©2009 Mark Tuttle
Evidence-based Medicine Exam 1 Notes
Ethical Issues of Infectious Disease: Case Studies and Tips from the experts 10/19 Ethics
- Evidenced-based Medicine definition:
o “EBM de-emphasizes intuition, unsystematic clinical experience, and pathophysiologic rationale as sufficient grounds for clinical decision making and stresses the examination of evidence from clinical research” JAMA (1992)
o “The conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. It means integrating individual clinical experience with the best available clinical evidence from systematic research.” David Sackett M.D. (1996)
- BUT, Without clinical expertise, even excellent external evidence may be inapplicable to or inappropriate for and individual patient
- 6-step EBM Process
1. The patient - Start with the patient - a clinical problem or question arises out of care of the patient
2. The question - Construct a well built clinical question derived from the case.
3. The resource - Select the appropriate resources and conduct a search
4. The evaluation - Appraise that evidence for its validity (closeness to the truth) and applicability (usefulness in clinical practice)
5. The patient - Return to the patient. Integrate that evidence with clinical expertise, patient preferences and apply it to practice
6. Self-evaluation - Evaluate your performance with the patient.
- Construct a clinical question
o Patient or problem (demographics)
§ How would you describe a group of patients similar to yours? This may include primary problem, disease, co-morbidities, sex, age, race.
o Intervention, prognostic factor, or exposure
§ What do you want to do for the patient? Meds? Tests? Surgery? What factors may influence the prognosis? Age? Co morbidities? What was the patient exposed to? Tobacco? Asbestos?
o Comparison
§ What is the main alternative to compare with the intervention? Are you trying to decide between two drugs, a drug and no medication, or two diagnostic tests? The clinical question does not always need a specific comparison.
o Outcomes
§ What do you wish to accomplish, measure, improve or affect? What are you trying to do for the patient? Relieve symptoms? Reduce adverse events? Improve function or scores?
o Ex. In elderly patients with congestive heart failure, is digoxin effective in reducing the need for re-hospitalization?
- Types of questions:
o Diagnosis: How to select and interpret diagnostic tests
o Therapy: How to select treatments that do more good than harm and are worth the efforts and costs of doing them.
o Prognosis: How to estimate a patients clinical course over time and anticipate likely complications
o Harm/Etiology: How to identify causes of disease (including iatrogenic forms)
o Cost
- Types of studies:
o Case Series/Case Reports
§ Consists of collections of reports on the treatment of individual patients or a report on a single patient.
§ Because they are reports and there is no use of control groups they have no statistical validity.
o Case Control Studies
§ Patients who already have a certain condition are compared with people who do not. They often rely on patient records or recall for data.
§ Often less reliable than randomized controlled trials and cohort studies because showing a statistical relationship does not mean that one factor necessarily caused the other.
o Cohort Studies
§ Follow patients who have a specific condition or receive a particular treatment over time and compare them with another group that has not been affected by the condition or treatment being studied.
§ Cohort studies are observational and not as reliable as randomized controlled studies, since the two groups may differ in ways other than in the variable under study.
o Randomized Controlled Clinical Trials
§ These are carefully planned projects that study the effect of a therapy on real patients. They include methodologies that reduce the potential for bias (randomization and blinding) and that allow for comparison between intervention groups and control groups (no intervention)
o Prospective, Blind Comparison to a “Gold Standard”
§ Study designed to show the efficacy of a diagnostic test or treatment.
§ This is a controlled trial that looks at patients with varying degrees of an illness and administers standard and investigational diagnostic test or treatment to all of the patients in the study group.
o Systematic Reviews
§ Reviews of the literature that are focused on a clinical topic to answer a specific question.
§ An extensive literature review is conducted to identify all studies with sound methodology.
§ The studies are reviewed, assessed, and the results are summarized according to the predetermined criteria of the review question.
o Meta-analysis
§ A study that thoroughly examines a number of valid studies on a topic and combine the results using accepted statistical methodology as if they were from one large study.
§ Part of the methodology includes critical appraisal of the selected randomized controlled trials selected for analysis.
- The evaluation
o Validity: measuring what you think you are measuring?
o Reliability: can it be repeated?
o Study may be properly conducted (internally valid) but not be generalizable (externally valid)
o External validity: A research study or experiment has external validity if the results obtained would apply to other similar programs or approaches. Can we generalize with confidence that this is true for the target population?
§ Because you are often using a restricted artificial population for your own study, it may be hard to generalize to everyone.
§ Generalizability may also be affected by those who volunteer for a study, as in they may be inherently different from those who do not volunteer (participation bias).
§ Drop-ins may be when someone from the placebo group decides to change to one of the treatment groups without anyone knowing, thus causing problems with the results.
o Validity criteria should be applied before an extensive analysis of the study data (results or conclusions)
§ Methodology, including potential bias, randomization, blinding, accounting for all patients
o Treatment Effect (Digoxin example)
§ Relative Risk (RR) = Y/X= 0.64/0.67 = 0.96
§ RR Reduction (RRR) = 1- X/Y x 100 = 1-0.96 = 4%
§ Absolute Risk Reduction (ARR) = (X – Y) x 100 = (.67-.64) x 100=3%
§ Number Needed to Treat (NNT) to prevent one adverse outcome
= 1/(X - Y) = 1/0.03 = 33 patients.
Orientation to Clinical Decision Making II 11/30 EBM
- This is just the syllabus of the class
Searching PubMed for the Evidence 12/1 EBM
- Background question: General knowledge about a condition
o Ex. What is the pathophysiology of myocardial infarction
- Foreground question: Specific knowledge to inform clinical decisions in your clinical scenario
o Ex. How effective is computed tomographic colonography compared with standard colonoscopy for detection of colorectal neoplasia?
- PICO Format
o Patient/Population: Disease or condition and the characteristics or subset
o Intervention: Drug or treatment in question
o Comparison: Alternative to selected treatment
o Outcome: Improvement?
- MeSH Terms: Medical Subject Headings
o Uniformity and consistency to the indexing of biomedical literature
(ex. Heart attack and myocardial infarction have a consolidated MeSH term)
Study Designs and Measures 12/2 EBM
- Epidemiology: The study of how disease is distributed in populations and the factors that influence or determine this distribution
o The premise of epidemiology is that disease, illness, and ill health are not randomly distributed in a population. We all have certain characteristics that predispose or protect us from a variety of different diseases.
Learning Objectives
1. Identify risk factors of diseases
- Risk factors increase a person’s likelihood of acquiring a disease, condition, or event. For example, not wearing a seat belt increases the risk of injury or death in a car accident. We are trying to identify causes, though this really is not possible. What we really try to determine are RISK FACTORS. The goals being to know how a disease/condition/event develops and how it might be transmitted (more related to infectious diseases).
2. Determine the extent of disease found in a community
- We want to know the burden (person, place, time) of disease as this helps for planning health services, facilities, and training of health care providers. Surveillance of populations (monitoring statistics of disease/events) is useful.
3. Study natural history and prognosis of disease.
- We need to define the natural history of disease so we can create modes of intervention, including treatment and prevention of complications.
4. Evaluate new preventive and therapeutic measures and new modes of health delivery.
- Again, this works in the prevention and intervention areas of epidemiology.
5. Provide foundation for developing public policy and regulatory decisions
- May deal with environmental problems such as radiation, radon in homes, occupations associated with an increased risk of disease in workers. How could we regulate conditions in the workplace? This is where policies are needed.
Goals of Epidemiology
1. Identify subgroups in the population who are at high risk for disease
- If we can identify these groups, we may be able to identify the risk factors that increase their risk of disease and thus modify exposure to those risk factors.
2. Primary, secondary, and tertiary prevention
- Primary prevention is preventing development of a disease in a person who is well. Again, this does not have to be a disease, it can be an event such as preventing injuries in the workplace (i.e., wearing hard hats in construction areas).
3. Reduce morbidity and mortality
4. Develop prevention and intervention programs
- Direct preventive efforts, such as screening for early detection) to populations are useful to those who can benefit from intervention.
5. Improve patient prognosis by discovering methods to enhance quantity and quality of life
Principles of conducting studies
- Causal inference: Causation cannot be observed. Causation is inferred.
- Counterfactual ideal: perfect comparison group for a group of exposed individuals consists of exactly the same individuals had they not been exposed.
o This isn’t practical, so you find a “substitute population” for control
- Types of studies
o Interventional (Trials)
§ The active manipulation of the agent by the investigator.
§ Adding OR removing a factor.
§ May use a surrogate outcome such as lower cholesterol (instead of CAD)
§ Randomized Clinical Trial
· The goal is to randomize everything possible except what you are trying to study, thus removing as much potential as possible for confounding.
· Randomization helps us control confounders that we cannot measure
· Stratification helps ensure that key confounding variables are equally distributed between the treatment and comparison groups. Individuals are first stratified or separated according to the confounding characteristic (i.e., sex, age).
· The “gold standard” with large enough sample size
· Intent-to-Treat Principle: look at who you began the study and didn’t finish because there may be confounding variables
o Non-compliance can be for reasons such as adverse reactions to the treatment, waning interest, and seeking other therapies
· Make the intervention as different as possible from non-intervention
· Equipoise: The ethical principle that it is truly uncertain whether the treatment is better than control
· Statistical significance tests to evaluate baseline imbalances in a clinical trial is improper
· Run-in phase: all potential subjects are given a placebo. Their compliance with the regimen is determined and for those in whom compliance is considered acceptable à entrance into trial
o Observational (Case-Control, Cohort, Cross-sectional, Ecological)
§ Cohort Study
· An existing group divided into exposed/unexposed à measure whether they get the disease
· Ex. Smokers (exposed) and non-smokers (unexposed) getting lung cancer
· Prospective studies are stronger and less biased but more expensive
· Can measure disease incidence (risk)
· Inefficient for rare, latent outcomes. Attrition can be poor
§ Case-control Study
· Locate people with the disease first then see if they have been exposed to a given variable
· Also locate people without the disease (also first) and then see if they were exposed the variable
· The goal of case identification is to identify as many TRUE cases (prevalent and incident cases) of disease quickly and inexpensively.
· BETTER for rare, latent outcomes.
· For example, cancers involving solid tumors may have a latency period (time between point of pre-clinical detection to disease diagnosis) of 15 years or more. It is hard to follow people for that much time and is very expensive.
· Selecting controls: make them as similar as possible to the case group in extraneous factors to make sure there is less chance of confounding
· Limitations
o Limitations in recall may lead to misclassification
o Problems in control selection
o Only estimates RR and IRR, and only under certain conditions
o Inefficient for rare exposures
Risk (Cumulative Incidence)
o # of new cases/# population at risk à for a given period of time
o Ex. 14 new lung cancer cases out of 100 smokers in one year would be the 1-year risk of lung cancer
o # population at risk (denominator) must be free of disease at beginning
o CI1 = # new cases in exposed / # total exposed
o CI2 = # new cases in unexposed / #total unexposed
- Relative risk
o CI1 / CI2
- Incidence Rate (Incidence Density)
o # new casesΣ of person-time spent as at-risk
o Once you get disease, you are no longer included in the denominator
- Odds
o If you have 5 marbles in a jar (1 blue and 4 red) the odds of selecting a blue marble are 1:4.
o NOT a proportion
o Also, Odds = Probability / (1-Probability)
- Probability
o This IS a proportion
o Probability of selecting that blue marble is 25%
Case / Control / TOTALExposed / a / b / a + b
Unexposed / c / d / c + d
TOTAL / a + c / b + d / N
- Exposure odds ratio
o The odds of being exposed among the cases divided by the odds of being exposed among the controls.
o For example if the OR = 1.50 in a study of HRT use and breast cancer, the odds of being exposed to HRT among breast cancer cases are 1.50 times the odds of being exposed to HRT among controls.