in test evaluation.

Evidence-based treatment

The term “evidence-based medicine” has become largely synonymous with the dictum that only randomized, double-blinded clinical trials give reliable estimates of the true efficacy of a treatment. For the purposes of guidelines, “levels of evidence” have been proposed, with a hierarchy from large to small RCTs, prospective cohort studies, case–control studies, and case series. In newer iterations of these “levels of evidence”, a metaanalysis of RCTs (without statistical heterogeneity, indicating that the trials appear to be estimating the same treatment effect), are touted as the highest level of evidence for a therapy.

In general, clinical questions about therapy or prevention are best addressed through randomized controlled trials. In observational studies, since the choice of treatment may have been influenced by extraneous factors which influence prognosis (so-called “confounding factors”), statistical methods are used to “adjust” for identified potentially confounding variables. However, not all such factors are known or accurately measured. An RCT, if large enough, deals with such extraneous prognostic variables by equally apportioning them to the two or more study arms by randomization. Thus, both known and unknown confounders are distributed roughly evenly between the study arms.

For example, a randomized controlled trial would be the appropriate design to assess whether dexamethasone administered prior to antibiotics reduces mortality in adults who have bacterial meningitis [9]. We would evaluate the following characteristics of such a study: who was studied; was there true random assignment; were interventions and assessments blinded; what was the outcome; and can we generalize to our own patients?

When evaluating clinical trials it is important to ensure that assignment of treatment was truly randomized. Studies should describe exactly how the patients were randomized (e.g., random numbers table, computer generating). It is also important to assess whether allocation of the intervention was truly concealed. It is especially important here to distinguish allocation concealment from blinding. Allocation of an intervention can always be concealed even though blinding of investigators, participants or outcome assessors may be impossible. Consider an RCT of antibiotics versus surgery for appendicitis (improbable as this is). Blinding participants and investigators after patients have been randomized would be difficult (sham operations are not considered ethical). However, allocation concealment occurs before randomization. It is an attempt to prevent selection bias by making certain that the investigator has no idea to what arm (antibiotics versus surgery) the next patient enrolled will be randomized. In many trials this is done through a centralized randomized process whereby the study investigator is faxed the assignment after the patient has been enrolled. In some trials, the assignment is kept in envelopes. The problem with this is that, if the site investigator (or another clinician) has a preference for one particular intervention over another, the possibility for tampering exists. For example, if a surgeon who is a site investigator is convinced that the patient he has just enrolled would benefit most from surgery, the surgeon might be tempted to hold the envelope up to a strong light, determine the allocation, and then select another if the contents of the envelope do not indicate surgery as the allocation. This would lead to selection bias and distort the result of the clinical trial. This type of tampering has been documented [10].

The degree of blinding in a study should also be considered. It is important to recognize that blinding can occur at six levels: the investigators, the patients, the outcome assessors, adjudication committee, the data monitoring committee, the data analysts, and even the manuscript writers (although in practice few manuscripts are written blinded of the results) [11]. Describing a clinical trial as “double-blinded” is vague if in fact blinding can occur at so many different levels. It is better to describe who was blinded than using generic terms.

Similarity of groups at baseline should also be considered when evaluating randomized controlled trials to assess whether differences in prognostic factors at baseline may have had an impact on the result. A careful consideration of the intervention is also important. One can ask what actually constitutes the intervention – was there a co-intervention that really may have been the “active ingredient”?

Follow-up is another important issue. It is important to assess whether all participants who were actually randomized are accounted for in the results. A rule of thumb is that the potential for the results to be misleading occurs if fewer than 80% of individuals randomized are not accounted for at the end (i.e., loss to follow-up of over 20% of participants). More rigorous randomized controlled trials are analyzed on an intention-to-treat basis. That is, all patients randomized are accounted for and are analyzed with respect to the group to which they were originally allocated. For example, an individual in our hypothetical appendicitis trial who was initially randomized to antibiotics but later received surgery would be considered in the analysis to have received antibiotics.

Having assured ourselves that the study is randomized, the randomization allocation was not prone to manipulation, and the randomized groups have ended up as comparable on major prognostic factors, we next examine the actual results. Consider a randomized controlled trial of two antibiotics A and B for community-acquired pneumonia. If the mortality rate with antibiotic A is 2% and that with B is 4%, the absolute risk reduction is the difference between the two rates (2%), the relative risk of A versus B is 0.5, and the relative risk reduction is 50%, that is the difference between the control and intervention rate (2%) divided by the control rate (4%). In studies with time-to-event data, the hazard ratio is measured rather than the relative risk, and can be thought of as an averaged relative risk over the duration of the study. Absolute risk reduction, relative risk, and hazard ratios are all commonly reported with a 95% confidence interval (CI) as a measure of precision. A 95% CI that does not cross 1.0 (for a relative risk or hazard ratio) or 0 (for the absolute risk reduction) has the same interpretation as a P value of < 0.05: we declare these results as “statistically significant”. Unlike the P value, the 95% CI gives us more information regarding the size of the treatment effect. Note that statistical significance simply tells us whether the results were likely due to chance; the CI also tells us the precision of the estimate (helpful especially for underpowered studies, in which the wide CI warns us that a larger study may be required to more precisely determine the effect). It is important to be aware that statistical significance and clinical importance are not synonymous. A small study may miss an important clinical effect, whereas a very large study may reveal a small but statistically significant difference of no clinical importance. In well-designed studies, researchers prespecify the size of a postulated “minimum clinically important difference” rather than solely relying on statistical significance.

Measures of relative risk, hazard ratios, or absolute risk reduction may be difficult to apply in clinical practice. A more practical way of determining the size of a treatment effect is to translate the absolute risk reduction into its reciprocal, the number needed to treat (NNT). In this example, the number needed to treat is the number of patients who need to be treated to prevent one death. It is the inverse of the absolute risk reduction (1/0.02), which is 50. Therefore, if 50 patients are treated with antibiotic B instead of A, one death would be prevented. A 95% CI can be calculated on the NNT, although we would only recommend such calculations for statistically significant treatment effects. This recommendation is based on the curious mathematical property that, as the absolute risk reduction crosses 0, the NNT becomes infinite, and thereafter crosses over into the bounds of a “number needed to harm”.

It is important to determine if all important outcomes were considered in the randomized controlled trial. For example, a clinical trial of a novel immuno modulating agent for patients with severe West Nile virus disease would need not only to consider neurologic signs and symptoms but also to assess functional status and health-related quality of life. When deciding whether the results of a randomized trial can be applied to your patients, the similarity in the setting and patient population needs to be considered. Finally, you must consider whether the potential benefits of the therapy outweigh the potential risks.

Rather than relying on individual RCTs, it is generally preferable to try to identify systematic reviews on the topic. Systematic reviews, however, also need to be critically evaluated. First, one must ensure that the stated question of the review addresses the clinical question that you are asking. The methods section should describe how all relevant studies were found: that is, including the specific search strategy as well as the inclusion and exclusion criteria. Study validity should be assessed, although there is no universally accepted method for scoring validity in systematic reviews. Both size and precision of treatment effects need to be considered. Similar to evaluating randomized controlled trials, whether all important outcomes were assessed in the review is important. Asking whether the findings are generalizable to your patients and whether the likely benefits are worth the potential harms and benefits is also important.

In summary, to assess a treatment we would find a systematic review or clinical trial; assess whether patients were properly randomized; whether various components of the study were blinded; whether there was a high proportion followed up for all clinically relevant outcomes. We then consider the actual results, and express these ideally as a “number needed to treat” to appreciate the importance (or lack thereof) for individual patients. Finally, we consider whether these results are applicable to the type and severity of disease that we may see in our clinics.

In examining a treatment in infectious diseases, a few caveats to these guidelines are in order.

For many infections there may be a very strong historic and biologic rationale to treat; in such cases an RCT using placebo will be unethical.Many infections may be too rare to study in RCTs, and some infected populations (such as injection drug users) may be difficult to enrol into treatment studies. Observational methods, such as case– control or cohorts to examine therapies or durations associated with cure or relapse, may be the most appropriate methods in these circumstances.While the individually randomized clinical trial is held up as an ideal, it may be more sensible to study many infections through so-called “cluster randomization” in which the unit of randomization may be the hospital, a school, neighborhood, or family. Such studies may detect a treatment effect where herd immunity is important, and may be more feasible to run. However, the confidence intervals for a cluster-randomized study are somewhat wider than if individuals are randomized.Even when individually randomized, the infection itself may represent a “cluster”. Thus, a highly effective therapy for one strain of multidrug resistant (MDR) M. tuberculosis may be useless against another MDR strain. Hence, biologic knowledge of the pathogen and therapy need to be considered when the results of an RCT are generalized to a particular clinical setting.

Evidence-based assessment of prognosis

Many studies about risk factors and outcomes for infectious diseases are published but the quality is variable. The best designs for assessing these are cohort studies in which a representative sample of patients is followed, either prior to developing the infection (to determine risk) or after being infected (to determine outcome). Patients should be assembled at a similar point in their illness (the so-called “inception cohort”), and follow-up should be sufficiently long and complete. Important prognostic factors should be measured, and adjusted for in the analysis. As with clinical trials, the outcome measures are a relative risk, absolute risk, or hazard ratio associated with a particular infection or prognostic factor. For example, to assess the outcome of patients with severe acute respiratory syndrome (SARS), one would optimally want an inception cohort of individuals who meet the case definition within several days of onset of symptoms. These individuals would then be followed prospectively. One of the challenges with SARS was the lack of a “realtime” diagnostic test with high sensitivity and specificity. In general, as diagnostic tests improve, our ability to detect early disease will improve. If SARS re-emerges and therapeutic agents are developed, this will change the natural history, hence the importance of noting whether therapy was administered in the cohort study. If strains of SARS coronavirus mutate as immunity to the virus builds, this may reduce the virulence of the agent. Therefore, it is important to keep in mind that estimates of risk and outcome may change with changes in the infectious agent.

Summary

We hope that the approaches described in this chapter will prove useful for evaluating articles about diagnosis, prognosis, treatment, or prevention in the infectious diseases literature. Using the principles described in this chapter, the chapters that follow attempt to summarize the best evidence for key clinical issues about infectious diseases.

References

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9 de Gans J, van de Beek D. Dexamethasone in adults with bacterial meningitis. N Engl J Med 2002;347:1549–56.

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11 Devereau PJ, Manns BJ, Ghali WA et al. Physician interpretations and textbook definitions of blinding terminology in randomized controlled trials. JAMA 2001;285:2000–3.

PART 1

Specific diseases