Join us this week to discuss the NephJC Summer Book Club, How Medicine Works and When it Doesn’t.
For this book club, NSMC intern Iván Rodríguez wrote this about chapter four:
The Quest for Causality
In this chapter the author, Doctor Perry Wilson, explains the important relationship between cause and effect in medicine. He tell us the story of Ms. Murphy, an octogenarian patient in a community hospital, that was admitted to the ER with shortness of breath, suddenly deteriorating and requiring more oxygen. he searched lots of possible causes (pulmonary embolism, pleural effusion) till he found… the end of the nasal cannula was unplugged from the oxygen supply.
I think in my practice as a young doctor living in the era of evidence-based medicine, feeling there's no cause for a patient´s disease (although I usually must deal with it) makes me feel uneasy. Here's where my brain typically screws up: inferring causality where there´s only correlation.
The author explains how to identify causality and also fallacies around this term. This is stated with clarity and with representative examples:
A child goes to get a vaccination, just a few minutes before he had a seizure. What would our brain assume if seizure presented after his vaccine? I bet we would assume causality of vaccine (A) with the seizure (B). However, it's just a matter of temporality.
This chapter informed me that if you want to prove causality, you need to assess A and B; A causes B, so if you change A you´ll also change B. This interaction between causality and effect seems easy, but applying it to real life could be hard and full of inaccuracies.
To get with causality I can use Bradford Hill criteria: strength of the association, consistency of effect, specificity, temporality, dose response, plausibility, laboratory evidence, experiment, and analogy. But Dr. Wilson advises us to be wary of taking this as complete certainty (is not a blackand white way of thinking).
A lot of spurious correlations are listed by the author (see Tyler Vigen´s website) just for mention some of them:
Margarine is a major driver of divorce in Maine.
Swimming-pool drownings and the number of films Nicolas Cage has appeared in
The number of murders via hot objects and the age of Miss America
The number of engineering doctorates and the volume of mozzarella cheese consumption
You need to account for confounders (factors not taken in mind when you assess for correlation).
And here's where random control trials (RCT) come in. These are considered the gold standard for causality in medicine, but how do they work? If A causes B, then people randomly assigned to A should have more B, they deal with confounders in an elegant way.
RCT, and causality, helps doctors and patients approach answers for both.If I diagnosed you with Covid-19, I know because of the existence of an RCT that ivermectin will not help you, and maybe it will cause harm to you.
After reading this chapter I understand there is uncertainty of causality most of the time. Now, I can explain to my patients in a better way, why I´m not prescribing a harmfulness treatment. Based on specific RCTs, a treatment may cause more harm than benefit. At the same time, I admit that as a kidney doc there are a lot of areas of knowledge to explore. I can be more proactive and, contribute to exploring and navigating in the sea of “cause and effect”.