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This document serves as a foundational guide to Ev This document serves as a foundational guide to Evidence-Based Medicine (EBM), defined as the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. It emphasizes that EBM is not just about reading research, but integrating individual clinical expertise with the best available external clinical evidence and patient values. The text outlines a systematic 5-step process: starting with a clinical scenario, converting it into a well-built clinical question using the PICO format (Population, Intervention, Comparison, Outcome), and selecting appropriate resources for research. It provides detailed frameworks for Critical Appraisal, distinguishing between the evaluation of diagnostic studies (focusing on sensitivity, specificity, and likelihood ratios) and therapeutic studies (focusing on validity, randomization, and risk calculations like Absolute Risk Reduction and Number Needed to Treat). Finally, it guides the practitioner on how to apply these statistical results back to the individual patient to determine clinical applicability and cost-effectiveness.
2. Topics & Headings (For Slides/Sections)
What is Evidence-Based Medicine?
Definition by Dr. David Sackett.
Integration of Clinical Expertise, Best Evidence, and Patient Values.
The 5 Steps of the EBM Process
Step 1: The Patient (Clinical Scenario).
Step 2: The Question (PICO).
Step 3: The Resource (Searching).
Step 4: The Evaluation (Critical Appraisal).
Step 5: The Patient (Application).
Constructing a Clinical Question (PICO)
Breaking down a vague problem into specific components.
Selecting the appropriate Study Design (RCT, Cohort, etc.).
Searching for Evidence
Boolean Logic (AND, OR).
MeSH Terms and Key Concepts.
Using Databases (PubMed, Cochrane).
Critical Appraisal: Diagnostic Tests
Validity Guides (Reference Standards).
Sensitivity & Specificity.
Likelihood Ratios & Nomograms.
Pre-test vs. Post-test Probability.
Critical Appraisal: Therapeutics
Validity Guides (Randomization, Blinding, Intention-to-Treat).
Results: Relative Risk, Absolute Risk Reduction, NNT.
Applicability to the Patient.
Applying the Evidence
Integrating evidence with patient preference.
Cost-effectiveness analysis.
3. Key Points (Study Notes)
The Definition of EBM: Integrating individual clinical expertise with the best available external clinical evidence from systematic research.
The PICO Framework:
Population: The specific patient group or problem (e.g., elderly women with CHF).
Intervention: The treatment or exposure (e.g., Digoxin).
Comparison: The alternative (e.g., Placebo or standard care).
Outcome: The result of interest (e.g., reduced hospitalization, mortality).
Study Hierarchy:
Therapy: Randomized Controlled Trial (RCT) > Cohort > Case Control.
Diagnosis: Cross-sectional with blind comparison to Gold Standard.
Diagnostic Statistics:
Sensitivity (SnNOUT): The probability that a diseased person tests positive. If Sensitive, when Negative, rule OUT the disease.
Specificity (SpPIN): The probability that a healthy person tests negative. If Specific, when Positive, rule IN the disease.
Likelihood Ratio (LR): How much a test result changes the probability of disease.
LR > 1: Increases probability.
LR < 1: Decreases probability.
Therapy Statistics:
Absolute Risk Reduction (ARR): The difference in risk between Control and Treatment groups (
R
c
​
−R
t
​
).
Relative Risk Reduction (RRR): The proportional reduction (
1−RR
).
Number Needed to Treat (NNT): The number of patients you need to treat to prevent one bad outcome. Calculated as
1/ARR
.
Validity in Therapeutics:
Randomization: Ensures groups are comparable.
Blinding: Prevents bias (Single, Double, Triple).
Intention-to-Treat (ITT): Analyzing patients in their original group regardless of whether they finished the treatment (preserves the benefits of randomization).
4. Easy Explanations (For Presentation Scripts)
On EBM: Think of EBM as a three-legged stool. One leg is your own experience as a doctor, one leg is the scientific research (papers), and the third leg is what the patient actually wants. If you only use one or two legs, the stool falls over. You need all three to stand firm.
On PICO: Imagine you have a vague question: "Is this drug good?" PICO forces you to be specific. Instead, you ask: "Does [Drug X] work better than [Drug Y] for [Patient Z] to cure [Condition A]?" It turns a blurry idea into a sharp target you can actually hit with a search.
On Sensitivity vs. Specificity:
Sensitivity is like a smoke alarm. If there's a fire (disease), the alarm (test) goes off 100% of the time. If it doesn't go off, you know there is no fire (SnNOUT - Sensitive, Negative, Rule Out).
Specificity is like a fingerprint scan. If the scan matches (Positive), you are 100% sure it's that person (SpPIN - Specific, Positive, Rule In).
On Likelihood Ratios: These tell you how much "weight" a test result carries. An LR of 10 means a positive result makes the disease 10 times more likely. An LR of 0.1 means a negative result makes the disease only 10% as likely (ruling it out).
On Intention-to-Treat: This is like a race where runners trip. If you analyze only who finished, you get a skewed result. ITT says: "No matter what happened during the race (tripped, stopped, or finished), you are on the Red Team because that's where we assigned you." This keeps the comparison fair.
On NNT (Number Needed to Treat): This is a reality check. If a drug saves 1 person out of 100, the NNT is 100. That means you have to treat 100 people to save 1 life. Is that worth the side effects and cost? NNT helps you decide.
5. Questions (For Review or Quizzes)
Definition: What are the three components that Dr. Sackett states must be integrated in Evidence-Based Medicine?
PICO: Identify the Population, Intervention, and Outcome in this question: "In children with otitis media, does a 5-day course of antibiotics reduce recurrence compared to a 10-day course?"
Searching: What does the Boolean operator "AND" do in a search strategy?
Diagnostics:
A test has a high sensitivity but low specificity. If the test comes back negative, what does that tell you about the patient?
What does the mnemonic "SpPIN" stand for?
Therapy Validity:
Why is "blinding" important in a clinical trial?
What is the difference between a "Double-Blind" and a "Single-Blind" study?
Therapy Results:
If the risk in the control group is 20% and the risk in the treatment group is 10%, what is the Absolute Risk Reduction (ARR)?
Using the numbers above, calculate the Number Needed to Treat (NNT).
Application: Why must you consider your patient's values and preferences, even if the evidence strongly supports a treatment?... |