Prof. Edwin Chan is the Director of Cochrane Singapore. He’s considered a master of EBM and systematic reviews in Asia, with extensive academic experience. We were honored and lucky to have Hualien Tzu Chi Hospital invite him to hold a one-day workshop for us on December 5, 2018. To see a master like him, I rushed back to Hualien from Dalin, Chiayi, even if it meant taking the night train (it’s a long journey!). Over the next few posts, I’ll share a few key takeaways that left a deep impression on me.
Is PICO omnipotent?
Whenever EBM is mentioned, the first reflex is “PICO”. It’s a great tool we often use for literature searching, but is PICO really omnipotent?
When facing different types of studies, we actually need to use different search strategies:
- Therapeutic: PICO
- Diagnostic: PIGO
- Harmfulness: PEO
- Prognostic: PO
Therapeutic studies use PICO
The PICO we often hear about was actually designed for therapeutic studies, including a specific population (P), intervention (I), comparison (C), and the outcome we want to observe (O).
Let’s take a closer look at the components of PICO, using this question as an example: Can caffeine increase the reaction time of people who are sleepy in the morning?
- Population: Which study population are we targeting? Here, it’s Adults with daytime drowsiness
- Intervention: What intervention is given? Here, it’s Caffeine
- Comparison: What are we comparing it against? We use Placebo / Decaffeinate
- Note that no matter the topic, we shouldn’t just put “no intervention”, but should use a placebo or another therapy.
- So we shouldn’t use “didn’t drink coffee”, but should compare it with “drinking a placebo”, which means a decaffeinated drink. Otherwise, it might be the act of drinking, or the expected effect of coffee, that wakes us up.
- Outcome: What result do we want to compare? Here, it’s Reaction time
This kind of search will yield highly precise results.
(For search methods like MeSH, Title search, Filter, etc., please see the next post:
Diagnostic studies use PIGO
For diagnostic questions, our ultimate goal is to know: what is the sensitivity and specificity of this new tool? Therefore, it’s crucial to know the true positive and true negative rates, which means we need a Gold standard for diagnosis — that’s the G in our PIGO!
Let’s take another example: Is using ultrasound to diagnose pneumothorax in children accurate?
- Population: Children (age under 18)
- Intervention: Sonography
- Gold standard: Chest X ray
- Outcome: Pneumothorax, Sensitivity, Specificity
Harmfulness studies use PEO
Ethics, ethics, ethics.
We can’t do a randomized study where if you draw A you’re told to smoke, and if you draw B you’re told not to smoke, right? This kind of study can mostly only be an observational study, looking at the incidence of various events among people who already smoke (unless Hitler comes back and locks people in gas chambers).
For example, if we want to know: Does drug abuse affect the marriage rate of Taiwanese men?
- Population: Taiwanese male, age 18~30
- Exposure: Drug abuse
- Outcome: Marriage rate
Prognostic studies use PO
Prognostic studies include: the natural progression of a disease with “no treatment” (if there’s treatment, it becomes a Therapeutic study, right?), the impact of prognostic factors (like age, weight, etc.), and clinical prediction rules (like calculating 10-year cardiovascular risk). When we search for these studies, we don’t focus on what treatments they underwent, but rather on describing this group of patients, so there’s no Intervention and Comparison. Additionally, we can include prognostic factors we are curious about (like wearing a mask after getting COVID-19) to see if they affect the prognosis.
So for example: After graduating from medical school, can you successfully get your medical license? (Just kidding xD)
- Population: Graduated medical student
- Outcome: Rate of getting medical license in the first year
To sum up
- Therapeutic: PICO. Note that C isn’t just “not giving”, it should be giving a placebo or another treatment.
- Diagnostic: PICO → PIGO. Set C to gold standard, so we can find the people who are truly diseased, and calculate sensitivity and specificity.
- Harmfulness: PICO → PEO. Remove I/C, replace with Exposure. After all, you can’t randomly assign people into exposure / placebo groups, right?
- Prognostic: PICO → PO. Remove I/C, look at the natural progression of the disease and the impact of prognostic factors. We can also add prognostic factors we are curious about into the search bar.
You actually don’t need to memorize that much. The most important point is: remember the four components of PICO, and for I and C, ask “Is this needed?”, “Should C be Placebo, Gold standard, or left out?” Then you’ll be able to set up a good question for searching~~